Predicting and monitoring cardiovascular outcomes using wearable devices and novel machine learning techniques

1)简要说明研究背景，包括潜在的影响心血管疾病(CVD)的诊断和监测需要侵入性的临床测试。这需要患者从日常生活中抽出时间，以及临床资源来进行测试，并且只反映了时间的一小部分。可穿戴设备为这一问题提供了解决方案-患者可以在日常生活中自由佩戴，同时定期甚至连续收集相关临床数据。可穿戴设备已被消费者大规模采用，截至2022年，已有超过10亿台联网设备[1]。这种采用表明，人们对监测自己健康的电子设备很满意，许多产品，如Apple Watch，通过心率监测、最大SpO2和其他健康测量，将健康意识作为其产品的一大部分。通过将这些丰富的数据与机器学习/人工智能进步所证明的新的时间序列方法相结合，可穿戴设备可以帮助将心血管监测从诊所转移到家庭，并帮助高危用户寻求医疗护理，以阻止或减缓他们的病情发展。这项技术的潜在影响是巨大的--心血管疾病导致了英国25%的死亡，而临床资源已经捉襟见肘。将诊断的负担从临床转移到算法上，或者简单地为临床医生提供另一种优先照顾患者的衡量标准，可能会对患者的福祉产生巨大影响。2)目的和目的本研究的主要目的是调查先进的基于ML/AI的时间序列分析技术是否可以利用从可穿戴设备收集的数据来预测心脏预后。3)研究方法的新颖性本研究旨在使用可穿戴信号(例如光体积描记)中以前未使用的功能来预测心脏结果，并将新方法应用于已知信号以发现或改进其预测能力，使其在临床环境中更有用和更受信任。4)与EPSRC的战略和研究领域保持一致医疗技术、人工智能5)参与的任何公司或合作者否[1]统计。《2016-2022年全球互联可穿戴设备》。可在以下网址获得：https://www.statista.com/statistics/487291/global-connected-wearable-devices/.访问[2022年2月8日]

1) Brief description of the context of the research including potential impactDiagnosis and monitoring of cardiovascular disease(CVD) requires intrusive, in-clinic testing. This requires patients to take time out of their daily lives, as well as clinical resources to perform tests, and only reflects a small snapshot of time. Wearable devices offer a solution to this problem - patients can wear them freely in their daily lives, whilst having relevant clinical data collected regularly or even continuously.Wearables have been massively adopted by consumers, with over 1 billion connected devices as of 2022 [1]. This adoption shows that people are comfortable with electronics that monitor their health, and many products such as the Apple Watch use health awareness as a large part of their product offering through heart rate monitoring, SpO2 max., and other fitness measurements.By combining this wealth of data with novel time series methods borne out of advancements in machine learning/artificial intelligence, wearables can help move cardiovascular monitoring from the clinic to the home, as well as help at-risk users seek medical attention that could stop or slow the progression of their conditions. The potential impact for this technology is huge - cardiovascular disease is responsible for 25% of deaths in the UK, and clinical resources are already stretched. Moving the burden of diagnosis from the clinic to an algorithm, or simply offering clinicians another metric for prioritising patient care, could have a massive effect on patient wellbeing. 2) Aims and ObjectivesThe key aim of this research is to investigate whether advanced ML/AI based time series analysis techniques can predict cardiac outcomes using data collected from wearable devices. 3) Novelty of Research MethodologyThis research aims to use previously unused features in wearable signals (e.g. photoplethysmography) to predict cardiac outcomes, as well as applying new methods to known signals to uncover or improve their predictive power, making them more useful and trusted in a clinical environment.4) Alignment to EPSRC's strategies and research areasHealthcare technologies, artificial intelligence5) Any companies or collaborators involvedNo[1] Statista. "Global connected wearable devices 2016-2022". Available at: https://www.statista.com/statistics/487291/global-connected-wearable-devices/. Accessed [8 February 2022]