Hardware-assisted and Deep-Learning techniques for Low-Power Detection of Cardiovascular Abnormalities in Smart Wearables

The COVID-19 pandemic has significantly reduced visits to hospitals and clinics, forcing physicians and clinics to investigate how to move online using telemedicine and home monitoring. Wearable technologies can help by enabling homecare monitoring if they provide accurate and precise measurements. The monitoring of cardiac health problems is such an example and can be managed when patients are residing at home with the use of wearable cardiac monitoring equipment. Recent studies indicate that of various COVID-19 related complications, cardiac abnormalities in particular are associated with a significantly higher mortality rate. It is therefore important to develop smart wearables that are able to analyze and interpret the recorded signal to detect anomalies outside clinical environments where no external devices are available to analyze and store the signals, nor healthcare personnel is present to assist the identification of abnormal heart activity. This paper looks into two different approaches to enable smart wearables to analyze a high-definition electrocardiogram arriving from ECG sensors arrays in order to detect cardiovascular abnormalities. The first approach relies on techniques that enable the execution of deep-learning models within an embedded processor. The second approach uses heterogeneous multicore embedded processors that accelerate the execution of the classifiers. Results indicate the benefits of each approach and the interplay between the performance achieved in terms of event detection ratio and latency of classification.