A robust continuous wavelet transform (CWT) based for R-peak detection method of ECG

Cardiovascular disease is the main cause of death worldwide. An electrocardiogram (ECG) signals is typically used as the first diagnosis tool to detect abnormality in the heart signal. Reliable detection of R-peak in the ECG signal indicates various heart malfunctions (e.g., arrhythmia) and allows for proactive prevention of death due to cardiovascular disease. Though several R-peak detection methods have been proposed, the existence of noise in ECG signals and changes in QRS morphology compromise the robustness and reliability of these methods. Therefore, the need for a reliable detection of R-peak is crucial for diagnosis of heart abnormalities. This paper introduces a time-efficient and novel continuous wavelet transform (CWT) based method for R-peak detection. The proposed method first transforms the ECG signal in to time-frequency spectrum. The contributions of different frequencies at every time point are then calculated from the time-frequency spectrum to efficiently reduce the impact of noise and generate a summed frequency signal. A threshold technique is also proposed to detect the R-peak from the newly generated signal allows. The MIT-BIH arrhythmia database is used as a reference for validation and comparison of the proposed method with the results of other existing R-peak detection methods. The experimental results prove the efficiency and robustness of the developed method on noisy ECG signals with changes in QRS morphology with 99.87% sensitivity, 99.76% positive prediction value and a detection error rate of only 0.37%. In addition to the high accuracy in detecting R-peaks, the ease-to-use and fast-processing make the proposed method an efficient and reliable tool for real-time abnormality detection in ECG signal.