Biomedical Engineering

Educational objectives

Knowledge and understanding:
Students will learn the fundamentals of the general modelling approach such as the between data model and system model, the importance of measurements, the
identification of model parameters in the absence and presence of noise, deconvolution techniques for estimating the model input from the output and its impulse and model
validation techniques, modelling for the artificial pancreas. Moreover, they will learn the basics of different approaches to neural modelling, ranging from circuit models of the
neuronal membrane to black box approaches used to solve the neural encoding/decoding problem, and the basics of neural networks.

Application of knowledge and understanding:
At the end of the course, students will be able to represent a set of measurements and study their statistical properties; identify the parameters of a data model in the absence and in the presence of noise; solve a discrete deconvolution problem in the absence and in the presence of noise; use a standardised model to simulate the glucose/insulin cycle in humans and its alterations (diabetes). They will also become familiar with the basics of tools used to build computational models of neural activity from experimental data.

Critical and judgement skills:
Students will learn how to select the most appropriate modelling approach for a given problem and to evaluate the complexity of the proposed solution.
Communication skills. Students will learn to communicate their modelling choices in a multidisciplinary context and to communicate possible design choices for this purpose.
Learning skills. Students will develop a mindset geared towards independent learning of advanced concepts not covered in the course.

Educational objectives

The goal of this activity, for which 1 CFU is recognized, is to allow the student to acquire specific knowledge, useful for inclusion in the future world of work.

Educational objectives

The Student can choose 12 credits, consistent with the curriculum; the aim, behind the specific aim of the chosen course, is to allow for in-depth study of topics of interest and/or to broaden knowledge of topics not directly addressed in the followed curriculum

Educational objectives

General objectives
The course aims to introduce the principles, methodologies, and applications of the main engineering techniques used to study biomedical data pertaining the domains of statistics and machine learning.
Specific objectives
- Knowledge and understanding
Students will learn concepts of descriptive statistics, hypothesis testing, classification models and advanced biosignal processing
- Applying knowledge and understanding
Students will familiarize with basic tools to apply statistical tests and to train basic classification models
- Critical and judgment skills
Students will learn to choose the most suitable control methodology for a specific problem and to evaluate the complexity of the proposed solution.
- Communication skills
Students will learn to communicate in a multidisciplinary context the main issues of interfacing neurophysiological signals with artificial systems, and to convey possible design choices for this purpose.
- Learning ability:
Students will develop a mindset oriented to independent learning of advanced concepts not covered in the course.

Educational objectives

KNOWLEDGE AND UNDERSTANDING Understanding of methodological tools and fundamental topics of Bioelectromagnetism (interaction of fields with molecular structures,
particularly with aqueous solutions and cells, techniques for calculating the EM field within exposed tissues, physiological reactions of biological systems to electromagnetic
stimulation, rationale and basic concepts of international regulations for health protection), aspects that also form the basis for subsequent specialized courses in the same scientific-disciplinary sector.
APPLICATIVE CAPABILITIES Through appropriate practical experiences, the ability to use multiphysics modeling software, particularly Sim4life-lite. Skills in developing
bioelectromagnetic modeling in an interpretative key, in order to predict the main phenomena related to the use of electromagnetic fields on humans, organs, tissues, and
cellular structures.
JUDGMENT AUTONOMY Potential for critical analysis of the fundamental applicative aspects related to the use of electromagnetic fields in the presence of human subjects.
COMMUNICATION SKILLS Acquisition of adequate knowledge for the dissemination of scientific and technical knowledge in the field of bioelectromagnetism.
ABILITY TO LEARN Gradual achievement and extension of a knowledge level suitable for the formation of a professional figure in the sector of human protection from exposure to EM fields in complex environments.

Educational objectives

The course main objectives are oriented:
- to enhance the student knowledge and know how related to the different interaction mechanisms of ionising radiation with matter
- to the detailed study of the principal technologies and devices used for radiation detection
- to the knowledge of the ionising radiation biological effects on organs and tissues
- to the study of the medicine applied techniques that employ ionising radiation for diagnostic, therapeutic and monitoring purposes.
The aim of the laboratory module is to consolidate the basic elements on radioprotection by experimental exploration of concepts related to radioisotopes activity, to the interaction of the radiation with the matter, to the adsorbed dose in function of exposure time, distance and shielding effectiveness. The knowledge of Physics gained in the previous courses is completed by practical application of statistics in counting techniques and by using control instrumentation (multimeter, oscilloscope)

Educational objectives

The course provides the student with advanced methods for extracting features from biomedical signals, both in the time and frequency domains, with particular regards on digital filters, multivariate analysis, time-frequency and time-scale spectral methods, examples of electroencephalographic, electrocardiographic, photoplethysmographic, and skin conductance signal processing. The course also provides basic tools for biomedical signal classification for brain-computer interface applications, such as LDA, SVM and clustering elements.

Educational objectives

During the module (9CFU) the aims of the course are the physical bases and design fundamentals of biomedical instrumentation such as clinical ultrasound systems, Computed Tomography and Magnetic Resonance Imaging. The course treats also an overview of past and actual technologies of the cited biomedical equipments. The students will acquire the knowledge of the working principles, design techniques and tecnological improvements. They should be able to comprehend and perform the principal test procedures for assessing the performances of biomedical equipments. They should also be able to manage the design criteria of said equipments. In a subsequent module (3 CFU) the aims of the course are designed to deepen the transducer array design criteria applied in ultrasonography and introduce the basic concepts of quality control of the sonographic image known in the scientific literature as Quality Assurance Test.
At the end of the learning path the student must be able to acquire the knowledge and methodologies needed to identify the control protocol of the specific image quality for the different types of probes

Educational objectives

The aim of this course is providing students with basics of instrumentation management and the comprehension of biomechanical models used in motion analysis. The course describes the operation principle of sensors typically used in a movement analysis laboratory, such as force, position, velocity and displacement transducers. Subsequently, it explains the principal techniques for the experimental data analysis of biomechanical variables, which exhaustively represent human movement kinematics and kinetics.

Class attendance is expected and strongly encouraged.

Educational objectives

The course aims to train students of the Master of Science in Biomedical Engineering, in agreement with the International and National Core Competence, who have knowledge and skills essential to the performance of the professional activity aimed at the assessment and management of biomedical technologies in healthcare .
The course objectives are based on the learning of knowledge, skills and methodologies with particular reference to carry out of the testing on the performance of medical devices.
At the end of the learning path the student must be able to acquire professional autonomy, decisional and operational addressed to deal with, identify and manage critical issues during the life cycle of biomedical technologies since they have a high impact on patient and operators safety.

Educational objectives

The course deepens the problems concerning plan, execution, maintenance and management of the Hospital plants and technological systems.
At the end of the course, students know different plants and technological systems and the ways to design, inspect and manage them, focusing on the problem of safety.

Educational objectives

The laurea degree thesis, which consists in the development of a theme and/or project under the supervision of a professor, has the objective of stimulating the student in carrying out personal work including the study of the literature on the chosen topic and the l research activity, both at the Sapienza Departments and at external places. Communication skills are also stimulated.

Educational objectives

Knowledge and understanding:
Students will learn the fundamentals of the general modelling approach such as the between data model and system model, the importance of measurements, the
identification of model parameters in the absence and presence of noise, deconvolution techniques for estimating the model input from the output and its impulse and model
validation techniques, modelling for the artificial pancreas. Moreover, they will learn the basics of different approaches to neural modelling, ranging from circuit models of the
neuronal membrane to black box approaches used to solve the neural encoding/decoding problem, and the basics of neural networks.

