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The results indicated a lower reconstruction error using the center of the muscle synergy clusters in comparison with the average of the activation coefficients, which confirms the current research's hypothesis.

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In this paper a novel approach for cardiac arrhythmias detection is proposed. The proposed method is based on using independent component analysis (ICA) and wavelet transform to extract important features. Using the extracted features different machine learning classification schemas, MLP and RBF neural networks and K-nearest neighbor, are used to classify 274 instance signals from the MIT-BIH database. Simulations show that multilayer neural networks with Levenberg-Marquardt (LM) back propagation algorithm provide the optimal learning system. We were able to obtain 98.5% accuracy, which is an improvement in comparison with the similar works.

The goal of this article is to optimize the feature extraction process by using ICA and wavelet transform, apply the obtained set to several different machine learning schemes, and compare their performances. The article is structured as follows. Section 2.0 describes our proposed method for cardiac arrhythmias detection. Section 3.0 covers an overview of different classifier types that were used in this work. Sections 4.0 and 5.0 summarize our simulation scheme and results. Finally, section 6.0 presents the concluding remarks.