Spectral Viterbi Algorithm for Contactless Wide-Range Heart Rate Estimation With Deep Clustering

Objective: The main challenge in contactless heartbeat detection comes from breathing and/or body motion, which typically deteriorate heart rate (HR) measurements, due to incorrect selection of spectral peaks associated with HR. To acquire the reliable peak selection on spectrum within a relatively broad range, this article first proposes a spectral Viterbi algorithm. Second, a nonlinear source separation approach is further proposed to eliminate the noises generated by respiration and movements, suppressing the undesired spectral energy. Proposal: Inspired by the fact that the period of peak-to-peak intervals of heartbeat (RRIs) rarely vary within a short duration, a novel spectral Viterbi algorithm is proposed to estimate HR change, by the path metric (PM) of candidate paths of HR change. Moreover, based on a deep recurrent neural network (RNN), deep clustering (DC) is applied to separate out the targeted heartbeat source from Doppler signal, by dividing its spectrogram. Results: On the premise of wide-range HR measurement, the usage of spectral Viterbi algorithm substantially improved the precision compared with typical methods of HR estimation, both in the statuses of human subjects’ sitting still and typewriting. In addition, the combination of DC obtains the smallest average errors. Significance: The proposed spectral Viterbi algorithm with DC is provided with three main strengths: 1) good adaptability to wide-range HR change; 2) robustness to nonlinearly mixed signal and noises; and 3) requirement of only a single-channel sensor.