Quality-aware PPG pulse-rate classification and regular/irregular rhythm recognition to gate PPG-based BP analysis (Sivanjaneyulu, Manikandan, Boppu, Cenkeramaddi; Healthc Technol Lett 2026)

A rule-based front end meant to sit before PPG BP estimation: Pipeline: preprocessing → PPG signal-quality assessment (PPG-SQA) → systolic peak detection → decision rules. Pulse-rate class (bradycardia / normal / tachycardia) from the average peak-to-peak interval (PPI_avg), compared against a beat-counting approach. Rhythm (regular vs irregular) from a PPI-variation rule with a 4 bpm threshold. Four PPG-SQA variants compared to find a lightweight, high-sensitivity gate that drops noisy segments.

• Form factor: mixed (standard PPG databases + own recordings with several sensors; sites not stated in abstract)

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.

Verbatim from the publisher's DOI record (opens in a new tab) · Publisher licence: http://creativecommons.org/licenses/by-nc-nd/4.0/

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