A hybrid EMD-DWT based algorithm for detection of QRS complex in electrocardiogram signal

Accurate QRS detection is an important first step for almost every electrocardiogram (ECG) signal analysis. However, detecting QRS is difficult, not only because of the large variety, but also as a result of interference caused by various types of noise. This paper employs a hybrid feature extraction technique of ECG signal for the detection of cardiac abnormalities. Noise removal and accurate QRS detection play a major role in the analysis of ECG signals. In this paper various types of noises such as additive white Gaussian noise, baseline wander and power line interference is eliminated to enhance the signal quality. This study proposes an improved QRS complex detection algorithm based on the combination of empirical mode decomposition-discrete wavelet transform (EMD-DWT) with threshold and compared with ordinary discrete wavelet transform. The system efficacy and performance have been evaluated using accuracy, sensitivity (Se), positive predictive value (PPV) and detection error rate (DER). The results show the high accuracy of the proposed EMD-DWT algorithm, which attains a detection error rate of 1.1233%, a sensitivity of 99.28%, and a positive predictive value of 99.99%, evaluated using the MIT-BIH arrhythmia database. The proposed algorithm improves the accuracy of QRS detection compared to state-of-art methods.