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Cardiovascular diseases (CVDs) are the number one cause of death worldwide.While there is growing evidence that the atrial fibrillation (AF) has strongassociations with various CVDs, this heart arrhythmia is usually diagnosedusing electrocardiography (ECG) which is a risk-free, non-intrusive, andcost-efficient tool. Continuously and remotely monitoring the subjects' ECGinformation unlocks the potentials of prompt pre-diagnosis and timelypre-treatment of AF before the development of any life-threateningconditions/diseases. Ultimately, the CVDs associated mortality could bereduced. In this manuscript, the design and implementation of a personalizedhealthcare system embodying a wearable ECG device, a mobile application, and aback-end server are presented. This system continuously monitors the users' ECGinformation to provide personalized health warnings/feedbacks. The users areable to communicate with their paired health advisors through this system forremote diagnoses, interventions, etc. The implemented wearable ECG devices havebeen evaluated and showed excellent intra-consistency (CVRMS=5.5%), acceptableinter-consistency (CVRMS=12.1%), and negligible RR-interval errors (ARE<1.4%).To boost the battery life of the wearable devices, a lossy compression schemautilizing the quasi-periodic feature of ECG signals to achieve compression wasproposed. Compared to the recognized schemata, it outperformed the others interms of compression efficiency and distortion, and achieved at least 2x of CRat a certain PRD or RMSE for ECG signals from the MIT-BIH database. To enableautomated AF diagnosis/screening in the proposed system, a ResNet-based AFdetector was developed. For the ECG records from the 2017 PhysioNet CinCchallenge, this AF detector obtained an average testing F1=85.10% and a besttesting F1=87.31%, outperforming the state-of-the-art.

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