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During the process of diagnosis and subsequent treatment, patients routinely undergo imaging, measurement and monitoring procedures using a wide range of techniques. Whether it is the automated monitoring of blood pressure of flow, the electrical signals generated during the contractions of the heart or medical images taken with a state of the art medical scanner, all these techniques produce vast amounts of data, for example in the form of time-series signals representing blood-pressure variation or the large image data-sets from a medical scanner. To help medical practitioners make sense of this flood of information, it is thus becoming increasingly important to provide reliable computational tools that can automatically enhance, analyse and monitor these signals and images, and extract (or facilitate the extractin of) clinically useful information. The same is true in medical and biological research, where similar biomedical monitoring techniques are used to study both healthy biological functions as well as mechanisms of disease and where ever larger studies collect ever larger data-sets of signals and images.

Signal and image processing techniques now allow us to predict unobserved biological processes from non-invasive measurements (for example in the control of blood flow), identify specific impairments (for example in executing movements of the limb), reliably screen large populations for common medical conditions (such as breast cancer) and allow us to automatically compare physiological properties between different populations (such as, for example, the change in the size of certain brain regions in epilepsy patients).

In this module you will study a range of signal and image processing techniques and will learn how they can be used to analyse a range of biomedical signals and images. Whilst learning general and specific analysis techniques, you will also gain insight into relevant biomedical background (such as the basic physiological properties that give rise to many biomedical signals and images) and many of the engineering principles that underlie the operation of key devices that are used to record biomedical signals or generate biomedical images. The module will also discuss engineering issues in the wider context of exploiting engineering for health-care, including relevant ethical and economic issues and multidisciplinary collaboration and communication.


Students should be aware that some knowledge of signal processing or control is strongly recommended. Knowledge of Matlab or Python programming required.

During the process of diagnosis and subsequent treatment, patients routinely undergo imaging, measurement and monitoring procedures using a wide range of techniques. Whether it is the automated monitoring of blood pressure of flow, the electrical signals generated during the contractions of the heart or medical images taken with a state of the art medical scanner, all these techniques produce vast amounts of data, for example in the form of time-series signals representing blood-pressure variation or the large image data-sets from a medical scanner. To help medical practitioners make sense of this flood of information, it is thus becoming increasingly important to provide reliable computational tools that can automatically enhance, analyse and monitor these signals and images, and extract (or facilitate the extractin of) clinically useful information. The same is true in medical and biological research, where similar biomedical monitoring techniques are used to study both healthy biological functions as well as mechanisms of disease and where ever larger studies collect ever larger data-sets of signals and images.

Signal and image processing techniques now allow us to predict unobserved biological processes from non-invasive measurements (for example in the control of blood flow), identify specific impairments (for example in executing movements of the limb), reliably screen large populations for common medical conditions (such as breast cancer) and allow us to automatically compare physiological properties between different populations (such as, for example, the change in the size of certain brain regions in epilepsy patients).

In this module you will study a range of signal and image processing techniques and will learn how they can be used to analyse a range of biomedical signals and images. Whilst learning general and specific analysis techniques, you will also gain insight into relevant biomedical background (such as the basic physiological properties that give rise to many biomedical signals and images) and many of the engineering principles that underlie the operation of key devices that are used to record biomedical signals or generate biomedical images. The module will also discuss engineering issues in the wider context of exploiting engineering for health-care, including relevant ethical and economic issues and multidisciplinary collaboration and communication.


Students should be aware that some knowledge of signal processing or control is strongly recommended. Knowledge of Matlab or Python programming required.

During the process of diagnosis and subsequent treatment, patients routinely undergo imaging, measurement and monitoring procedures using a wide range of techniques. Whether it is the automated monitoring of blood pressure of flow, the electrical signals generated during the contractions of the heart or medical images taken with a state of the art medical scanner, all these techniques produce vast amounts of data, for example in the form of time-series signals representing blood-pressure variation or the large image data-sets from a medical scanner. To help medical practitioners make sense of this flood of information, it is thus becoming increasingly important to provide reliable computational tools that can automatically enhance, analyse and monitor these signals and images, and extract (or facilitate the extractin of) clinically useful information. The same is true in medical and biological research, where similar biomedical monitoring techniques are used to study both healthy biological functions as well as mechanisms of disease and where ever larger studies collect ever larger data-sets of signals and images.