Application of knowledge and understanding:
At the end of the course, students will be able to represent a set of measurements and study their statistical properties; identify the parameters of a data model in the absence and in the presence of noise; solve a discrete deconvolution problem in the absence and in the presence of noise; use a standardised model to simulate the glucose/insulin cycle in humans and its alterations (diabetes). They will also become familiar with the basics of tools used to build computational models of neural activity from experimental data.

Critical and judgement skills:
Students will learn how to select the most appropriate modelling approach for a given problem and to evaluate the complexity of the proposed solution.
Communication skills. Students will learn to communicate their modelling choices in a multidisciplinary context and to communicate possible design choices for this purpose.
Learning skills. Students will develop a mindset geared towards independent learning of advanced concepts not covered in the course.

Educational objectives

Knowledge of the pathophysiology of organs in particular, syndromes involving the loss of organ function and possible replacement with devices.

Other objectives are the analysis of reactions to non-biological materials, the possible use of 3D printing to support medicine and the use of artificial intelligence.

Educational objectives

The goal of the course is to teach students how to use numerical methods to solve some engineering problems for which no analytical solution can be found. The course focuses on understanding the theoretical issues behind the numerical methods studied and implementing them using a programming language. This approach is essential for learning how to choose a numerical method correctly, while considering its limitations.

Educational objectives

The goal of this activity, for which 1 CFU is recognized, is to allow the student to acquire specific knowledge, useful for inclusion in the future world of work.

Educational objectives

The Student can choose 12 credits, consistent with the curriculum; the aim, behind the specific aim of the chosen course, is to allow for in-depth study of topics of interest and/or to broaden knowledge of topics not directly addressed in the followed curriculum

Educational objectives

KNOWLEDGE AND UNDERSTANDING Understanding of methodological tools and fundamental topics of Bioelectromagnetism (interaction of fields with molecular structures,
particularly with aqueous solutions and cells, techniques for calculating the EM field within exposed tissues, physiological reactions of biological systems to electromagnetic
stimulation, rationale and basic concepts of international regulations for health protection), aspects that also form the basis for subsequent specialized courses in the same scientific-disciplinary sector.
APPLICATIVE CAPABILITIES Through appropriate practical experiences, the ability to use multiphysics modeling software, particularly Sim4life-lite. Skills in developing
bioelectromagnetic modeling in an interpretative key, in order to predict the main phenomena related to the use of electromagnetic fields on humans, organs, tissues, and
cellular structures.
JUDGMENT AUTONOMY Potential for critical analysis of the fundamental applicative aspects related to the use of electromagnetic fields in the presence of human subjects.
COMMUNICATION SKILLS Acquisition of adequate knowledge for the dissemination of scientific and technical knowledge in the field of bioelectromagnetism.
ABILITY TO LEARN Gradual achievement and extension of a knowledge level suitable for the formation of a professional figure in the sector of human protection from exposure to EM fields in complex environments.

Educational objectives

The course aims to provide a basic formation on the operation principle of the standard and state-of-the-art biomedical instrumentation.
The course also intends to introduce the students to the use of software for biomedical data elaboration and of the characterization methods for the devices used in medical imaging systems

Educational objectives

The course aims to provide a basic formation on the operation principle of the standard and state-of-the-art biomedical instrumentation.
The course also intends to introduce the students to the use of software for biomedical data elaboration and of the characterization methods for the devices used in medical imaging systems.

Educational objectives

The course aims to provide a basic formation on the operation principle of the standard and state-of-the-art biomedical instrumentation.
The course also intends to introduce the students to the use of software for biomedical data elaboration and of the characterization methods for the devices used in medical imaging systems.

Educational objectives

The course provides the student with advanced methods for extracting features from biomedical signals, both in the time and frequency domains, with particular regards on digital filters, multivariate analysis, time-frequency and time-scale spectral methods, examples of electroencephalographic, electrocardiographic, photoplethysmographic, and skin conductance signal processing. The course also provides basic tools for biomedical signal classification for brain-computer interface applications, such as LDA, SVM and clustering elements.

Educational objectives

During the module (9CFU) the aims of the course are the physical bases and design fundamentals of biomedical instrumentation such as clinical ultrasound systems, Computed Tomography and Magnetic Resonance Imaging. The course treats also an overview of past and actual technologies of the cited biomedical equipments. The students will acquire the knowledge of the working principles, design techniques and tecnological improvements. They should be able to comprehend and perform the principal test procedures for assessing the performances of biomedical equipments. They should also be able to manage the design criteria of said equipments. In a subsequent module (3 CFU) the aims of the course are designed to deepen the transducer array design criteria applied in ultrasonography and introduce the basic concepts of quality control of the sonographic image known in the scientific literature as Quality Assurance Test.
At the end of the learning path the student must be able to acquire the knowledge and methodologies needed to identify the control protocol of the specific image quality for the different types of probes

Educational objectives

General objectives
The course aims to introduce the principles, methodologies, and applications of the main engineering techniques used to study biomedical data pertaining the domains of statistics and machine learning.
Specific objectives
- Knowledge and understanding
Students will learn concepts of descriptive statistics, hypothesis testing, classification models and advanced biosignal processing
- Applying knowledge and understanding
Students will familiarize with basic tools to apply statistical tests and to train basic classification models
- Critical and judgment skills
Students will learn to choose the most suitable control methodology for a specific problem and to evaluate the complexity of the proposed solution.
- Communication skills
Students will learn to communicate in a multidisciplinary context the main issues of interfacing neurophysiological signals with artificial systems, and to convey possible design choices for this purpose.
- Learning ability:
Students will develop a mindset oriented to independent learning of advanced concepts not covered in the course.

Educational objectives

The course aims to train students of the Master of Science in Biomedical Engineering, in agreement with the International and National Core Competence, who have knowledge and skills essential to the performance of the professional activity aimed at the assessment and management of biomedical technologies in healthcare .
The course objectives are based on the learning of knowledge, skills and methodologies with particular reference to carry out of the testing on the performance of medical devices.
At the end of the learning path the student must be able to acquire professional autonomy, decisional and operational addressed to deal with, identify and manage critical issues during the life cycle of biomedical technologies since they have a high impact on patient and operators safety.

Educational objectives

The course deepens the problems concerning plan, execution, maintenance and management of the Hospital plants and technological systems.
At the end of the course, students know different plants and technological systems and the ways to design, inspect and manage them, focusing on the problem of safety.

Educational objectives

The laurea degree thesis, which consists in the development of a theme and/or project under the supervision of a professor, has the objective of stimulating the student in carrying out personal work including the study of the literature on the chosen topic and the l research activity, both at the Sapienza Departments and at external places. Communication skills are also stimulated.

Educational objectives

Knowledge and understanding:
Students will learn the fundamentals of the general modelling approach such as the between data model and system model, the importance of measurements, the
identification of model parameters in the absence and presence of noise, deconvolution techniques for estimating the model input from the output and its impulse and model
validation techniques, modelling for the artificial pancreas. Moreover, they will learn the basics of different approaches to neural modelling, ranging from circuit models of the
neuronal membrane to black box approaches used to solve the neural encoding/decoding problem, and the basics of neural networks.

