An Effective Deep Learning Based Approach to Measure Interdependence of Heart Rate Variability Analysis and R-Peaks

Heart rate variability (HRV) is the physiological phenomenon to measure variations between consecutive heartbeats. HRV analysis has been used to analyse and detect different cardiac diseases that rely on R-peaks' reliability and accurate detection. Myocardial infarction (MI) is a main cardiac disease that causes irregularity in heartbeats and some non-specific abnormalities occur in the recorded ECG, including ST changes and T-wave alternans. These abnormalities may be ignored by considering minor changes that can lead to delayed diagnosis. Therefore, the analysis of these abnormalities is a challenging task. This study investigated these non-specific abnormalities to recognize MI patterns based on HRV analysis highlighting the importance of R-peaks detection accuracy. Short-term HRV analysis of two publicly available datasets, MIT-BIH ST Changes and European ST-T changes was performed based on R-peaks detected from ECG signals. R-peaks detection achieved an averaged 99.77% sensitivity, 99.49% positive predictability and 99.32% accuracy for MIT-BIH ST changes dataset whereas for European ST-T changes dataset, 99.85% sensitivity, 99.41% positive predictability, and 99.28% accuracy was achieved. Subsequently, HRV parameters were computed from both datasets and data fusion was performed followed by a deep learning model to find out the most accurate pattern recognition results. For pattern recognition, a finely tuned artificial neural network was applied to HRV parameters, in two scenarios. For both scenarios, an accuracy greater than 99% was achieved. Furthermore, linear regression was also performed on computed HRV measures in both scenarios that observed a similarity of 97% between both datasets. The importance of reliable and accurate R-peaks detection for HRV analysis to analyse and detect different cardiovascular diseases was elaborated in the end.