Interpretable photoplethysmography-based machine-learning model for noninvasive assessment of systolic blood pressure

Hypertension significantly contributes to cardiovascular morbidity and mortality but is often underdiagnosed and poorly controlled, as intermittent cuff-based measurements provide only discrete readings and miss short-term vascular dynamics. Photoplethysmography (PPG), a noninvasive optical technique tracking pulsatile blood-volume changes, offers a practical, low-cost, wearable-compatible alternative capable of capturing temporal and morphological waveform patterns. Unlike dual-sensor or calibration-dependent systems, single-site PPG can extract indices linked to arterial stiffness, compliance, and wave reflection, providing physiologically interpretable vascular information. Advances in signal processing and interpretable machine learning have shifted PPG analysis from descriptive morphology to feature-driven clinical prediction. Integrating waveform metrics-such as systolic amplitude, contour sharpness, and harmonic energy distribution-with demographic variables enables transparent modeling of blood-pressure status.

To develop an interpretable machine-learning model for classifying systolic blood pressure (SBP) status using physiological and morphological features derived from a single PPG signal.

We analyzed 860 participants and extracted waveform-derived and clinical features from 90-second fingertip PPG recordings. A LASSO-regularized logistic regression model with fivefold cross-validation was used for feature selection and classification, and model interpretability was assessed using SHAP values.

The final model achieved an AUC of 0.83 (95% CI 0.79-0.87), an F1-score of 0.83, and a Cohen's d of 1.39. Five predictors were retained: age, BMI, P1 amplitude, waveform sharpness (1_10), and harmonic ratio (H2/H1). Higher P1, greater waveform sharpness, and higher BMI were positively associated with elevated SBP, whereas H2/H1 was negatively associated.

An interpretable PPG-based model using physiologically meaningful features can distinguish hypertensive from normotensive individuals and may serve as a practical digital screening tool for elevated systolic blood pressure.