Application of knowledge and understanding:
At the end of the course, students will be able to represent a set of measurements and study their statistical properties; identify the parameters of a data model in the absence and in the presence of noise; solve a discrete deconvolution problem in the absence and in the presence of noise; use a standardised model to simulate the glucose/insulin cycle in humans and its alterations (diabetes). They will also become familiar with the basics of tools used to build computational models of neural activity from experimental data.

Critical and judgement skills:
Students will learn how to select the most appropriate modelling approach for a given problem and to evaluate the complexity of the proposed solution.
Communication skills. Students will learn to communicate their modelling choices in a multidisciplinary context and to communicate possible design choices for this purpose.
Learning skills. Students will develop a mindset geared towards independent learning of advanced concepts not covered in the course.

Educational objectives

Providing critical knowledge of the properties and structure of non-metallic materials used in biomedical applications. Describe the role that such materials have in the design of biomedical and prosthetics devices, including those aspects related to materials transformation and surface treatments. Providing knowledge of the interaction of non-metallic materials with biological systems. Leading the student to develop technical skills needed to understand and work in a highly multidisciplinary and continuously evolving field.

Educational objectives

Providing critical knowledge of the properties and structure of non-metallic materials used in biomedical applications. Describe the role that such materials have in the design of biomedical and prosthetics devices, including those aspects related to materials transformation and surface treatments. Providing knowledge of the interaction of non-metallic materials with biological systems. Leading the student to develop technical skills needed to understand and work in a highly multidisciplinary and continuously evolving field.

Educational objectives

The goal of this activity, for which 1 CFU is recognized, is to allow the student to acquire specific knowledge, useful for inclusion in the future world of work.

Educational objectives

The Student can choose 12 credits, consistent with the curriculum; the aim, behind the specific aim of the chosen course, is to allow for in-depth study of topics of interest and/or to broaden knowledge of topics not directly addressed in the followed curriculum

Educational objectives

KNOWLEDGE AND UNDERSTANDING Understanding of methodological tools and fundamental topics of Bioelectromagnetism (interaction of fields with molecular structures,
particularly with aqueous solutions and cells, techniques for calculating the EM field within exposed tissues, physiological reactions of biological systems to electromagnetic
stimulation, rationale and basic concepts of international regulations for health protection), aspects that also form the basis for subsequent specialized courses in the same scientific-disciplinary sector.
APPLICATIVE CAPABILITIES Through appropriate practical experiences, the ability to use multiphysics modeling software, particularly Sim4life-lite. Skills in developing
bioelectromagnetic modeling in an interpretative key, in order to predict the main phenomena related to the use of electromagnetic fields on humans, organs, tissues, and
cellular structures.
JUDGMENT AUTONOMY Potential for critical analysis of the fundamental applicative aspects related to the use of electromagnetic fields in the presence of human subjects.
COMMUNICATION SKILLS Acquisition of adequate knowledge for the dissemination of scientific and technical knowledge in the field of bioelectromagnetism.
ABILITY TO LEARN Gradual achievement and extension of a knowledge level suitable for the formation of a professional figure in the sector of human protection from exposure to EM fields in complex environments.

Educational objectives

Providing critical knowledge of the properties and structure of non-metallic materials used in biomedical applications. Describe the role that such materials have in the design of biomedical and prosthetics devices, including those aspects related to materials transformation and surface treatments. Providing knowledge of the interaction of non-metallic materials with biological systems. Leading the student to develop technical skills needed to understand and work in a highly multidisciplinary and continuously evolving field.

Educational objectives

Providing critical knowledge of the properties and structure of metallic materials and surfaces used in biomedical applications. Describe the role that such materials have in the design of biomedical and prosthetics devices, including those aspects related to materials transformation and surface treatments. Providing knowledge of the interaction of metallic materials and surfaces with biological systems. Leading the student to develop technical skills needed to understand and work in a highly multidisciplinary and continuously evolving field.

Educational objectives

The teaching “Strength of Biomaterials”, given at the 5th year of the Master Degree in Biomedical Engineering, is in direct succession to that of "Mechanics applied to Solids and Structures” of the Bachelor Degree in Clinical Engineering at the 2nd year of the Bachelor Degree in Clinical Engineering, and constitutes a natural progression. In fact, the knowledge gained in “Mechanics applied to Solids and Structures”, as the theory of beams, mechanics of three-dimensional solids, of the prism of SAINT-VENANT, find immediate application in the study of the mechanical behaviour of long bones, of biological tissues and biomaterials, of the major bone joints of the human body and of prostheses that replace these joints.

Expected results of learning.
We expect that the candidate master engineer acquires the skills to study the the mechanical behaviour of long bones, of biological tissues and biomaterials, of the major bone joints of the human body and of prostheses that replace these joints.

Educational objectives

The course provides the student with advanced methods for extracting features from biomedical signals, both in the time and frequency domains, with particular regards on digital filters, multivariate analysis, time-frequency and time-scale spectral methods, examples of electroencephalographic, electrocardiographic, photoplethysmographic, and skin conductance signal processing. The course also provides basic tools for biomedical signal classification for brain-computer interface applications, such as LDA, SVM and clustering elements.

Educational objectives

During the module (9CFU) the aims of the course are the physical bases and design fundamentals of biomedical instrumentation such as clinical ultrasound systems, Computed Tomography and Magnetic Resonance Imaging. The course treats also an overview of past and actual technologies of the cited biomedical equipments. The students will acquire the knowledge of the working principles, design techniques and tecnological improvements. They should be able to comprehend and perform the principal test procedures for assessing the performances of biomedical equipments. They should also be able to manage the design criteria of said equipments. In a subsequent module (3 CFU) the aims of the course are designed to deepen the transducer array design criteria applied in ultrasonography and introduce the basic concepts of quality control of the sonographic image known in the scientific literature as Quality Assurance Test.
At the end of the learning path the student must be able to acquire the knowledge and methodologies needed to identify the control protocol of the specific image quality for the different types of probes

Educational objectives

Course Objectives
The course of study aims to train students of the Master's Degree course in Biomedical Engineering and to provide the fundamental knowledge, skills and methodologies relating to the design of a prosthetic medical device, with particular reference to endoprostheses. The course will provide the theoretical and computational tools necessary to address the 3D modeling and biomechanical analysis of an endoprosthesis model, in particular osteoarticular and osteosynthesis.
The objectives of the course intend to develop in the students the ability to identify the design specifications of an endoprosthesis, the approach to design, the identification of technological solutions compliant with clinical specifications, verifying that the proposed solution guarantees the functionality of the prosthesis.

Learning Outcomes
At the end of the educational path the student must be able to acquire professional, decision-making and operational autonomy aimed at dealing with, identifying and managing relationships with medical staff by proposing solutions, where possible immediate, or short and medium-term alternatives. The learning ability is stimulated by a training course that alternates methodological principles, application examples and in-depth exercises. In particular, the student will know:
• critically know the fundamental principles for the design and virtual prototyping of elementary prosthetic structures;
• draw up the technical project specifications from the morphological, structural, functional analysis taking into account the interaction with biological tissues for the setting of a virtual pre-operative plan;
• formulate and interpret the results from the biomechanical analysis of the 3D models for the validation of the identified design solution.

Educational objectives

General objectives
The course aims to introduce the principles, methodologies, and applications of the main engineering techniques used to study biomedical data pertaining the domains of statistics and machine learning.
Specific objectives
- Knowledge and understanding
Students will learn concepts of descriptive statistics, hypothesis testing, classification models and advanced biosignal processing
- Applying knowledge and understanding
Students will familiarize with basic tools to apply statistical tests and to train basic classification models
- Critical and judgment skills
Students will learn to choose the most suitable control methodology for a specific problem and to evaluate the complexity of the proposed solution.
- Communication skills
Students will learn to communicate in a multidisciplinary context the main issues of interfacing neurophysiological signals with artificial systems, and to convey possible design choices for this purpose.
- Learning ability:
Students will develop a mindset oriented to independent learning of advanced concepts not covered in the course.

