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Erschienen in:
2012 | OriginalPaper | Buchkapitel
Detection and Classification of ECG Chaotic Components Using ANN Trained by Specially Simulated Data
verfasst von : Polina Kurtser, Ofer Levi, Vladimir Gontar
Erschienen in: Engineering Applications of Neural Networks
Verlag: Springer Berlin Heidelberg
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This paper presents the use of simulated ECG signals with known chaotic and random noise combination for training of an Artificial Neural Network (ANN) as a classification tool for analysis of chaotic ECG components. Preliminary results show about 85% overall accuracy in the ability to classify signals into two types of chaotic maps – logistic and Henon. Robustness to random noise is also presented. Future research in the form of raw data analysis is proposed, and further features analysis is needed.