Time-frequency domain analysis via pulselets for remote heart rate estimation showed an improvement over current state-of-the-art methods when tested on a dataset of 30 videos.
Does time-frequency domain analysis via pulselets improve non-contact heart rate estimation from remotely acquired PPGs compared to current state-of-the-art methods?
A novel time-frequency domain analysis method using pulselets improves the accuracy of non-contact heart rate estimation from remotely acquired photoplethysmograms.
A novel method for remote heart rate estimation via analysis in the time-frequency domain is proposed. A photoplethysmogram (PPG) waveform is constructed via a Bayesian minimization approach with the required posterior probability obtained through an importance-weighted Monte Carlo sampling method. A pulselet (wavelet chosen for its similarities with a finger pulse oximiter PPG waveform), is used in the continuous wavelet transform to produce a map of the wavelet energy response in the time-frequency domain. This allows the heart rate frequency to be estimated at each time step, accounting for naturally occurring changes in heart rate over time which may cause error with frequency domain based methods. The frequency corresponding to the highest wavelet response at each time step is averaged across the entire time series to estimate the average heart rate. Experimental results against a data set of 30 videos show an improvement over current state-of-the-art methods.
Chwyl et al. (Wed,) conducted a other in Heart rate estimation (n=30). Time-frequency domain analysis via pulselets vs. Current state-of-the-art methods was evaluated on Heart rate estimation accuracy. Time-frequency domain analysis via pulselets for remote heart rate estimation showed an improvement over current state-of-the-art methods when tested on a dataset of 30 videos.