Educational objectives

The “Engineering for Regenerative Medicine" course aims to provide a solid foundation for biomedical engineers to develop comprehensive knowledge in the field of tissue engineering and regenerative medicine, enabling them to effectively interact with professionals from other disciplines such as biology and medicine. Classes will enable students to develop an autonomous critical and analytical capacity for the selection and use of biomaterials, cell cultures, scaffold fabrication techniques, and engineering of functional human tissues for regeneration, or disease modelling for drug testing.

Educational objectives

The laurea degree thesis, which consists in the development of a theme and/or project under the supervision of a professor, has the objective of stimulating the student in carrying out personal work including the study of the literature on the chosen topic and the l research activity, both at the Sapienza Departments and at external places. Communication skills are also stimulated.

Educational objectives

Knowledge and understanding:
Students will learn the fundamentals of the general modelling approach such as the between data model and system model, the importance of measurements, the
identification of model parameters in the absence and presence of noise, deconvolution techniques for estimating the model input from the output and its impulse and model
validation techniques, modelling for the artificial pancreas. Moreover, they will learn the basics of different approaches to neural modelling, ranging from circuit models of the
neuronal membrane to black box approaches used to solve the neural encoding/decoding problem, and the basics of neural networks.

Application of knowledge and understanding:
At the end of the course, students will be able to represent a set of measurements and study their statistical properties; identify the parameters of a data model in the absence and in the presence of noise; solve a discrete deconvolution problem in the absence and in the presence of noise; use a standardised model to simulate the glucose/insulin cycle in humans and its alterations (diabetes). They will also become familiar with the basics of tools used to build computational models of neural activity from experimental data.

Critical and judgement skills:
Students will learn how to select the most appropriate modelling approach for a given problem and to evaluate the complexity of the proposed solution.
Communication skills. Students will learn to communicate their modelling choices in a multidisciplinary context and to communicate possible design choices for this purpose.
Learning skills. Students will develop a mindset geared towards independent learning of advanced concepts not covered in the course.

Educational objectives

Knowledge and understanding: students will be able to understand the basics of the structure and functioning of the nerve cell; to link the activity of individual cells to their function within organised circuits and neuronal systems; to know the nature of the different correlates of brain activity, the techniques for their acquisition and the principles of analysis applied to them; to understand the concept of brain network or circuit, the different definitions of brain connectivity and the main techniques for its estimation and
representation; to know the main engineering techniques used to study neuronal systems and interact with them; to know some examples of application to neuroprosthetics and robot-assisted neurorehabilitation.

Applied knowledge and ability to understand: students will be able to choose the most suitable brain signal acquisition and analysis technique for the specific problem; to choose the brain network estimation method best suited to the nature of the data and to the design and clinical requirements; to choose how to acquire, process and decode brain signals and interface them with external, robotic devices, infrastructures and intelligent environments.

Autonomy of judgement: students will be able to evaluate the implications and possible applications of the different acquisition and analysis methods studied to problems of a clinical, industrial and social nature.

Communication skills: students will learn to communicate in a multidisciplinary context regarding the choices made in relation to the physiological or clinical problem addressed and to communicate and justify the choices made to this end.

Learning skills: students will develop an independent learning attitude towards advanced concepts not covered in the course.

Educational objectives

The goal of the course is to teach students how to use numerical methods to solve some engineering problems for which no analytical solution can be found. The course focuses on understanding the theoretical issues behind the numerical methods studied and implementing them using a programming language. This approach is essential for learning how to choose a numerical method correctly, while considering its limitations.

Educational objectives

The goal of this activity, for which 1 CFU is recognized, is to allow the student to acquire specific knowledge, useful for inclusion in the future world of work.

Educational objectives

The Student can choose 12 credits, consistent with the curriculum; the aim, behind the specific aim of the chosen course, is to allow for in-depth study of topics of interest and/or to broaden knowledge of topics not directly addressed in the followed curriculum

Educational objectives

KNOWLEDGE AND UNDERSTANDING Understanding of methodological tools and fundamental topics of Bioelectromagnetism (interaction of fields with molecular structures,
particularly with aqueous solutions and cells, techniques for calculating the EM field within exposed tissues, physiological reactions of biological systems to electromagnetic
stimulation, rationale and basic concepts of international regulations for health protection), aspects that also form the basis for subsequent specialized courses in the same scientific-disciplinary sector.
APPLICATIVE CAPABILITIES Through appropriate practical experiences, the ability to use multiphysics modeling software, particularly Sim4life-lite. Skills in developing
bioelectromagnetic modeling in an interpretative key, in order to predict the main phenomena related to the use of electromagnetic fields on humans, organs, tissues, and
cellular structures.
JUDGMENT AUTONOMY Potential for critical analysis of the fundamental applicative aspects related to the use of electromagnetic fields in the presence of human subjects.
COMMUNICATION SKILLS Acquisition of adequate knowledge for the dissemination of scientific and technical knowledge in the field of bioelectromagnetism.
ABILITY TO LEARN Gradual achievement and extension of a knowledge level suitable for the formation of a professional figure in the sector of human protection from exposure to EM fields in complex environments.

Educational objectives

The “Engineering for Regenerative Medicine" course aims to provide a solid foundation for biomedical engineers to develop comprehensive knowledge in the field of tissue engineering and regenerative medicine, enabling them to effectively interact with professionals from other disciplines such as biology and medicine. Classes will enable students to develop an autonomous critical and analytical capacity for the selection and use of biomaterials, cell cultures, scaffold fabrication techniques, and engineering of functional human tissues for regeneration, or disease modelling for drug testing.

Educational objectives

The course provides the student with advanced methods for extracting features from biomedical signals, both in the time and frequency domains, with particular regards on digital filters, multivariate analysis, time-frequency and time-scale spectral methods, examples of electroencephalographic, electrocardiographic, photoplethysmographic, and skin conductance signal processing. The course also provides basic tools for biomedical signal classification for brain-computer interface applications, such as LDA, SVM and clustering elements.

Educational objectives

During the module (9CFU) the aims of the course are the physical bases and design fundamentals of biomedical instrumentation such as clinical ultrasound systems, Computed Tomography and Magnetic Resonance Imaging. The course treats also an overview of past and actual technologies of the cited biomedical equipments. The students will acquire the knowledge of the working principles, design techniques and tecnological improvements. They should be able to comprehend and perform the principal test procedures for assessing the performances of biomedical equipments. They should also be able to manage the design criteria of said equipments. In a subsequent module (3 CFU) the aims of the course are designed to deepen the transducer array design criteria applied in ultrasonography and introduce the basic concepts of quality control of the sonographic image known in the scientific literature as Quality Assurance Test.
At the end of the learning path the student must be able to acquire the knowledge and methodologies needed to identify the control protocol of the specific image quality for the different types of probes

Educational objectives

The aim of this course is providing students with basics of instrumentation management and the comprehension of biomechanical models used in motion analysis. The course describes the operation principle of sensors typically used in a movement analysis laboratory, such as force, position, velocity and displacement transducers. Subsequently, it explains the principal techniques for the experimental data analysis of biomechanical variables, which exhaustively represent human movement kinematics and kinetics.

