Quality-Aware Pulse Rate Classification and Rhythm Recognition Method for Improved PPG-Based Blood Pressure Analysis

In this paper, we propose a photoplethysmogram (PPG) signal quality-aware pulse rate (PR) classification and rhythm (regular/irregular) recognition framework. The proposed method comprises preprocessing, PPG signal quality assessment (PPG-SQA), systolic peak detection and decision rules. In this study, we collected a wide variety of normal and abnormal PPG signals from standard PPG databases. Further, the PPG signals are recorded with various sensors and subjects to validate the accuracy and robustness of the method under intra-subject, inter-subject and sensor variability. Evaluation results demonstrate that the average peak-to-peak interval ( ${\mathrm{PPI}}_{\mathrm{avg}}$ )-based PR classification method had a higher accuracy (bradycardia [BC]: 100%, normal: 99.31%, tachycardia [TC]: 95.68% for normotension [NT] blood pressure [BP] class PPG segments) as compared to the counting approach-based PR classification method. Results further showed that the rhythm recognition method with a 4 bpm threshold outperformed, achieving a sensitivity (SE) of 99.89%, specificity (SP) of 99.33% and accuracy (ACC) of 99.80%. To reduce false alarms, we present four PPG-SQA approaches to explore a lightweight and best PPG-SQA with higher SE for detecting noise-free PPG signals. Results demonstrated that the quality-aware PR classification and rhythm recognition methods can improve the trustworthiness of cardiac arrhythmia recognition and BP analysis while reducing false alarms and computational power by discarding noisy PPG signals, which are unavoidable in continuous and long-term health monitoring.