An Efficient R-Peak Detection in Electro-Cardio-Gram Signal Using Intelligent Signal Processing Techniques

The accurate detection of R-peaks in Electro-Cardio-Gram (ECG) signals plays a crucial role in identifying cardiovascular issues and determining heart rate variability (HRV). However, existing peak detection methods often struggle with non-stationary ECG signals and are prone to false or missed detections. To address these challenges, this paper presents an innovative approach based on Intelligent Signal Processing (ISP) for R-peak detection. The proposed method comprises three main stages: Decomposition of the ECG signal using Fractional Wavelet Transform (FrWT) or Fractional Fourier Transform (FrFT). This stage allows for the effective analysis of non-stationary components in the ECG signal, enhancing the accuracy of subsequent processing. Extraction of the energy envelope using the calculation of Shannon energy envelope (SEE). By capturing the energy distribution of the signal, this step provides a robust representation of the ECG waveform, facilitating the identification of R-peaks. Identification of True R-peaks through advanced signal processing techniques. By analyzing the energy envelope and employing intelligent algorithms, true R-peaks are accurately distinguished from noise or artifacts, thereby minimizing false detections and missed peaks. Simulation results demonstrate the superior performance of the proposed R-peak detection method (i.e. FrWT) compared to existing techniques. The approach exhibits enhanced accuracy, sensitivity, and robustness, even in challenging scenarios characterized by non-stationary ECG signals. Overall, the presented ISP-based technique offers a promising solution for improving the reliability and effectiveness of R-peak detection in ECG analysis, thereby aiding in the early detection and diagnosis of cardiovascular abnormalities.