A Novel Algorithm for HRV Estimation from Short-Term Acoustic Recordings at Neck.

Heart rate variability (HRV) is an important noninvasive parameter to monitor the activity of the autonomic nervous system. This paper proposes an algorithm to analyze HRV by processing the acoustic data, recorded by placing a small, wearable sensor on the suprasternal notch (at neck) of an adult subject, primarily intended to record breathing sounds. The method used an empirical data analysis approach of the Hilbert-Huang transform (HHT) to construct an instantaneous energy envelope and segment the cardiac cycle by detecting S1 and S2 sounds using the K-means algorithm. The time-domain HRV analysis for the short-term recordings of 10 subjects demonstrated a close agreement with the reference ECG signal. The instantaneous heart rate (IHR) comparisons yielded an accuracy of 95.78% and 92.35% for S1 and S2 sounds respectively. The experimental results showed that the proposed algorithm can provide an accurate HRV analysis for the cardiac signals recorded at the neck.