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

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