[paper] Knowing Your Heart Condition Anytime

Lei Wang, Xingwei Wang, Dalin Zhang, Xiaolei Ma, Yong Zhang, Haipeng Dai, 

Chenren Xu, Zhijun Li, Tao Gu

Knowing Your Heart Condition Anytime:

User-Independent ECG Measurement Using Commercial Mobile Phones

Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies

Vol. 7, Issue 3, Article No.: 131, pp 1–28

DOI: 10.1145/3610871

Abstract: Electrocardiogram (ECG) monitoring has been widely explored in detecting and diagnosing cardiovascular diseases due to its accuracy, simplicity, and sensitivity. However, medical- or commercial-grade ECG monitoring devices can be costly for people who want to monitor their ECG on a daily basis. These devices typically require several electrodes to be attached to the human body which is inconvenient for continuous monitoring. To enable low-cost measurement of ECG signals with off-the-shelf devices on a daily basis, in this paper, we propose a novel ECG sensing system that uses acceleration data collected from a smartphone. Our system offers several advantages over previous systems, including low cost, ease of use, location and user independence, and high accuracy. We design a two-tiered denoising process, comprising SWT and Soft-Thresholding, to effectively eliminate interference caused by respiration, body, and hand movements. Finally, we develop a multi-level deep learning recovery model to achieve efficient, real-time and user-independent ECG measurement on commercial mobile phones. We conduct extensive experiments with 30 participants (with nearly 36,000 heartbeat samples) under a user-independent scenario. The average errors of the PR interval, QRS interval, QT interval, and RR interval are 12.02 ms, 16.9 ms, 16.64 ms, and 1.84 ms, respectively. As a case study, we also demonstrate the strong capability of our system in signal recovery for patients with common heart diseases, including tachycardia, bradycardia, arrhythmia, unstable angina, and myocardial infarction.

Fig:  Seismocardiogram (SCG) ECG recovery system:

(a) Typical application scenario with SCG/ECG system 

(b) Interface of the mobile APP.

Acknowledgments: This research is supported by National Natural Science Foundation of China (Grant No. 62102006). This work is also in part supported by The Natural Science Foundation of the Jiangsu Higher Education Institutions of China (Grant No. 1020231697)