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

在英国，近四分之一的成年人目前经历了某种形式的心理健康障碍，估计每年给医疗系统造成的成本为770亿英镑。然而，对精神健康问题患者的客观或实时监测很少。这个试点项目将研究开发一种新的数据融合框架，该框架将适合结合对患者行为的许多观察，以便在任何环境下进行准确的精神健康监测。最近的研究表明，某些类型的身体行为、日常周期(昼夜节律)和社交网络活动可以指示一个人的心理健康状态。然而，由于健康问题的性质和所需仪器的原因，记录做出诊断所需的数据是困难的。商业设备(包括智能手机)的最新发展意味着，我们现在有机会以廉价和常规的方式记录人类行为以及日常生理模式(如睡眠和心脏活动)。然后，通过应用先进的模式识别和数据融合技术，我们打算向患者和护理提供者提供日常心理健康反馈。这有助于对病情恶化的个人进行早期干预，从而降低保健费用和减轻疾病的严重程度。我们还打算开始回答一个更基本的问题，即随着精神健康的恶化，昼夜节律如何变化。开发一个用户友好和用户可控的监测系统，以及一套合适的算法，将是朝着我们开始收集的日益增长的多维生物特征数据的更大集成迈出的重要一步。这包括位置、体温、言语模式和社交行为等信号。融合来自许多不同传感器和许多不同算法的数据的潜力，将为生物医学应用中的研究人员开始面临的海量数据提供一个可理解的解释平台。它还将有助于提高监测系统的准确性，并为医生提供对患者行为的更客观评估，从而可能导致更准确和及时的诊断。

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.