Predicting cardiac arrhythmia on ECG signal using an ensemble of optimal multicore support vector machines

The use of artificial intelligence in the process of diagnosing heart disease has been considered by researchers for many years. In this paper, an efficient method for selecting appropriate features extracted from electrocardiogram (ECG) signals, based on a genetic algorithm for use in an ensemble multi-kernel support vector machine classifiers, each of which is based on an optimized genetic algorithm is proposed. It has already been shown that due to its features (feature space mapping and decision boundary maximization), support vector machine classification is one of the classification methods that are suitable for any type of environment. This paper uses a number of multi-kernel support vector machine classifiers as an ensemble classifier. ensemble diversity is created by teaching each multi-kernel support vector machine classifier on a subspace (ie, a subset of features). In this method, the majority vote method is used to combine the output of the categories. On the other hand, in the classification of ECG signals, signals are usually used as their characteristics; As a result, since the methods of classifying signals are faced with a large number of features, and not removing these features creates a problem of high dimensions and also increases the computational for the intended application, the step of selecting the feature is inevitable. The extracted features include temporal properties, AR, and wavelet coefficients, the number of which will be optimized using a genetic algorithm. The evaluation of this set of features selected by the genetic algorithm is examined by applying it to a multivariate SVM. A genetic algorithm is used to optimize the parameters of each of the SVMs. Indicates the desired method. With the help of computer simulation, the overall accuracy of the system for identifying 6 types of heart rhythms is 99.15%, which in comparison with the accuracy obtained with previous research, shows the optimal performance of the proposed method.