A Peak Detection Algorithm for Multimodal Physiological Signals Based on U-Net

Fiducial points detection serves as the cornerstone of physiological signal processing. Conventional multimodal biomedical sensors require the deployment of diverse or reconfigurable algorithms to handle different types of signals. However, these approaches can be complex and costly in terms of hardware, or require more processing time. To address these challenges, a unified adaptive thresholding method enhanced with a signal synthesizer is proposed to processing multimodal physiological signals to get R-peak, systolic peak and spike, respectively. The proposed approach is evaluated on MIT-BIH, MIMIC and Quiroga databases, achieving precision of 99.03%, 99.38%, 98.81% and recall rate of 99.34%, 99.63%, 98.14% for R-peak, systolic-peak and spike-potential, respectively. These results demonstrate a promising energy-efficient solution for multimodal real-time monitoring.