Class attendance is expected and strongly encouraged.

Educational objectives

General objectives
The course aims to introduce the principles, methodologies, and applications of the main engineering techniques used to study biomedical data pertaining the domains of statistics and machine learning.
Specific objectives
- Knowledge and understanding
Students will learn concepts of descriptive statistics, hypothesis testing, classification models and advanced biosignal processing
- Applying knowledge and understanding
Students will familiarize with basic tools to apply statistical tests and to train basic classification models
- Critical and judgment skills
Students will learn to choose the most suitable control methodology for a specific problem and to evaluate the complexity of the proposed solution.
- Communication skills
Students will learn to communicate in a multidisciplinary context the main issues of interfacing neurophysiological signals with artificial systems, and to convey possible design choices for this purpose.
- Learning ability:
Students will develop a mindset oriented to independent learning of advanced concepts not covered in the course.

Educational objectives

The goal of the course is to show how the knowledges in biomedical engineering and in experimental biology can work together to get a comprehensive evaluation of the properties of living tissues, thorough specific laboratory experiences. At the end of the Lab classes the students are expected to be able to design an experimental testing protocol aimed at the measurement of the biomechanical properties of living tissues-

Educational objectives

The laurea degree thesis, which consists in the development of a theme and/or project under the supervision of a professor, has the objective of stimulating the student in carrying out personal work including the study of the literature on the chosen topic and the l research activity, both at the Sapienza Departments and at external places. Communication skills are also stimulated.

Educational objectives

Knowledge and understanding:
Students will learn the fundamentals of the general modelling approach such as the between data model and system model, the importance of measurements, the
identification of model parameters in the absence and presence of noise, deconvolution techniques for estimating the model input from the output and its impulse and model
validation techniques, modelling for the artificial pancreas. Moreover, they will learn the basics of different approaches to neural modelling, ranging from circuit models of the
neuronal membrane to black box approaches used to solve the neural encoding/decoding problem, and the basics of neural networks.

Application of knowledge and understanding:
At the end of the course, students will be able to represent a set of measurements and study their statistical properties; identify the parameters of a data model in the absence and in the presence of noise; solve a discrete deconvolution problem in the absence and in the presence of noise; use a standardised model to simulate the glucose/insulin cycle in humans and its alterations (diabetes). They will also become familiar with the basics of tools used to build computational models of neural activity from experimental data.

Critical and judgement skills:
Students will learn how to select the most appropriate modelling approach for a given problem and to evaluate the complexity of the proposed solution.
Communication skills. Students will learn to communicate their modelling choices in a multidisciplinary context and to communicate possible design choices for this purpose.
Learning skills. Students will develop a mindset geared towards independent learning of advanced concepts not covered in the course.

Educational objectives

The goal of the course is to teach students how to use numerical methods to solve some engineering problems for which no analytical solution can be found. The course focuses on understanding the theoretical issues behind the numerical methods studied and implementing them using a programming language. This approach is essential for learning how to choose a numerical method correctly, while considering its limitations.

Educational objectives

Providing critical knowledge of the properties and structure of non-metallic materials used in biomedical applications. Describe the role that such materials have in the design of biomedical and prosthetics devices, including those aspects related to materials transformation and surface treatments. Providing knowledge of the interaction of non-metallic materials with biological systems. Leading the student to develop technical skills needed to understand and work in a highly multidisciplinary and continuously evolving field.

Educational objectives

Providing critical knowledge of the properties and structure of non-metallic materials used in biomedical applications. Describe the role that such materials have in the design of biomedical and prosthetics devices, including those aspects related to materials transformation and surface treatments. Providing knowledge of the interaction of non-metallic materials with biological systems. Leading the student to develop technical skills needed to understand and work in a highly multidisciplinary and continuously evolving field.

Educational objectives

The goal of this activity, for which 1 CFU is recognized, is to allow the student to acquire specific knowledge, useful for inclusion in the future world of work.

Educational objectives

The Student can choose 12 credits, consistent with the curriculum; the aim, behind the specific aim of the chosen course, is to allow for in-depth study of topics of interest and/or to broaden knowledge of topics not directly addressed in the followed curriculum

Educational objectives

KNOWLEDGE AND UNDERSTANDING Understanding of methodological tools and fundamental topics of Bioelectromagnetism (interaction of fields with molecular structures,
particularly with aqueous solutions and cells, techniques for calculating the EM field within exposed tissues, physiological reactions of biological systems to electromagnetic
stimulation, rationale and basic concepts of international regulations for health protection), aspects that also form the basis for subsequent specialized courses in the same scientific-disciplinary sector.
APPLICATIVE CAPABILITIES Through appropriate practical experiences, the ability to use multiphysics modeling software, particularly Sim4life-lite. Skills in developing
bioelectromagnetic modeling in an interpretative key, in order to predict the main phenomena related to the use of electromagnetic fields on humans, organs, tissues, and
cellular structures.
JUDGMENT AUTONOMY Potential for critical analysis of the fundamental applicative aspects related to the use of electromagnetic fields in the presence of human subjects.
COMMUNICATION SKILLS Acquisition of adequate knowledge for the dissemination of scientific and technical knowledge in the field of bioelectromagnetism.
ABILITY TO LEARN Gradual achievement and extension of a knowledge level suitable for the formation of a professional figure in the sector of human protection from exposure to EM fields in complex environments.

Educational objectives

Providing critical knowledge of the properties and structure of non-metallic materials used in biomedical applications. Describe the role that such materials have in the design of biomedical and prosthetics devices, including those aspects related to materials transformation and surface treatments. Providing knowledge of the interaction of non-metallic materials with biological systems. Leading the student to develop technical skills needed to understand and work in a highly multidisciplinary and continuously evolving field.

Educational objectives

Providing critical knowledge of the properties and structure of metallic materials and surfaces used in biomedical applications. Describe the role that such materials have in the design of biomedical and prosthetics devices, including those aspects related to materials transformation and surface treatments. Providing knowledge of the interaction of metallic materials and surfaces with biological systems. Leading the student to develop technical skills needed to understand and work in a highly multidisciplinary and continuously evolving field.

Educational objectives

The course provides the student with advanced methods for extracting features from biomedical signals, both in the time and frequency domains, with particular regards on digital filters, multivariate analysis, time-frequency and time-scale spectral methods, examples of electroencephalographic, electrocardiographic, photoplethysmographic, and skin conductance signal processing. The course also provides basic tools for biomedical signal classification for brain-computer interface applications, such as LDA, SVM and clustering elements.

Educational objectives

During the module (9CFU) the aims of the course are the physical bases and design fundamentals of biomedical instrumentation such as clinical ultrasound systems, Computed Tomography and Magnetic Resonance Imaging. The course treats also an overview of past and actual technologies of the cited biomedical equipments. The students will acquire the knowledge of the working principles, design techniques and tecnological improvements. They should be able to comprehend and perform the principal test procedures for assessing the performances of biomedical equipments. They should also be able to manage the design criteria of said equipments. In a subsequent module (3 CFU) the aims of the course are designed to deepen the transducer array design criteria applied in ultrasonography and introduce the basic concepts of quality control of the sonographic image known in the scientific literature as Quality Assurance Test.
At the end of the learning path the student must be able to acquire the knowledge and methodologies needed to identify the control protocol of the specific image quality for the different types of probes

Educational objectives

The aim of this course is providing students with basics of instrumentation management and the comprehension of biomechanical models used in motion analysis. The course describes the operation principle of sensors typically used in a movement analysis laboratory, such as force, position, velocity and displacement transducers. Subsequently, it explains the principal techniques for the experimental data analysis of biomechanical variables, which exhaustively represent human movement kinematics and kinetics.

