Multi-scale markers of circadian rhythm changes for monitoring of mental health

点击翻译按钮获取中文摘要

Almost one quarter of adults currently experience some form of mental health disorder in the UK, costing the healthcare system an estimated £77 billion each year. However, there exists very little objective or real-time monitoring of sufferers of mental health issues. This pilot project will investigate the development of a novel data fusion framework that will be suitable for combining many observations of a patient's behaviour to allow accurate mental health monitoring in any environment. Recent studies have shown that certain types of physical behaviour, daily cycles (circadian rhythms) and social networking activity can be indicative of an individual's state of mental health. However, recording the necessary data to make a diagnosis is difficult, both due to the nature of the health issues and because of the instrumentation needed. Recent developments in commercially available equipment (including smart phones) mean that we now have the opportunity to cheaply and routinely record human behaviour as well as daily patterns of physiology (such as sleep and cardiac activity). By then applying advanced pattern recognition and data fusion techniques, we intend to provide daily feedback of mental well-being to both the patient and care providers. This could facilitate early interventions in deteriorating individuals, thereby lowering costs of health care and reducing the severity of the illness. We also intend to begin to answer the more fundamental question about how circadian rhythms change as mental health deteriorates. The developed of a user-friendly and user-controlled monitoring system, together with a suite of suitable algorithms, will be an important step towards a larger integration of the ever increasing multi-dimensional biometric data we are beginning to collect. This includes signals such as location, body temperature, speech patterns and social interaction behaviours. The potential to fuse data from many different sensors, and many different algorithms, will provide a platform for intelligible interpretation of the vast quantities of data that are beginning to confront researchers in biomedical applications. It will also help to improve the accuracy of monitoring systems and provide the doctor with more objective assessments of patient behaviour, which could lead to more accurate and timely diagnoses.