Exploiting Tunable Q-Factor Wavelet Transform Domain Sparsity to Denoise Wrist PPG Signals

Abstract

Wearable health monitoring devices based on photoplethysmogram (PPG) can estimate valuable physiological parameters and are often regarded as a touchstone for indicating the cardiovascular status of a person. Severe motion artifacts are generally observed in the acquired PPG signals from the biosensors in the wearables when the PPG acquisition is performed during different physical activities. Classical filters can remove thermal noise and electromagnetic interference from the raw PPG signals, but they fail to handle the motion artifacts that cover a wider range of signal frequencies. This work introduces a new motion artifact removal scheme that exploits the sparsity of PPG signals in the tunable-Q factor wavelet transform (TQWT) domain using the basis pursuit denoising (BPDN) scheme. The proposed scheme deftly removes noise while preserving the original morphology of the PPG signal without using any reference accelerometer sensor data. In particular, a regularized SALSA-based BPDN algorithm is employed for sparse recovery, which utilizes a soft thresholding approach to denoise the noisy PPG signals. TQWT can be tuned according to the oscillatory behavior of the raw PPG signal. At the same time, BPDN exploits the sparsity of the wavelet coefficients to yield an artifact-free reconstruction of PPG. Further, heart rate estimation is performed using the denoised PPG signal with support vector regression to demonstrate the clinical information preservation during the proposed denoising process. The proposed denoising scheme is evaluated on two publicly available databases, and an overall minimum mean absolute error (MAE) of 1.03 and 0.76 beats per minute (bpm) is achieved. The suggested denoising approach has demonstrated better performance with the lowest MAE compared to the previously reported methods.