Through unobtrusive and/or wearable monitoring one can get a very clear picture of many aspects of daily human functioning, above all one can obtain a sense of human behavior, which is reflective of brain functioning and wellness. Through the monitoring of human behaviour, this holds the potential for healthcare providers to provide and implement a variety of therapies in an ultra-timely fashion, a revolutionary system of healthcare which is simply not possible to achieve through traditional means of healthcare delivery.
When monitoring human behaviour in this way, there are three essential elements that need to be met for this to be an effective and reliable system: i) the monitoring needs to be continuous so that ‘developments’ can be detected at any time; ii) the monitoring needs to be unobtrusive, so that the patient's lifestyle and activities are not compromised in any way through being monitored, and; iii) the whole system must rely upon accurate and reliable sensors, data transmissions, alerts, and monitoring so that any adverse change in behaviour is detected immediately and acted upon accordingly. Virtually every disease and illness affects brain functioning and behaviour in some way. In the case of mental health, for example, many prevalent conditions such as Depression, Bipolar Disorder, Psychosis, Dementia, ADHD etc. have already well established brain-behaviour relationships.
Earlier and effective intervention (through detection of behaviour change signatures) holds the potential to avert unnecessary hospital admissions and improve patient care and wellbeing. Effective and reliable continuous unobtrusive monitoring of human behaviour therefore holds the potential to radically transform the way in which healthcare is delivered, to improve hospital bed-occupancy, to reduce the cost of healthcare and to improve patients' lives and the wellbeing of society.
Clearly, there are many questions that need to be addressed before continuous unobtrusive monitoring is developed as a reliable facilitator of novel healthcare delivery and this includes developing a clear understanding and appreciation of behaviour signatures of health and disease across a broad range of scenarios. The development of continuous unobtrusive monitoring of human behaviour as an early warning system for disease development forms the rationale for this talk
Professor James is a biomedical engineer whose current research activity centres on the development of biomedical signal and pattern processing techniques for use as diagnostic or prognostic tools in the treatment of disorders of the human body. Primarily his work has concentrated on the development of processing techniques applied to the analysis of the electromagnetic (EM) activity of the human brain, particularly in a functional neuroimaging context.
Much of his research has been devoted to creating automated analysis techniques for the analysis of EEG data in epilepsy – these include automated spike and seizure detection algorithms, EEG (and MEG) de-noising algorithms and seizure onset prediction algorithms. He is particularly interested in the development of techniques of Blind Source Separation (BSS) and Independent Component Analysis (ICA) for EM brain signal analysis such as denoising, source identification and extraction, and for the automation of such algorithms for clinical use.
He also has a particular interest in the ongoing development of these inherently multi-channel techniques and how then can be used in a single-channel environment. This is a very practical issue with devices that are to be worn in an ambulatory setting. Significant advances have been made in this area allowing these very powerful BSS techniques to be used to extract multiple underlying sources from just single (or very few) recording channels.
Whilst the core work undertaken by Prof James revolves around extracting information from the human brain, particularly for diagnosis and prognosis in epilepsy, as a biomedical engineer his work is truly multi-disciplinary. His research work is generally cross-funded across the disciplines and his 15+ PhD students and research fellows have been/are all co-supervised across schools (and faculties). He now runs the Neural Engineering Lab at the University of Warwick.
Through this interaction Prof. James’s research is being applied in the fields of epilepsy research; Brain-Computer Interfacing; EEG denoising for evoked potentials analysis (BCI and use with Cochlear Implants); EEG/MEG slow-wave analysis for ADHD diagnosis and understand; heart and lung sound detection and identification; pattern processing of electrophysiological signals from C.elegans as well as their behavioural monitoring through image processing; as well as Monitoring the well-being of psychiatric patients, and elderly individuals in their home environment using Pervasive Ambient Monitoring (PAM) and other behavioural monitoring techniques.