Abstract:
ECG contains very important clinical information about the cardiac activities of heart. The features of small variations in ECG signal with time varying morphological characteristics needs to be extracted by signal processing method because there are not visible of graphical ECG signal.
In this work, we have developed and evaluated an electrocardiogram (ECG) feature extraction system based on the multi-resolution wavelet transform. The wavelet transform with scaling function more closely similar to the shape of the ECG (Daubechies wavelets (DWT) and Morlet (CWT)) signal achieved better detection. In the first step, the ECG signal was de-noised by removing the corresponding wavelet coefficients at higher scales. Then, QRS complexes are detected and each complex is used to locate the peaks of the individual waves, including onsets and offsets of the P and T waves which are present in one cardiac cycle. We evaluated the algorithm on MIT-BIH Database, the manually annotated database, for validation purposes. The proposed QRS detector achieved sensitivity of 〖99.18%〗_-^+2.75and a positive predictivity of 〖98.00%〗_-^+4.45over the validation database.
