Detection of QRS complex in electrocardiogram signal based on a combination of hilbert transform, wavelet transform and adaptive thresholding

Electrocardiogram (ECG) signal is one of the most important and most used biologic signals which have a significant role in diagnosis of heart diseases. Extraction of QRS complex and obtaining its characteristics is one of the most important parts in ECG signal processing. R wave is one of the main sections of QRS complex which has the essential role in determining and diagnosis of heart rhythm irregularities and also in determining heart rate variability (HRV). In this paper, we suggest a new algorithm by using a combination of Hilbert transform, wavelet transform and adaptive thresholding. We apply our algorithm on various ECG signals to evaluate its performance and see the proposed method outperforms other methods. All signals proposed in this paper except signals used in modeling part (that use simulated ECG signal in “MATLAB” software) are form MIT-BIH database.