This paper proposes a novel system to classify three types of electrocardiogram beats, namely normal beats and two manifestations of heart arrhythmia. This system includes three main modules: a feature extraction module, a classifier module, and an optimization module. In the feature extraction module, a proper set combining the shape features and timing features is proposed as the efficient characteristic of the patterns. In the classifier module, a multi-class support vector machine (SVM)-based classifier is proposed. For the optimization module, a particle swarm optimization algorithm is proposed to search for the best value of the SVM parameters and upstream by looking for the best subset of features that feed the classifier. Simulation results show that the proposed algorithm has very high recognition accuracy. This high efficiency is achieved with only little features, which have been selected using particle swarm optimizer.

This paper proposes a four stage, denoising, feature extraction, optimization and classification method for detection of premature ventricular contractions. In the first stage, we investigate the application of wavelet denoising in noise reduction of multi-channel high resolution ECG signals. In this stage, the Stationary Wavelet Transform is used. Feature extraction module extracts ten ECG morphological features and one timing interval feature. Then a number of radial basis function (RBF) neural networks with different value of spread parameter are designed and compared their ability for classification of three different classes of ECG signals. Genetic Algorithm is used to find best value of RBF parameters. A classification accuracy of 100% for training dataset and 95.66% for testing dataset and an overall accuracy of detection of 95.83% were achieved over seven files from the MIT/BIH arrhythmia database.