Prediction of electrocardiography features points using seismocardiography data: a machine learning approach

Seismocardiography (SCG) is a cardiac diagnostic method which evaluates vibrations of the chest via accelerometers. Current prototypical wearable SCG sensor patches are combined with electrocardiography (ECG) sensors which enables the estimation of cardiac timing intervals such as pre-ejection period (PEP). We envision a system with only SCG sensors to increase the battery run time and wearing comfort. Therefore, we present a method to predict the timing of ECG R-peak based on SCG data. The developed machine learning (ML) algorithms were evaluated with respect to algorithm complexity, body postures, and personalization schemes, on a dataset consisting of 10 subjects. Our findings show that R-peaks can be predicted with a mean error of 4:16 ms for supine, 9:43 ms for sitting, and 14:3 ms for standing posture. While personalization results only in minor improvements, using more complex ML classifier is beneficial for standing posture, which is more affected by motion artifacts.