Optimizing R-Wave Amplitude Threshold Filters in Predicting Coronary Artery Disease and Myocardial Infarction Using Heart Rate Variability:

The measurement of ECG-derived heart rate variability (HRV) provides early diagnosis and prevention of cardiac events such as coronary artery disease (CAD) and myocardial infarction (MI). The disease severity causes ECG shape deformation, leading to difficulty in R-wave detection required for heart rate variability (HRV) analysis. Therefore, the study aimed to optimize R-wave threshold filters at 50%, 60%, 70%, and 80% within the framework of HRV analysis for better diagnosis of MI and CAD. Lead-II ECG was recorded from CAD (n=15), MI (n=15), and Control (n=10) group participants using an MP45 amplifier (Biopac Systems Inc., USA). The ECG morphologic features were extracted to analyze the change in R-wave amplitude. The R-R interval time series was extracted at different threshold filters (50%, 60%, 70%, and 80%) and used for heart rate variability (HRV) analysis. The HRV features extracted using Kubios V2.0 in time, frequency, and nonlinear domains at different threshold filters were compared. The obtained findings revealed that R-wave amplitude was suppressed under the CAD and MI groups in comparison to the control subjects. The observed results also suggested 70% threshold filters for optimum detection of R-wave under CAD and MI. Further, the time and frequency domains presented the best result in comparison to the nonlinear HRV domain. The present study suggested that HRV analysis may be made more predictive for various cardiac diseases by optimizing R-wave threshold filters.