MorphSSF: a fast slope-sum adaptive-period dilation algorithm for photoplethysmography beat detection

Abstract Objective: Detecting heartbeats in photoplethysmography (PPG) underlies heart-rate, heart-rate-variability and arrhythmia analysis on wearable and clinical devices, and must be both efficient and accurate. Several accurate benchmark detectors, especially the multi-scale peak-and-trough family (MSPTD/MSPTDfast), rely on costly local-maxima scalograms; we seek a detector that improves the benchmark-wide speed–accuracy trade-off rather than merely accelerating one baseline. Approach: MorphSSF converts each PPG window to a slope-sum function (SSF), estimates the dominant pulse period from a band-limited autocorrelation, and selects candidates as fixed points of a period-matched running-maximum dilation on the SSF. This combines systolic-upstroke enhancement with adaptive period spacing using one-dimensional operations and no scalogram. We evaluate under identical conditions on seven datasets (544 recordings) with benchmark-wide rank tests, detector-specific exclusions and one-to-one matching. Separate fixed-point C and causal MATLAB variants are evaluated. Main results: MorphSSF has the best mean dataset F1 rank. Against MSPTDfast (v2), sensitivity tests favour MorphSSF on five datasets (including a zero median effect on CapnoBase); F1 is higher on MIMIC-test, MIMIC-train and PPG-DaLiA, while positive predictive value (PPV) is lower on WESAD. Both pooled and equal-dataset analyses favour MorphSSF in F1 against all eight baselines. MorphSSF is 3.05–218.18× faster than the eight baselines, including 3.23× faster than MSPTDfast (v2), based on median runtime ratios across recordings. All detectors receive the same prefiltered PPG input, with this shared filtering step excluded from runtime measurements. Significance: Period-adaptive detection on the slope-sum provides an efficient, scalogram-free approach to PPG beat detection.