Smart Seismocardiography: A machine learning approach for automatic data processing

Seismocardiography (SCG) is a non-invasive method that measures local vibrations created by the mechanical cardiovascular exercises on the chest wall. Thereby, mechanical movements of the heart are recorded in real-time from vibration sensors positioned on the chest of the subject, to further compute the heart rate and retrieve the SCG waveform.

Although such events have been widely studied, robust signal processing methods, analogous to electrocardiography (ECG), remain a challenging task. On the other hand, the use of piezoelectric sensors has been favored in recent years due to its features and low-cost. However, robust data processing techniques should be developed to increase their performance and reliability.

In this work, we propose an attractive method for SCG data processing based on the k-means clustering algorithm to automatically label waveform events. Interestingly, the SCG signals are recovered from a custom-made device built around an ultra-low-cost piezoelectric sensor. Once the signals are measured, they are pre-processed by spectral filtering using the power spectral density (PSD) representation. Afterwards, the signal spectrum is used to filter out the useful components to compute the heart rate (HR) in the range from 50 – 120 Beats Per Minute (BPM). Thereby, the filtered signal is sequentially segmented, and every frame is processed by a light-weight k-means algorithm.

Finally, we show the performance of the smart seismocardiography by analyzing SCG waveforms at different physiological conditions.