A Novel Machine-Learning-Based Noise Detection Method for Photoplethysmography Signals

Wearable devices are widespread for continuous health monitoring; capturing various physiological parameters for remote health monitoring and early detection of health issues. These devices are susceptible to interference such as Motion Artifacts (MA) and Baseline Wanders (BW). Mitigating potential false alarms due to those artifacts is an important challenge in wearable healthcare. To tackle this challenge, it is crucial to first identify noise in the signals recorded by wearable systems. Most of the conventional methods rely on reference data like accelerometer data to detect noise in Photoplethysmogram (PPG) signals. This study proposes a Machine Learning (ML)-based approach to distinguish between clean and corrupted segments in PPG signals without relying on other sensors' data. Binary and three-class classification on clean, MA-, and BW-corrupted signals produce promising F1-scores from 89.3% to 99.4%.