Key result
PPG rhythm recognition with a 4 bpm threshold achieves ~100% diagnostic accuracy.
Why the study?
Noisy photoplethysmogram signals are unavoidable in continuous and long-term health monitoring, requiring lightweight quality-aware pulse rate classification and rhythm recognition methods to reduce false alarms and computational power while improving the trustworthiness of cardiac arrhythmia recognition and blood pressure analysis.
Does a quality-aware PPG framework using average peak-to-peak intervals and a 4 bpm threshold improve accuracy and reduce false alarms in pulse rate classification and rhythm recognition across varying blood pressure classes?
Does a quality-aware PPG framework using average peak-to-peak intervals and a 4 bpm threshold improve accuracy and reduce false alarms in pulse rate classification and rhythm recognition across varying blood pressure classes?
Integrating lightweight signal quality assessment with average peak-to-peak interval pulse rate classification and a 4 bpm threshold rhythm recognition algorithm achieves >99% accuracy in rhythm detection while discarding noisy PPG segments to reduce false alarms during continuous health monitoring.
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 ()‐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.
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Sivanjaneyulu et al. (2026) studied Cardiac arrhythmia and blood pressure variations (hypotension, normotension, hypertension). Quality-aware PPG pulse rate classification (average peak-to-peak interval) and rhythm recognition framework vs. Count-based pulse rate classification and non-quality-aware methods was evaluated on Accuracy of rhythm recognition (regular vs. irregular) with a 4 bpm threshold. The PPG rhythm recognition method with a 4 bpm threshold achieved 99.80% accuracy, 99.89% sensitivity, and 99.33% specificity.
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