Abstract
The main objective of this paper is to investigate and propose a new approach to distinguish between two classes of beats from the ECG holter recordings - the premature ventricular beats (V) and the normal ones (N). The integrated methodology consists of a specific sequence: R-peak detection, feature extraction, Principal Component Analysis dimensionality reduction and classification with a neural classifier. ECG beats of holter recordings are described using means as simple as possible resulting in a description of the QRS complex by features derived mathematically from the signal using only R-peak detection. For this research work, normal (N) and ventricular (V) beats from the well known MIT-BIH database were used to test the proposed methodology. The results are promising paving the way for the more demanding multiclass classification problem.
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Chudacek, V., Georgoulas, G., Huptych, M., Stylios, C., Lhotska, L. (2009). Discriminating between V and N Beats from ECGs Introducing an Integrated Reduced Representation along with a Neural Network Classifier. In: Alippi, C., Polycarpou, M., Panayiotou, C., Ellinas, G. (eds) Artificial Neural Networks – ICANN 2009. ICANN 2009. Lecture Notes in Computer Science, vol 5769. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-04277-5_49
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DOI: https://doi.org/10.1007/978-3-642-04277-5_49
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