Signal processing and machine learning play major roles in the modern biomedicine aiding precise diagnosis of a condition, early detection/prediction of an impending episode and continuous monitoring of at-risk patients. While signal processing helps in extracting relevant diagnostic information from the electrophysiological signals along with removal of noise and unwanted artefacts from those signals of interest; machine learning uses this information (or features) to build a model that could be used for monitoring and diagnosis of a number of disease conditions.
The aim of the group in this field is to develop novel signal processing and machine learning techniques that could be used for early detection/prediction of a multitude of disease conditions; e.g. Cardiovascular and Neurodegenerative diseases and remote tracking of progress of Stroke survivors during their rehabilitation cycle. A particular interest is not only to develop appropriate algorithms but also to implement them in real-time hardware with emphasis on ultra-low energy consumption so that these hardware modules could be used for development of complete body-worn remote monitoring system to make the next-generation remote healthcare system a reality.
We already have world-class research activities in these areas and our aim is to maintain our prominence in this field with broader activities encompassing other disease areas. We have strong collaboration with a number of world-class European Hospitals as well as the Southampton University Hospitals NHS Trust, closely working with the clinicians on healthcare problems that have significant social impacts. Some of the example problems we are currently working on are: prediction of life-threatening arrhythmia and myocardial infarction, development of algorithms for reducing probability of inappropriate electrical shocks for Implementable Cardioverter-defibrillator (ICD), prediction of possible cognitive impairments in new-borns with complicacy at the birth, developing complete automated system for delivering intervention in children diagnosed with autism spectrum disorders and learning disability etc.