Improving Wearable PPG Monitoring with Morphology-Aware and Learning-Driven Heartbeat Detection

Photoplethysmography (PPG) is a widely used, non-invasive technique for heart rate monitoring, but its reliability is often compromised by noise and motion artifacts (MAs). This study presents the Peakwise Correlation Pulse Detector (PCPD), an algorithm for detecting heartbeats directly from PPG signals without requiring electrocardiogram (ECG) signals during operation, evaluated here under clean and controlled synthetic pulse-free-noise conditions. PCPD follows a five-step pipeline: (1) signal segmentation with overlap, (2) sliding cross-correlation using high-quality heartbeat templates, (3) computation of the Minimum Correlation Curve (MCC), (4) feature extraction with machine learning-based classification, and (5) peak translation with constraint analysis. The method was evaluated on a clean dataset, comprising a newly collected PPG dataset with manually annotated signal quality and the publicly available BIDMC dataset, and on a noisy dataset that augments the clean data with synthetic pulse-free segments generated from band-limited Gaussian noise. This evaluation framework tests heartbeat detectors under ideal conditions and under a controlled non-informative-noise protocol. On clean signals, PCPD achieved balanced precision–recall scores of 99.13% on the proposed dataset (internal performance) and 97.04% on the independent BIDMC dataset. Under added synthetic pulse-free noise, PCPD achieved accuracies of 99.30% and 97.16% on the proposed and BIDMC datasets, respectively. In comparative experiments against eleven benchmark heartbeat detection algorithms, PCPD achieved the best performance among the evaluated implementations under the tested synthetic-noise protocol. These results support PCPD as a promising PPG-only peak detector and motivate further validation under real motion artifacts and for downstream signal quality assessment.