Detecting and classifying life-threatening ECG ventricular arrythmias using wavelet decomposition

In this study, we developed a wavelet-based algorithm for detecting and classifying four types of ventricular arrhythmias. We implemented the algorithm using four different wavelets and compared each result. For extracted arrhythmia episodes from the MIT-BIH arrhythmia and malignant ventricular arrhythmia databases, a Daubechies wavelet of length four gave the best result of the four different wavelets studied. By using wavelet decomposition, we reduced the amount of data necessary to be processed by the algorithm to less than ten percent of the original data.