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Single-lead wearable electrocardiographic (ECG) devices for remote monitoring are emerging as critical components of the viability of long-term continuous health and wellness monitoring applications. These sensors make it simple to monitor chronically ill patients and the elderly in long-term care homes, as well as empower users focused on fitness and wellbeing with timely health and lifestyle information and metrics. This article addresses the future developments in single-lead electrocardiogram (ECG) wearables, their design concepts, signal processing, machine learning (ML), and emerging healthcare applications. A literature review of multiple wearable ECG remote monitoring devices is first performed; Apple Watch, Kardia, Zio, BioHarness, Bittium Faros and Carnation Ambulatory Monitor. Zio showed the longest wear time with patients wearing the patch for 14 days maximum but required users to mail the device to a processing center for analysis. While the Apple Watch and Kardia showed good quality acquisition of raw ECG but are not continuous monitoring devices. The design considerations for single-lead ECG wearable devices could be classified as follows: power needs, computational complexity, signal quality, and human factors. These dimensions shadow hardware and software characteristics of ECG wearables and can act as a checklist for future single-lead ECG wearable designs. Trends in ECG de-noising, signal processing, feature extraction, compressive sensing (CS), and remote monitoring applications are later followed to show the emerging opportunities and recent innovations in single-lead ECG wearables.

Horizons in Single-Lead ECG Analysis From Devices to Data