CVD PREDICTION FROM HRV DERIVED FROM WEARABLE PPG

Journal of problem in computer science and information technologies, 2025

Full text Open access: Gold Metadata (CSL-JSON)
DOI 10.26577/jpcsit202542
Data confidence 85 high
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Abstract

Cardiovascular disease is the leading global cause of death; ischemic heart disease (IHD) is its most common and lethal form, motivating scalable, non-invasive screening. We tested whether a single 60-minute photoplethysmography (PPG) recording from the Zhurek fingertip wearable can distinguish healthy autonomic control from IHD-related dysregulation. Agreement with a three-lead Holter reference was clinically acceptable (HR −0.601 bpm; SDNN +33.1 ms; RMSSD −4.8 ms). Forty hour-long sessions were analyzed (20 healthy, 18–22 years; 20 angiography-confirmed IHD) using eight HRV/demographic features. Mann–Whitney tests showed significant differences for SDNN, LF, HF, Max_HR, BMI, and age (p<0.05), and a two-component PCA (49.5% variance) separated cohorts without labels. SHAP for a CatBoost model highlighted LF and age as strongest positive contributors and HF as protective. Thus, one-hour PPG preserves diagnostically useful autonomic signatures, enabling ~24× shorter monitoring than Holter and supporting scalable ambulatory IHD risk stratification.