Abstract
Automatic recognition of cardiac arrhythmias is important for diagnosis of cardiac abnormalies. Several algorithms have been proposed to classify ECG arrhythmias; however, they cannot perform very well. Therefore, in this paper, an expert system for ElectroCardioGram (ECG) arrhythmia classification is proposed. Discrete wavelet transform is used for processing ECG recordings, and extracting some features, and the Multi-Layer Perceptron (MLP) neural network performs the classification task. Two types of arrhythmias can be detected by the proposed system. Some recordings of the MIT-BIH arrhythmias database have been used for training and testing our neural network based classifier. The simulation results show that the classification accuracy of our algorithm is 96.5% using 10 files including normal and two arrhythmias.
Highlights
The ElectroCardioGram (ECG) signal is an important signal among all bioelectrical signals
We propose a Neural Network (NN) based algorithm for classification of Paced Beat (PB), Artial Premature Beat (APB) arrhythmias as well as the normal signal
The wavelet representation of a discrete signal X consisting of N samples can be computed by convolving X with the Low-Pass Filters (LPF) and High-Pass Filters (HPF) and down-sampling the output signal by 2, so that the two frequency bands each contains N/2 samples
Summary
The ElectroCardioGram (ECG) signal is an important signal among all bioelectrical signals. Analysis of the ECG signal is widely used in the diagnosis of many cardiac disorders It can be recorded from the wave passage of the depolarization and repolarization processes in the heart. Several algorithms have been proposed to classify ECG heartbeat patterns based on the features extracted from the ECG signals. Classification techniques for ECG patterns include linear discriminate analysis [2], support vector machines [9], artificial neural networks [10,11,12,13,14], mixture-of-experts algorithms [12], and statistical Markov models [15, 16]. Computer-based diagnosis algorithms have generally three steps, namely: EGC beat detection, extraction of useful features from beats, and classification. We propose a Neural Network (NN) based algorithm for classification of Paced Beat (PB), Artial Premature Beat (APB) arrhythmias as well as the normal signal.
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