Automatic ECG Beat Arrhythmia Detection

Background: In recent years automated data analysis techniques have drawn great attention and are used in almost every ?eld of research including biomedical. Arti?cial Neural Networks (ANNs) are one of the Computer- Aided- Diagnosis tools which are used extensively by advances in computer hardware technology. The application of these techniques for disease diagnosis has made great progress and is widely used by physicians. An Electrocardiogram carries vital information about heart activity and physicians use this signal for cardiac disease diagnosis which was the great motivation towards our study. Methods: In this study we are using Probabilistic Neural Networks (PNN) as an automatic technique for ECG signal analysis along with a Genetic Algorithm (GA). As every real signal recorded by the equipment can have di?erent artifacts, we need to do some preprocessing steps before feeding it to the ANN. Wavelet transform is used for extracting the morphological parameters and median ?lter for data reduction of the ECG signal. The subset of morphological parameters are chosen and optimized using GA. We had two approaches in our investigation, the ?rst one uses the whole signal with 289 normalized and de-noised data points as input to the ANN. In the second approach after applying all the preprocessing steps the signal is reduced to 29 data points and also their important parameters extracted to form the ANN input with 35 data points. Results: The outcome of the two approaches for 8 types of arrhythmia shows that the second approach is superior than the ?rst one with an average accuracy of %99.42 .