On the estimation of beat-to-beat time domain heart rate variability indices from smoothed heart rate time series

This study tests the feasibility of estimating some time-domain heart rate variability indices (the standard deviation of the RR time series, SDNN, and the standard deviation of the differentiated RR time series, or RMSSD) from smoothed and rounded to the nearest beat per minute heart period time series using shallow neural networks. These time series are often stored in wearable devices instead of the beat-to-beat RR time series. Because the algorithm for obtaining the recorded mean heart rate in wearable devices is often not disclosed, this study test different hypothetic sampling strategies and smoothers. Sixteen features extracted from 5 minute smoothed heart period time series were employed to train, validate, and test shallow neural networks in order to provide estimates of the SDNN and RMSSD indices from freely available public databases RR time series. The results show that, using the proposed features, the median relative error (averaged for each database) in the SDNN ranges from 2% to 14% depending on the smoothness, sampling strategy, and database. The RMSSD is harder to estimate, and its median relative error ranges from 6% to 32%. The proposed methodology can be easily extended to other averaged heart rate time series, HRV indices and supervised learning algorithms