Signal and image processing techniques now allow us to predict unobserved biological processes from non-invasive measurements (for example in the control of blood flow), identify specific impairments (for example in executing movements of the limb), reliably screen large populations for common medical conditions (such as breast cancer) and allow us to automatically compare physiological properties between different populations (such as, for example, the change in the size of certain brain regions in epilepsy patients).

In this module you will study a range of signal and image processing techniques and will learn how they can be used to analyse a range of biomedical signals and images. Whilst learning general and specific analysis techniques, you will also gain insight into relevant biomedical background (such as the basic physiological properties that give rise to many biomedical signals and images) and many of the engineering principles that underlie the operation of key devices that are used to record biomedical signals or generate biomedical images. The module will also discuss engineering issues in the wider context of exploiting engineering for health-care, including relevant ethical and economic issues and multidisciplinary collaboration and communication.


Students should be aware that some knowledge of signal processing or control is strongly recommended. Knowledge of Matlab or Python programming required.

During the process of diagnosis and subsequent treatment, patients routinely undergo imaging, measurement and monitoring procedures using a wide range of techniques. Whether it is the automated monitoring of blood pressure of flow, the electrical signals generated during the contractions of the heart or medical images taken with a state of the art medical scanner, all these techniques produce vast amounts of data, for example in the form of time-series signals representing blood-pressure variation or the large image data-sets from a medical scanner. To help medical practitioners make sense of this flood of information, it is thus becoming increasingly important to provide reliable computational tools that can automatically enhance, analyse and monitor these signals and images, and extract (or facilitate the extractin of) clinically useful information. The same is true in medical and biological research, where similar biomedical monitoring techniques are used to study both healthy biological functions as well as mechanisms of disease and where ever larger studies collect ever larger data-sets of signals and images.

Signal and image processing techniques now allow us to predict unobserved biological processes from non-invasive measurements (for example in the control of blood flow), identify specific impairments (for example in executing movements of the limb), reliably screen large populations for common medical conditions (such as breast cancer) and allow us to automatically compare physiological properties between different populations (such as, for example, the change in the size of certain brain regions in epilepsy patients).

In this module you will study a range of signal and image processing techniques and will learn how they can be used to analyse a range of biomedical signals and images. Whilst learning general and specific analysis techniques, you will also gain insight into relevant biomedical background (such as the basic physiological properties that give rise to many biomedical signals and images) and many of the engineering principles that underlie the operation of key devices that are used to record biomedical signals or generate biomedical images. The module will also discuss engineering issues in the wider context of exploiting engineering for health-care, including relevant ethical and economic issues and multidisciplinary collaboration and communication.


Students should be aware that some knowledge of signal processing or control is strongly recommended. Knowledge of Matlab or Python programming required.

Conventional laboratory experiments are useful mainly to assist understanding or analysis. Because they are of necessity stereotyped, they are of limited usefulness when a circuit or system must be designed to meet a given specification. The majority of engineering tasks fall into this latter category, and therefore require design or synthesis skills, in addition to the understanding of underlying engineering principles.

Students on all Biomedical Engineering pathways will work together on the main design exercises to produce a software and a hardware prototype system as group projects as well as smaller individual projects.

This module includes individual and team design exercises devised to provide a bridge between 'conventional' experiments and the project work in the third and fourth years, (which in turn provide a bridge to 'real' projects in industry). The exercise has real deadlines and concrete deliverables and students are encouraged to be creative, develop imaginative solutions and to make mistakes.