Class attendance is expected and strongly encouraged.

Educational objectives

General objectives
The course aims to introduce the principles, methodologies, and applications of the main engineering techniques used to study biomedical data pertaining the domains of statistics and machine learning.
Specific objectives
- Knowledge and understanding
Students will learn concepts of descriptive statistics, hypothesis testing, classification models and advanced biosignal processing
- Applying knowledge and understanding
Students will familiarize with basic tools to apply statistical tests and to train basic classification models
- Critical and judgment skills
Students will learn to choose the most suitable control methodology for a specific problem and to evaluate the complexity of the proposed solution.
- Communication skills
Students will learn to communicate in a multidisciplinary context the main issues of interfacing neurophysiological signals with artificial systems, and to convey possible design choices for this purpose.
- Learning ability:
Students will develop a mindset oriented to independent learning of advanced concepts not covered in the course.

Educational objectives

The course of study aims to prepare students in Biomedical Engineering in accordance with the needs of Medicine to use 3D anatomical models for the preoperative planning of clinical cases in support of radiological images and print them with 3D technology as a tool training and team discussion of the clinical case.
The course objectives are based on the learning of knowledge, skills and methodologies essential to the performance of technical assistance to medical personnel in processing and 3D reconstruction of the diagnostics image.
At the end of the course, the student must be able to acquire professional, decision-making and operational autonomy aimed at addressing, identifying and managing relationships with the medical staff, proposing solutions, where possible immediate, or alternatives in the short and medium term.

Educational objectives

The laurea degree thesis, which consists in the development of a theme and/or project under the supervision of a professor, has the objective of stimulating the student in carrying out personal work including the study of the literature on the chosen topic and the l research activity, both at the Sapienza Departments and at external places. Communication skills are also stimulated.

Educational objectives

Knowledge and understanding:
Students will learn the fundamentals of the general modelling approach such as the between data model and system model, the importance of measurements, the
identification of model parameters in the absence and presence of noise, deconvolution techniques for estimating the model input from the output and its impulse and model
validation techniques, modelling for the artificial pancreas. Moreover, they will learn the basics of different approaches to neural modelling, ranging from circuit models of the
neuronal membrane to black box approaches used to solve the neural encoding/decoding problem, and the basics of neural networks.

Application of knowledge and understanding:
At the end of the course, students will be able to represent a set of measurements and study their statistical properties; identify the parameters of a data model in the absence and in the presence of noise; solve a discrete deconvolution problem in the absence and in the presence of noise; use a standardised model to simulate the glucose/insulin cycle in humans and its alterations (diabetes). They will also become familiar with the basics of tools used to build computational models of neural activity from experimental data.

Critical and judgement skills:
Students will learn how to select the most appropriate modelling approach for a given problem and to evaluate the complexity of the proposed solution.
Communication skills. Students will learn to communicate their modelling choices in a multidisciplinary context and to communicate possible design choices for this purpose.
Learning skills. Students will develop a mindset geared towards independent learning of advanced concepts not covered in the course.

Educational objectives

The goal of the course is to teach students how to use numerical methods to solve some engineering problems for which no analytical solution can be found. The course focuses on understanding the theoretical issues behind the numerical methods studied and implementing them using a programming language. This approach is essential for learning how to choose a numerical method correctly, while considering its limitations.

Educational objectives

KNOWLEDGE AND UNDERSTANDING. The course aims at providing the basic knowledge needed to perform electrical and electronic measurements, with particular emphasis on measurements relevant to the biomedical field. Specific emphasis is given to measurement for electronic device testing and to electro-physiological measurements. The most recent measurement techniques based on impedance spectroscopy are discussed.
APPLYING KNOWLEDGE AND UNDERSTANDING. The theoretical part of the course is completed and complemented by a series of laboratory experiments which allows the student to put into practice the concepts learnt and to acquire the fundamental skills for developing simple electrical medical instruments and for performing the basic measurements encountered in the field of biomedical engineering. Moreover, the basic concepts concerning the programming and management of virtual instrumentation are applied, developing user interfaces for processing and presentation of physiological signals.
MAKING JUDGEMENTS. Laboratory activities aim at allowing the student to make autonomous judgements; the actual autonomy reached by the student is assessed by means of a specific practical session during the final examination. The student gets acquainted with solving new problems in realistic experimental scenarios.
COMMUNICATION SKILLS. The experimental activities include group work, which enhances the student communication skills and interaction capabilities. The oral examination enhances the student capabilities to communicate his/her knowledge. Moreover, student must redact a written technical report concerning the development of a biomedical instrument.
LEARNING SKILLS. The didactic paradigm of the course urges the student to autonomously acquire new technical knowledge, related to the course syllabus, mainly as a result of the problem solving approach of experimental activities. The teaching material, reach of literature references, develops autonomous study capabilities.

Educational objectives

The Student can choose 12 credits, consistent with the curriculum; the aim, behind the specific aim of the chosen course, is to allow for in-depth study of topics of interest and/or to broaden knowledge of topics not directly addressed in the followed curriculum

Educational objectives

KNOWLEDGE AND UNDERSTANDING Understanding of methodological tools and fundamental topics of Bioelectromagnetism (interaction of fields with molecular structures,
particularly with aqueous solutions and cells, techniques for calculating the EM field within exposed tissues, physiological reactions of biological systems to electromagnetic
stimulation, rationale and basic concepts of international regulations for health protection), aspects that also form the basis for subsequent specialized courses in the same scientific-disciplinary sector.
APPLICATIVE CAPABILITIES Through appropriate practical experiences, the ability to use multiphysics modeling software, particularly Sim4life-lite. Skills in developing
bioelectromagnetic modeling in an interpretative key, in order to predict the main phenomena related to the use of electromagnetic fields on humans, organs, tissues, and
cellular structures.
JUDGMENT AUTONOMY Potential for critical analysis of the fundamental applicative aspects related to the use of electromagnetic fields in the presence of human subjects.
COMMUNICATION SKILLS Acquisition of adequate knowledge for the dissemination of scientific and technical knowledge in the field of bioelectromagnetism.
ABILITY TO LEARN Gradual achievement and extension of a knowledge level suitable for the formation of a professional figure in the sector of human protection from exposure to EM fields in complex environments.

Educational objectives

The course aims to provide a basic formation on the operation principle of the standard and state-of-the-art biomedical instrumentation.
The course also intends to introduce the students to the use of software for biomedical data elaboration and of the characterization methods for the devices used in medical imaging systems

Educational objectives

The course aims to provide a basic formation on the operation principle of the standard and state-of-the-art biomedical instrumentation.
The course also intends to introduce the students to the use of software for biomedical data elaboration and of the characterization methods for the devices used in medical imaging systems.

Educational objectives

The course aims to provide a basic formation on the operation principle of the standard and state-of-the-art biomedical instrumentation.
The course also intends to introduce the students to the use of software for biomedical data elaboration and of the characterization methods for the devices used in medical imaging systems.

Educational objectives

The course presents theoretical and practical topics concerning digital signal processing. About 40% of the course consists of practical experiences carried out on the Colab platform using the Python language. After attending the course the student will be able to analyze and implement FIR filters, IIR filters, smoothing filters, upsampling, downsampling and resampling operations, transform operations. It will have some experience with the ECG and audio signals and with the time series processing.

