Delineation of QRS features and denoising of ECG signal using Fejer Korovkin wavelet

Cardiovascular diseases are a leading cause of increased mortality worldwide. Electrocardiogram (ECG) signals is the most effective tool and plays a crucial role in diagnosing heart conditions. The detection of R peaks within the QRS complex is pivotal for accurate heart condition diagnosis. However, denoising ECG signals and accurately detecting R peaks present significant challenges due to various noise factors. This research paper proposes an efficient R peak detection technique that leverages the Maximal Overlap Discrete Wavelet Transform (MODWT) in conjunction with the Fejer–Korovkin (FK) wavelet function. The proposed methodology is rigorously validated using standard physionet databases. The results of the proposed algorithm demonstrate exceptional performance for the TWADB and QTDB databases. For TWADB and QTDB, a sensitivity (Se+) of 99.97%, a positive prediction (P+) rate of 99.64%, a low detection error rate (DER) of 0.04 and QTDB, a Se+ of 99.95%, a P+ rate of 99.99%, and an extremely low DER of 0.063 has been achieved, respectively.