Towards Personalized Healthcare in Cardiac Population: The Development of a Wearable ECG Monitoring System, an ECG Lossy Compression Schema, and a ResNet-Based AF Detector

TL;DR

This paper presents a comprehensive personalized healthcare system with a wearable ECG device, a lossy compression schema, and a ResNet-based AF detector, improving continuous monitoring, data efficiency, and automated diagnosis of atrial fibrillation.

Contribution

It introduces a novel wearable ECG system with validated performance, a lossy compression method that doubles compression ratio, and a ResNet-based AF detector with superior accuracy.

Findings

Wearable ECG devices showed high intra- and inter-consistency.

The lossy compression schema outperformed existing methods in efficiency and distortion.

The ResNet-based AF detector achieved state-of-the-art F1 scores on benchmark data.

Abstract

Cardiovascular diseases (CVDs) are the number one cause of death worldwide. While there is growing evidence that the atrial fibrillation (AF) has strong associations with various CVDs, this heart arrhythmia is usually diagnosed using electrocardiography (ECG) which is a risk-free, non-intrusive, and cost-efficient tool. Continuously and remotely monitoring the subjects' ECG information unlocks the potentials of prompt pre-diagnosis and timely pre-treatment of AF before the development of any life-threatening conditions/diseases. Ultimately, the CVDs associated mortality could be reduced. In this manuscript, the design and implementation of a personalized healthcare system embodying a wearable ECG device, a mobile application, and a back-end server are presented. This system continuously monitors the users' ECG information to provide personalized health warnings/feedbacks. The users are able to communicate with their paired health advisors through this system for remote diagnoses, interventions, etc. The implemented wearable ECG devices have been evaluated and showed excellent intra-consistency (CVRMS=5.5%), acceptable inter-consistency (CVRMS=12.1%), and negligible RR-interval errors (ARE<1.4%). To boost the battery life of the wearable devices, a lossy compression schema utilizing the quasi-periodic feature of ECG signals to achieve compression was proposed. Compared to the recognized schemata, it outperformed the others in terms of compression efficiency and distortion, and achieved at least 2x of CR at a certain PRD or RMSE for ECG signals from the MIT-BIH database. To enable automated AF diagnosis/screening in the proposed system, a ResNet-based AF detector was developed. For the ECG records from the 2017 PhysioNet CinC challenge, this AF detector obtained an average testing F1=85.10% and a best testing F1=87.31%, outperforming the state-of-the-art.