Educational objectives

The course provides the student with advanced methods for extracting features from biomedical signals, both in the time and frequency domains, with particular regards on digital filters, multivariate analysis, time-frequency and time-scale spectral methods, examples of electroencephalographic, electrocardiographic, photoplethysmographic, and skin conductance signal processing. The course also provides basic tools for biomedical signal classification for brain-computer interface applications, such as LDA, SVM and clustering elements.

Educational objectives

During the module (9CFU) the aims of the course are the physical bases and design fundamentals of biomedical instrumentation such as clinical ultrasound systems, Computed Tomography and Magnetic Resonance Imaging. The course treats also an overview of past and actual technologies of the cited biomedical equipments. The students will acquire the knowledge of the working principles, design techniques and tecnological improvements. They should be able to comprehend and perform the principal test procedures for assessing the performances of biomedical equipments. They should also be able to manage the design criteria of said equipments. In a subsequent module (3 CFU) the aims of the course are designed to deepen the transducer array design criteria applied in ultrasonography and introduce the basic concepts of quality control of the sonographic image known in the scientific literature as Quality Assurance Test.
At the end of the learning path the student must be able to acquire the knowledge and methodologies needed to identify the control protocol of the specific image quality for the different types of probes

Educational objectives

The Student can choose 12 credits, consistent with the curriculum; the aim, behind the specific aim of the chosen course, is to allow for in-depth study of topics of interest and/or to broaden knowledge of topics not directly addressed in the followed curriculum

Educational objectives

The goal of this activity, for which 1 CFU is recognized, is to allow the student to acquire specific knowledge, useful for inclusion in the future world of work.

Educational objectives

General objectives
The course aims to introduce the principles, methodologies, and applications of the main engineering techniques used to study biomedical data pertaining the domains of statistics and machine learning.
Specific objectives
- Knowledge and understanding
Students will learn concepts of descriptive statistics, hypothesis testing, classification models and advanced biosignal processing
- Applying knowledge and understanding
Students will familiarize with basic tools to apply statistical tests and to train basic classification models
- Critical and judgment skills
Students will learn to choose the most suitable control methodology for a specific problem and to evaluate the complexity of the proposed solution.
- Communication skills
Students will learn to communicate in a multidisciplinary context the main issues of interfacing neurophysiological signals with artificial systems, and to convey possible design choices for this purpose.
- Learning ability:
Students will develop a mindset oriented to independent learning of advanced concepts not covered in the course.

Educational objectives

The laurea degree thesis, which consists in the development of a theme and/or project under the supervision of a professor, has the objective of stimulating the student in carrying out personal work including the study of the literature on the chosen topic and the l research activity, both at the Sapienza Departments and at external places. Communication skills are also stimulated.

Educational objectives

Knowledge and understanding:
Students will learn the fundamentals of the general modelling approach such as the between data model and system model, the importance of measurements, the
identification of model parameters in the absence and presence of noise, deconvolution techniques for estimating the model input from the output and its impulse and model
validation techniques, modelling for the artificial pancreas. Moreover, they will learn the basics of different approaches to neural modelling, ranging from circuit models of the
neuronal membrane to black box approaches used to solve the neural encoding/decoding problem, and the basics of neural networks.

Application of knowledge and understanding:
At the end of the course, students will be able to represent a set of measurements and study their statistical properties; identify the parameters of a data model in the absence and in the presence of noise; solve a discrete deconvolution problem in the absence and in the presence of noise; use a standardised model to simulate the glucose/insulin cycle in humans and its alterations (diabetes). They will also become familiar with the basics of tools used to build computational models of neural activity from experimental data.

Critical and judgement skills:
Students will learn how to select the most appropriate modelling approach for a given problem and to evaluate the complexity of the proposed solution.
Communication skills. Students will learn to communicate their modelling choices in a multidisciplinary context and to communicate possible design choices for this purpose.
Learning skills. Students will develop a mindset geared towards independent learning of advanced concepts not covered in the course.

Educational objectives

This course aims to provide students with a practical and hands-on experience with common modeling and analysis tools of "omics" data in molecular biology and medicine. It would be expected that after completing this course a student would be able to model, analyze and interpret using Matlab, large scale genomic data like, for example, transcriptomics data of a patient using the appropriate methodology. Furthermore, students will understand the basic biological theory behind these analysis tecniques and critically analyze the results.

Educational objectives

The goal of the course is to teach students how to use numerical methods to solve some engineering problems for which no analytical solution can be found. The course focuses on understanding the theoretical issues behind the numerical methods studied and implementing them using a programming language. This approach is essential for learning how to choose a numerical method correctly, while considering its limitations.

Educational objectives

The goal of this activity, for which 1 CFU is recognized, is to allow the student to acquire specific knowledge, useful for inclusion in the future world of work.

Educational objectives

The Student can choose 12 credits, consistent with the curriculum; the aim, behind the specific aim of the chosen course, is to allow for in-depth study of topics of interest and/or to broaden knowledge of topics not directly addressed in the followed curriculum

Educational objectives

KNOWLEDGE AND UNDERSTANDING Understanding of methodological tools and fundamental topics of Bioelectromagnetism (interaction of fields with molecular structures,
particularly with aqueous solutions and cells, techniques for calculating the EM field within exposed tissues, physiological reactions of biological systems to electromagnetic
stimulation, rationale and basic concepts of international regulations for health protection), aspects that also form the basis for subsequent specialized courses in the same scientific-disciplinary sector.
APPLICATIVE CAPABILITIES Through appropriate practical experiences, the ability to use multiphysics modeling software, particularly Sim4life-lite. Skills in developing
bioelectromagnetic modeling in an interpretative key, in order to predict the main phenomena related to the use of electromagnetic fields on humans, organs, tissues, and
cellular structures.
JUDGMENT AUTONOMY Potential for critical analysis of the fundamental applicative aspects related to the use of electromagnetic fields in the presence of human subjects.
COMMUNICATION SKILLS Acquisition of adequate knowledge for the dissemination of scientific and technical knowledge in the field of bioelectromagnetism.
ABILITY TO LEARN Gradual achievement and extension of a knowledge level suitable for the formation of a professional figure in the sector of human protection from exposure to EM fields in complex environments.

Educational objectives

The course provides the student with advanced methods for extracting features from biomedical signals, both in the time and frequency domains, with particular regards on digital filters, multivariate analysis, time-frequency and time-scale spectral methods, examples of electroencephalographic, electrocardiographic, photoplethysmographic, and skin conductance signal processing. The course also provides basic tools for biomedical signal classification for brain-computer interface applications, such as LDA, SVM and clustering elements.

