Abstract
In order to improve the classification results on electrocardiogram (ECG) signals, Optimal Discrimination Plane (ODP) approach is introduced. Features are extracted from time-series data using the ODP that is developed by Fisher’s criterion method. ECG patterns are projected onto two orthogonal vectors, and the two-dimensional feature vectors are used as features to represent the ECG segments. Two types of ECG signals are obtained from MIT-BIH database, namely normal sinus rhythm and premature ventricular contraction. A quadratic discriminant function based classifier and a threshold vector based classifier are employed to classify these ECG beats, respectively. The results show the proposed technique can achieve better classification results compared to that of some recently published on arrhythmia classification.
Supported by Zhejiang Province Natural Science Foundation, P. R. C. (Grant No. Y104284).
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Ge, D., Qu, X. (2006). Feature Extraction Based on Optimal Discrimination Plane in ECG Signal Classification. In: Li, X., Zaïane, O.R., Li, Z. (eds) Advanced Data Mining and Applications. ADMA 2006. Lecture Notes in Computer Science(), vol 4093. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11811305_15
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DOI: https://doi.org/10.1007/11811305_15
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-37025-3
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