A lightGBM-based method for the signal quality assessment of wrist photoplethysmography.

In the application of wrist-based Photoplethysmography (PPG) devices for health monitoring, assessing the quality of PPG signals is essential for accurately monitoring cardiovascular parameters. However, the wrist-based PPG signal is susceptible to motion and light interference in practical applications. A machine learning-based signal quality assessment algorithm for wrist PPG signals was proposed to improve the accuracy and reliability of the monitoring data. The algorithm's performance was evaluated on two datasets: the publicly available Wearable and Clinical Signals (WCS) dataset, containing 3,038 wrist-based PPG segments collected from 18 volunteers using an Empatica E4 device; our LAB dataset, comprising 2,426 wrist-based PPG segments acquired from 12 volunteers under varied interference conditions via a custom-developed wearable watch system. Data pre-processing encompassed denoising and normalization, followed by the extraction of 11 mathematical statistical features in time and frequency domains based on pulse wave morphology and 2 features based on template matching (Euclidean Distance and Correlation Coefficient). The classifier, constructed using the LightGBM algorithm, achieved high performance under rigorous leave-one-subject-out cross-validation (LOSO-CV) on the WCS dataset (accuracy = 92.6%, precision = 96.6%, recall = 89.8%, F1-score = 91.4%, AUC = 0.925) and the LAB dataset (accuracy = 96.1%, precision = 98.1%, recall = 95.2%, F1-score = 96.6%, AUC = 0.941). The results show that the machine learning algorithm for wrist-based PPG signal quality assessment, combining the mathematical statistical features in time and frequency domains and the template matching features, can effectively enhance the performance of signal quality assessment, and provides a powerful tool for improving the accuracy of wearable devices in cardiovascular health monitoring.