Analysis and Classification of Arrhythmia using ECG signals

In medical practices, the ECG plays an important role in diagnosing cardiac arrhythmia. In this paper, a novel method for classification of various types of arrhythmia using morphological and dynamic features is presented. Discrete wavelet transform (DWT) is applied on each heart beat to obtain the morphological features. It provides better time and frequency resolution of the electrocardiogram (ECG) signal. A survey of ECG classification into arrhythmia types is presented in this paper. Early and accurate detection of arrhythmia types is important in detecting heart diseases and finding treatment of a patient. The main purpose of this paper is to design an efficient neural network and also to implement reliable techniques for the classification of various ECG arrhythmia conditions. The obtained accuracy level of arrhythmia detection is 98%, which is the highest rate of performance. The presented approach by GSNN to classify and predict arrhythmia will provide efficient arrhythmia detection as compared to other techniques. The suggested scheme will improve the prediction and classification efficiency.