Exercises share common characteristics:
• Customer orientated rather than proscriptive specifications are given
• Design work carried out, bringing academic knowledge to bear on practical problems
• Laboratory sessions are used for guided development/ construction/ verification of designs
• Allow students to demonstrate their communication skills in writing individual and group reports/presentations.

In support of these design exercises, students will be introduced to the importance of human-computer interaction in software design and computer systems, in particular the specialist requirements for a Biomedical application. In addition, they will be introduced to some advanced simulation and design modelling frameworks and tools for biomechanical systems.



They will explore how the study of human interaction affects the design of Biomedical systems, hardware and software, and improve their awareness of the issues that determine the usability of an interactive system.

They will explore the analogue relationship between mechanical and electrical systems, enabling circuit problems and mechanical systems to be treated in the same framework. Combining this with modelling and analysis will develop a better understanding of vibration problems in continuous mechanical systems and allow simulation and visualisation of any mechanical implementation within the design project.

This module teaches the applications of biomedical signal analysis and control systems for biomedicine. The module emphasises developing an understanding through lab-based system design exercises by applying theoretical knowledge taught in the module. The module is split in two parts: 50% control and 50% biomedical signals analysis.
The control topics include electrical/mechanical analogues, p notation, block diagrams, electromechanical systems: torque, inertia, motor model. Using this knowledge, you will follow the Stanford bio-design process to develop an Active Tremor Suppression Brace for Parkinsons
The biomedical signals analysis part will provide a theoretical understanding of the fundamentals of biomedical signal processing, including representation of signals, signal arithmetics, frequency analysis and time-frequency representation of a signal and the fundamentals of Electrocardiogram (ECG) signals. You will design an automated algorithm for ECG analysis in the lab where you will write programmes to separate artefacts and identify individual ECG waves which are fundamental in clinical diagnosis of cardiac diseases.

This module teaches the applications of biomedical signal analysis and control systems for biomedicine. The module emphasises developing an understanding through lab-based system design exercises by applying theoretical knowledge taught in the module. The module is split in two parts: 50% control and 50% biomedical signals analysis.
The control topics include electrical/mechanical analogues, p notation, block diagrams, electromechanical systems: torque, inertia, motor model. Using this knowledge, you will follow the Stanford bio-design process to develop an Active Tremor Suppression Brace for Parkinsons
The biomedical signals analysis part will provide a theoretical understanding of the fundamentals of biomedical signal processing, including representation of signals, signal arithmetics, frequency analysis and time-frequency representation of a signal and the fundamentals of Electrocardiogram (ECG) signals. You will design an automated algorithm for ECG analysis in the lab where you will write programmes to separate artefacts and identify individual ECG waves which are fundamental in clinical diagnosis of cardiac diseases.

This module teaches the applications of biomedical signal analysis and control systems for biomedicine. The module emphasises developing an understanding through lab-based system design exercises by applying theoretical knowledge taught in the module. The module is split in two parts: 50% control and 50% biomedical signals analysis.
The control topics include electrical/mechanical analogues, p notation, block diagrams, electromechanical systems: torque, inertia, motor model. Using this knowledge, you will follow the Stanford bio-design process to develop an Active Tremor Suppression Brace for Parkinsons
The biomedical signals analysis part will provide a theoretical understanding of the fundamentals of biomedical signal processing, including representation of signals, signal arithmetics, frequency analysis and time-frequency representation of a signal and the fundamentals of Electrocardiogram (ECG) signals. You will design an automated algorithm for ECG analysis in the lab where you will write programmes to separate artefacts and identify individual ECG waves which are fundamental in clinical diagnosis of cardiac diseases.

This course is designed to develop fundamental mathematical skills which Biomedical engineers need in order to tackle a wide variety of engineering and design problems. There is a particular focus on developing an understanding of mathematics as a toolbox through practical examples based on case studies from academia and industry

This course is designed to develop fundamental mathematical skills which Biomedical engineers need in order to tackle a wide variety of engineering and design problems. There is a particular focus on developing an understanding of mathematics as a toolbox through practical examples based on case studies from academia and industry