Research on heart rate estimation algorithm based on dynamic PPG - Guo, Jiawei; Chen, Shiyuan; Lan, Ting; Li, Ruochen; Wang, Lichao; Wu, Yunchong; Zhong, Jun; Zhu, Wei (Frontiers in Signal Processing, 2026)
Heart rate is one of the most vital physiological parameters and is clinically widely used to assess human health status. In recent years, wearable devices based on photoplethysmography (PPG) have been extensively applied in real-time monitoring. However, PPG signals are susceptible to interference from various types of noise during acquisition, particularly motion artifacts (MA), which pose a significant challenge to the accurate extraction of physiological parameters. This study focuses on heart rate extraction from dynamic PPG signals and explores denoising methods combining traditional signal processing and machine learning techniques. The main research contents of this paper are as follows: further improvements are made on the basis of existing algorithms by integrating support vector machines (SVM). A more comprehensive signal quality assessment is performed via SVM, which incorporates the time-domain and frequency?domain statistical characteristics of both PPG signals and triaxial acceleration (ACC) signals. In addition, the short-time Fourier transform (STFT) is integrated to capture time-varying characteristics, thereby mitigating the impact of local signal quality degradation on the analysis of full-window signals. For spectral peak tracking, a Gaussian window is adopted to optimize the spectral search range and a comprehensive analysis is conducted by fusing spectral amplitude information with historical heart rate data. Experimental results demonstrate that the heart rate error of the test set is 1.71 beats per minute (BPM).