QRS COMPLEX DETECTION USING OPTIMAL DISCRETE WAVELET

Abstract

Wavelet transform has emerged as a powerful tool for time frequency analysis of complex nonstationary signals such as the electrocardiogram (ECG) signal. In this paper, the design of good wavelets for cardiac signal is discussed from the perspective of orthogonal filter banks. Optimum wavelet for ECG signal is designed and evaluated based on perfect reconstruction conditions and QRS complex detection. The performance is evaluated by using the ECG records from the MIT-BIH arrhythmia database. In the first step, the filter coefficients (optimum wavelet) is designed by reparametrization of filter coefficients. In the second step, ECG signal is decomposed to three levels using the optimum wavelet and reconstructed. From the reconstructed signal, the range of error signal is calculated and it is compared with the performance of other suitable wavelets already available in the literature. The optimum wavelet gives the maximum error range as 10-14–10-11 which is better than that of other wavelets existing in the literature. In the third step, the baseline wandering is removed from the ECG signal for better detection of QRS complex. The optimum wavelet detects all R peaks of all records. That is using optimum wavelet 100% sensitivity and positive predictions are achieved. Based on the performance, it is confirmed that optimum wavelet is more suitable for ECG signal.