HeartQuake

Abstract

This work presents HeartQuake, a low cost, accurate, non-intrusive, geophone-based sensing system for extracting accurate electrocardiogram (ECG) patterns using heartbeat vibrations that penetrate through a bed mattress. In HeartQuake, cardiac activity-originated vibration patterns are captured on a geophone and sent to a server, where the data is filtered to remove the sensor's internal noise and passed on to a bidirectional long short term memory (Bi-LSTM) deep learning model for ECG waveform estimation. To the best of our knowledge, this is the first solution that can non-intrusively provide accurate ECG waveform characteristics instead of more basic abstract features such as the heart rate using bed-mounted geophone sensors. Our extensive experimental results with a baseline dataset collected from 21 study participants and a longitudinal dataset from 15 study participants suggest that HeartQuake, even when using a general non-personalized model, can detect all five ECG peaks (e.g., P, Q, R, S, T) with an average error of 13 msec when participants are stationary on the bed. Furthermore, clinically used ECG metrics such as the RR interval and QRS segment width can be estimated with errors 3 msec and 10 msec, respectively. When additional noise factors are present (e.g., external vibration and various sleeping habits), the estimation error increases, but can be mitigated by using a personalized model. Finally, a qualitative study with 11 physicians on the clinically perceived quality of HeartQuake-generated ECG signals suggests that HeartQuake can effectively serve as a screening tool for detecting and diagnosing abnormal cardiovascular conditions. In addition, HeartQuake's low-cost and non-intrusive nature allow it to be deployed in larger scales compared to current ECG monitoring solutions.