End-to-End Deep Learning for Reliable Cardiac Activity Monitoring using Seismocardiograms

TL;DR

This paper introduces SeismoNet, a deep learning model that accurately detects cardiac activity from motion-based seismocardiogram signals, offering a non-invasive and user-friendly alternative to traditional ECG monitoring.

Contribution

The paper presents SeismoNet, an end-to-end deep convolutional neural network that directly detects R-peaks from noisy SCG signals without manual feature extraction.

Findings

Achieved 0.98 sensitivity in R-peak detection

Achieved 0.98 positive predictive value

Validated on publicly available CEBS dataset

Abstract

Continuous monitoring of cardiac activity is paramount to understanding the functioning of the heart in addition to identifying precursors to conditions such as Atrial Fibrillation. Through continuous cardiac monitoring, early indications of any potential disorder can be detected before the actual event, allowing timely preventive measures to be taken. Electrocardiography (ECG) is an established standard for monitoring the function of the heart for clinical and non-clinical applications, but its electrode-based implementation makes it cumbersome, especially for uninterrupted monitoring. Hence we propose SeismoNet, a Deep Convolutional Neural Network which aims to provide an end-to-end solution to robustly observe heart activity from Seismocardiogram (SCG) signals. These SCG signals are motion-based and can be acquired in an easy, user-friendly fashion. Furthermore, the use of deep learning enables the detection of R-peaks directly from SCG signals in spite of their noise-ridden morphology and obviates the need for extracting hand-crafted features. SeismoNet was modelled on the publicly available CEBS dataset and achieved a high overall Sensitivity and Positive Predictive Value of 0.98 and 0.98 respectively.