Influence of Empirical Mode Decomposition on Heart Rate Variability Indices Obtained from Smartphone Seismocardiograms

Heart rate variability (HRV) is a physiological variation of time interval between consecutive heart beats caused by the activity of autonomic nervous system. Seismo-cardiography (SCG) is a non-invasive method of analyzing cardiac vibrations and can be used to obtain inter-beat intervals required to perform HRV analysis. Heart beats on SCG signals are detected as the occurrences of aortic valve opening (AO) waves. Morphological variations between subjects complicate developing annotation algorithms. To overcome this obstacle we propose the empirical mode decomposition (EMD) to improve the signal quality. We used two algorithms to determine the influence of EMD on HRV indices: the first algorithm uses a band-pass filter and the second algorithm uses EMD as the first step. Higher beat detection performance was achieved for algorithm with EMD (Se=0.926, PPV=0.926 for all analyzed beats) than the algorithm with a band-pass filter (Se=0.859, PPV=0.855). The influence of analyzed algorithms on HRV indices is low despite the differences of heart beat detection performance between analyzed algorithms.