mmArrhythmia

Abstract

Arrhythmia is a common problem of irregular heartbeats, which may lead to serious complications such as stroke and even mortality. Due to the paroxysmal nature of arrhythmia, its long-term monitoring and early detection in daily household scenarios, instead of depending on ECG examination only available during clinical visits, are of critical importance. While ambulatory ECG Holter and wearables like smartwatches have been used, they are still inconvenient and interfere with users' daily activities. In this paper, we bridge the gap by proposing mmArrhythmia, which employs low-cost mmWave radar to passively sense cardiac motions and detect arrhythmia, in an unobtrusive contact-less way. Different from previous mmWave cardiac sensing works focusing on healthy people, mmArrhythmia needs to distinguish the minute and transient abnormal cardiac activities of arrhythmia patients. To overcome the challenge, we custom-design an encoder-decoder model that can perform arrhythmia feature encoding, sampling and fusion over raw IQ sensing data directly, so as to discriminate normal heartbeat and arrhythmia. Furthermore, we enhance the robustness of mmArrhythmia by designing multichannel ensemble learning to solve the model bias problem caused by unbalanced arrhythmia data distribution. Empirical evaluation over 79,910 heartbeats demonstrates mmArrhythmia's ability of robust arrhythmia detection, with 97.32% accuracy, 98.63% specificity, and 92.30% sensitivity.