Applying Artificial Intelligence to Wearable Sensor Data to Diagnose and Predict Cardiovascular Disease: A Review

Abstract

Cardiovascular disease (CVD) is the world’s leading cause of mortality. There is significant interest in using Artificial Intelligence (AI) to analyse data from novel sensors such as wearables to provide an earlier and more accurate prediction and diagnosis of heart disease. Digital health technologies that fuse AI and sensing devices may help disease prevention and reduce the substantial morbidity and mortality caused by CVD worldwide. In this review, we identify and describe recent developments in the application of digital health for CVD, focusing on AI approaches for CVD detection, diagnosis, and prediction through AI models driven by data collected from wearables. We summarise the literature on the use of wearables and AI in cardiovascular disease diagnosis, followed by a detailed description of the dominant AI approaches applied for modelling and prediction using data acquired from sensors such as wearables. We discuss the AI algorithms and models and clinical applications and find that AI and machine-learning-based approaches are superior to traditional or conventional statistical methods for predicting cardiovascular events. However, further studies evaluating the applicability of such algorithms in the real world are needed. In addition, improvements in wearable device data accuracy and better management of their application are required. Lastly, we discuss the challenges that the introduction of such technologies into routine healthcare may face.

Wearable Devices in Cardiovascular Medicine

“…Deep learning (DL) models-a branch of supervised ML-have become increasingly popular in recent years and have achieved excellent performance in many biomedical applications, particularly in detecting and predicting CVDs such as arrhythmias, myocardial infarction, HF, and coronary artery disease (CAD). 26 As opposed to more traditional ML models (ie, random forest, support vector machine), DL models are more complex in nature. Although DL models have demonstrated superior performance in CVD detection, these models cannot easily learn from small data sets.…”

The use of commercial wrist-worn technology to track physiological outcomes in behavioral interventions

“…They also have the potential to identify disease indicators. For example, analysis of EKG waveforms can aid in the prediction of atrial fibrillation and other cardiovascular disorders [54]. They also have utility in predicting freeze of gait and motor dysfunction in neurodegenerative diseases such as Parkinson's disease [55].…”

Recent developments and future perspectives of microfluidics and smart technologies in wearable devices

“…20,21 The incorporation of these intelligent technologies in wearable devices can ultimately lead to the development of a more personalized approach to healthcare, with the ability to predict and prevent disease, improving overall wellness. 17 These exciting developments make clear that the future of healthcare is strongly linked to the integration of smart technologies and microfluidics in wearable devices. The potential benefits of such integration are numerous.…”

“…20,21 The incorporation of these intelligent technologies in wearable devices can ultimately lead to the development of a more personalized approach to healthcare, with the ability to predict and prevent disease, improving overall wellness. 17…”