Improved Health Monitoring R-peak Detection in Long-term ECG Signal: Leveraging Hybrid Linearization and LSTM with Grey Wolf Optimization

Introduction: The precise interpretation of the Electrocardiogram (ECG) signal can reveal the condition of the heart. Health Monitoring (HM)-ECG signal analysis can assist in identifying any abnormalities or arrhythmias in the heart with the detection of an R peak in the ECG signal. The following issues are not handled by the conventional algorithm for R peak detection: noise from muscle artifacts, power line interference and baseline drift, and variability in ECG waveforms brought on by pathological abnormalities. Methodology: This research proposes a robust approach for detecting R peaks in QRS complexes using a recurrent neural network. Our proposed methodology was applied to the well-known MITBIH Arrhythmia Database (MIT-DB) dataset and the China Physiological Signal Challenge (2020) database, which contains over a million beats. The hybrid linearization technique used an adaptive filter and Discrete Wavelet Transform (DWT) to remove noise from the ECG signal. The next step was to use principal component analysis (PCA) to extract characteristics from the ECG data. Lastly, the R peak signals were classified using Long Short-Term Memory (LSTM) to improve accuracy through optimization techniques like Grey Wolf Optimization (GWO). The algorithm';s performance was also evaluated using the MIT-BIH Arrhythmia database and the China Physiological Signal Challenge (2020). Results: The suggested formal technique yielded the best results for R-peak detection on CPSC- DB, with an F1-score of 95.3%, recall of 96.8%, accuracy of 99.5%, and precision of 95.3%. Conclusion: The F1-score, recall, and precision of the algorithms on MIT-DB are all equivalent to, or better than, those of the competing methods.