Stochastic Modeling Based Nonlinear Bayesian Filtering for Photoplethysmography Denoising in Wearable Devices

Photoplethysmography (PPG) has shown its great potential for noninvasive health monitoring, but its application in wearable devices is largely impeded due to its extreme vulnerability to motion artifacts. In this article, we proposed a new stochastic modeling based nonlinear Bayesian filtering framework for the recovery of corrupted PPG waveform under strenuous physical exercise in wearable health-monitoring devices. A deep recurrent neural network was first recruited for accurate cardiac-period segmentation of corrupted PPG signals. Then, a stochastic model was applied to extract waveform details from clean PPG pulses, and was further derived into a system-state space. Following this was an extended Kalman filter using the state-space structured by modeling. The covariance of measurement noise was estimated by motion-related information to adjust it into the real physical environment adaptively. Comparison results with state-of-the-art methods on a wearable-device-based 48-subject data set showed the outstanding performance of the proposed denoising framework, with period-segmentation sensitivity and precision higher than 99.1%, instantaneous heart rate (HR) error lower than 2 beats/min, average HR error down to 1.14 beats/min, and recovery accuracy of waveform details significantly improved (p < 0.05). This framework is the first PPG denoising strategy that introduces waveform-modeling methods to ensure detail recovery, and a great example of algorithm fusion between stochastic signal processing and emerging deep learning methods for time-sequential biomedical signal processing.