Heart Rate Variability Analysis From Electrocardiogram (ECG) and Photoplethysmogram (PPG) Signals By Using Soft Computing Technique

The use of biomedical signal processing software to aid in the analysis and interpretation of electrocardiograms is not a new development but rather an established field of study. The identification of the QRS complex, P and T waves, and the measurement of the QT segment are crucial to the efficiency of an automatic ECG analysis system. Diagnosis of heart function and other crucial facts about the subject's cardiovascular activity can be gleaned through the detection and measurement of characteristic waves. To get a feel for your heart rate, for instance, you need to identify QRS complexes, which are then utilized as a benchmark for beat alignment. Myocardial infarction is also linked to morphological changes in the QT interval and elevation/depression of the ST segment (MI). R peak registration is the foundation for automatic peak detection and segmentation, including the identification of onset and termination times. The position of the QRS complex is indicated by the index of the R peak, or the index of the QS complex peak if the R peak is negative. In this study, researchers analyzed HR and HRV signals from a range of participants, including those of different ages, sexes, and heart problems. The detection of variations is done with the aid of statistical indices. During this process, we also create a database of electrocardiogram (ECG) and photoplethysmogram (PPG) readings from our lab. The peaks of the PPG are also picked up, and a comparison between the HR measured by the ECG and the PPG demonstrates that the PPG can be used as a substitute for HRV detection.