Knowing Your Heart Condition Anytime

心跳 计算机科学 QRS波群 阈值 光容积图 实时计算 人工智能 模式识别(心理学) 滤波器(信号处理) 计算机视觉 心脏病学 医学 计算机安全 图像(数学)
作者
Lei Wang,Xingwei Wang,Dalin Zhang,Xiaolei Ma,Yong Zhang,Haipeng Dai,Chenren Xu,Zhijun Li,Tao Gu
出处
期刊:Proceedings of the ACM on interactive, mobile, wearable and ubiquitous technologies [Association for Computing Machinery]
卷期号:7 (3): 1-28 被引量:1
标识
DOI:10.1145/3610871
摘要

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.

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