Educational objectives

During the module (9CFU) the aims of the course are the physical bases and design fundamentals of biomedical instrumentation such as clinical ultrasound systems, Computed Tomography and Magnetic Resonance Imaging. The course treats also an overview of past and actual technologies of the cited biomedical equipments. The students will acquire the knowledge of the working principles, design techniques and tecnological improvements. They should be able to comprehend and perform the principal test procedures for assessing the performances of biomedical equipments. They should also be able to manage the design criteria of said equipments. In a subsequent module (3 CFU) the aims of the course are designed to deepen the transducer array design criteria applied in ultrasonography and introduce the basic concepts of quality control of the sonographic image known in the scientific literature as Quality Assurance Test.
At the end of the learning path the student must be able to acquire the knowledge and methodologies needed to identify the control protocol of the specific image quality for the different types of probes

Educational objectives

General objectives
The course aims to introduce the principles, methodologies, and applications of the main engineering techniques used to study biomedical data pertaining the domains of statistics and machine learning.
Specific objectives
- Knowledge and understanding
Students will learn concepts of descriptive statistics, hypothesis testing, classification models and advanced biosignal processing
- Applying knowledge and understanding
Students will familiarize with basic tools to apply statistical tests and to train basic classification models
- Critical and judgment skills
Students will learn to choose the most suitable control methodology for a specific problem and to evaluate the complexity of the proposed solution.
- Communication skills
Students will learn to communicate in a multidisciplinary context the main issues of interfacing neurophysiological signals with artificial systems, and to convey possible design choices for this purpose.
- Learning ability:
Students will develop a mindset oriented to independent learning of advanced concepts not covered in the course.

Educational objectives

The laurea degree thesis, which consists in the development of a theme and/or project under the supervision of a professor, has the objective of stimulating the student in carrying out personal work including the study of the literature on the chosen topic and the l research activity, both at the Sapienza Departments and at external places. Communication skills are also stimulated.

Educational objectives

Knowledge and understanding: students will be able to understand the basics of the structure and functioning of the nerve cell; to link the activity of individual cells to their function within organised circuits and neuronal systems; to know the nature of the different correlates of brain activity, the techniques for their acquisition and the principles of analysis applied to them; to understand the concept of brain network or circuit, the different definitions of brain connectivity and the main techniques for its estimation and
representation; to know the main engineering techniques used to study neuronal systems and interact with them; to know some examples of application to neuroprosthetics and robot-assisted neurorehabilitation.

Applied knowledge and ability to understand: students will be able to choose the most suitable brain signal acquisition and analysis technique for the specific problem; to choose the brain network estimation method best suited to the nature of the data and to the design and clinical requirements; to choose how to acquire, process and decode brain signals and interface them with external, robotic devices, infrastructures and intelligent environments.

Autonomy of judgement: students will be able to evaluate the implications and possible applications of the different acquisition and analysis methods studied to problems of a clinical, industrial and social nature.

Communication skills: students will learn to communicate in a multidisciplinary context regarding the choices made in relation to the physiological or clinical problem addressed and to communicate and justify the choices made to this end.

Learning skills: students will develop an independent learning attitude towards advanced concepts not covered in the course.

Educational objectives

Knowledge and understanding:
Students will learn the fundamentals of the general modelling approach such as the between data model and system model, the importance of measurements, the
identification of model parameters in the absence and presence of noise, deconvolution techniques for estimating the model input from the output and its impulse and model
validation techniques, modelling for the artificial pancreas. Moreover, they will learn the basics of different approaches to neural modelling, ranging from circuit models of the
neuronal membrane to black box approaches used to solve the neural encoding/decoding problem, and the basics of neural networks.

Application of knowledge and understanding:
At the end of the course, students will be able to represent a set of measurements and study their statistical properties; identify the parameters of a data model in the absence and in the presence of noise; solve a discrete deconvolution problem in the absence and in the presence of noise; use a standardised model to simulate the glucose/insulin cycle in humans and its alterations (diabetes). They will also become familiar with the basics of tools used to build computational models of neural activity from experimental data.

Critical and judgement skills:
Students will learn how to select the most appropriate modelling approach for a given problem and to evaluate the complexity of the proposed solution.
Communication skills. Students will learn to communicate their modelling choices in a multidisciplinary context and to communicate possible design choices for this purpose.
Learning skills. Students will develop a mindset geared towards independent learning of advanced concepts not covered in the course.

Educational objectives

The goal of this activity, for which 1 CFU is recognized, is to allow the student to acquire specific knowledge, useful for inclusion in the future world of work.

Educational objectives

The Student can choose 12 credits, consistent with the curriculum; the aim, behind the specific aim of the chosen course, is to allow for in-depth study of topics of interest and/or to broaden knowledge of topics not directly addressed in the followed curriculum

Educational objectives

KNOWLEDGE AND UNDERSTANDING Understanding of methodological tools and fundamental topics of Bioelectromagnetism (interaction of fields with molecular structures,
particularly with aqueous solutions and cells, techniques for calculating the EM field within exposed tissues, physiological reactions of biological systems to electromagnetic
stimulation, rationale and basic concepts of international regulations for health protection), aspects that also form the basis for subsequent specialized courses in the same scientific-disciplinary sector.
APPLICATIVE CAPABILITIES Through appropriate practical experiences, the ability to use multiphysics modeling software, particularly Sim4life-lite. Skills in developing
bioelectromagnetic modeling in an interpretative key, in order to predict the main phenomena related to the use of electromagnetic fields on humans, organs, tissues, and
cellular structures.
JUDGMENT AUTONOMY Potential for critical analysis of the fundamental applicative aspects related to the use of electromagnetic fields in the presence of human subjects.
COMMUNICATION SKILLS Acquisition of adequate knowledge for the dissemination of scientific and technical knowledge in the field of bioelectromagnetism.
ABILITY TO LEARN Gradual achievement and extension of a knowledge level suitable for the formation of a professional figure in the sector of human protection from exposure to EM fields in complex environments.

Educational objectives

The course provides the student with advanced methods for extracting features from biomedical signals, both in the time and frequency domains, with particular regards on digital filters, multivariate analysis, time-frequency and time-scale spectral methods, examples of electroencephalographic, electrocardiographic, photoplethysmographic, and skin conductance signal processing. The course also provides basic tools for biomedical signal classification for brain-computer interface applications, such as LDA, SVM and clustering elements.

Educational objectives

During the module (9CFU) the aims of the course are the physical bases and design fundamentals of biomedical instrumentation such as clinical ultrasound systems, Computed Tomography and Magnetic Resonance Imaging. The course treats also an overview of past and actual technologies of the cited biomedical equipments. The students will acquire the knowledge of the working principles, design techniques and tecnological improvements. They should be able to comprehend and perform the principal test procedures for assessing the performances of biomedical equipments. They should also be able to manage the design criteria of said equipments. In a subsequent module (3 CFU) the aims of the course are designed to deepen the transducer array design criteria applied in ultrasonography and introduce the basic concepts of quality control of the sonographic image known in the scientific literature as Quality Assurance Test.
At the end of the learning path the student must be able to acquire the knowledge and methodologies needed to identify the control protocol of the specific image quality for the different types of probes

Educational objectives

General objectives
The course aims to introduce the principles, methodologies, and applications of the main engineering techniques used to study biomedical data pertaining the domains of statistics and machine learning.
Specific objectives
- Knowledge and understanding
Students will learn concepts of descriptive statistics, hypothesis testing, classification models and advanced biosignal processing
- Applying knowledge and understanding
Students will familiarize with basic tools to apply statistical tests and to train basic classification models
- Critical and judgment skills
Students will learn to choose the most suitable control methodology for a specific problem and to evaluate the complexity of the proposed solution.
- Communication skills
Students will learn to communicate in a multidisciplinary context the main issues of interfacing neurophysiological signals with artificial systems, and to convey possible design choices for this purpose.
- Learning ability:
Students will develop a mindset oriented to independent learning of advanced concepts not covered in the course.

Educational objectives

The laurea degree thesis, which consists in the development of a theme and/or project under the supervision of a professor, has the objective of stimulating the student in carrying out personal work including the study of the literature on the chosen topic and the l research activity, both at the Sapienza Departments and at external places. Communication skills are also stimulated.