Evaluation of Heart Rate Variability by Smartphone App Using Pulse Photoplethysmography in Acute Myocardial Infarction.

Highlights: Smartphone-based PPG measurements of SDNN and rMSSD effectively differentiate AMI patients from healthy controls, with SDNN ≤21.35 ms showing high sensitivity (87.6%) for detection.Reduced HRV indices (SDNN and rMSSD) correlate with clinical severity markers, including lower LVEF, higher Killip class, and multivessel CAD.Short-term HRV assessment via smartphone PPG offers a noninvasive, accessible tool for early AMI screening and risk stratification.SDNN outperforms rMSSD in diagnostic accuracy (AUC 0.832 vs. 0.738), supporting its utility in digital health monitoring for cardiovascular disease. Background: Heart rate variability (HRV) correlates with localized myocardial ischemia and predicts adverse cardiovascular outcomes after acute myocardial infarction (AMI), including sudden cardiac death, non-sudden cardiac death, and noncardiac death. Photoplethysmography (PPG) measurements demonstrate good agreement with ECG for time-domain HRV indices. In this study, HRV was measured via smartphone PPG, focusing on standard deviation of all normal RR (NN) intervals (SDNN) and root mean square of successive differences (rMSSD)-parameters recognized for their low error and recommended for clinical use. Methods: This cross-sectional case-control study was conducted at a tertiary hospital in Vietnam. HRV indices (SDNN and rMSSD) were measured for 2 minutes using a camera-based PPG smartphone application. Clinical data were collected at admission. Linear and logistic regression analyses assessed associations between HRV, AMI status, and clinical severity. Receiver operating characteristic (ROC) curve analysis evaluated diagnostic performance. Results: A total of 101 patients with AMI and 121 age- and sex-matched healthy controls were included. The AMI group exhibited significantly lower HRV indices, with a mean SDNN of 20.63 ±10.16 ms and rMSSD of 23.67±12.38 ms, compared with 33.99±11.72 ms (SDNN) and 35.9 ±16.21 ms (rMSSD) in the control group (P <0.001 for both). An SDNN cutoff of ≤21.35 ms yielded an area under the curve of 0.832, with a sensitivity of 87.6% and specificity of 62.4% for identifying AMI. Lower HRV was also significantly associated with higher clinical severity indicators, including reduced left ventricular ejection fraction, Killip class II-IV, regional wall motion abnormalities, and multivessel coronary artery disease. Conclusions: The use of camera-based HRV smartphone applications to measure short-term SDNN and rMSSD may serve as a novel digital health tool to improve the detection of coronary artery disease, particularly AMI, given its simplicity and noninvasive nature.