Abstract
Recent studies show that some types of J wave syndromes could indicate high-risk of malignant arrhythmias and sudden cardiac death. It has been a critical problem to identify J wave accurately and therefore effectively avoid misdiagnoses in clinical applications. Out of this purpose, an automatic J wave detection method is proposed in this paper. This technique explored for J wave will definitely stand out with a huge database. Firstly, a training set which contains J wave and normal ECG signals is needed. Method of feature point location could be used for picking up J-wave segment from abnormal ECG signals and non-J-wave segment from normal ECG signals as well. Secondly, feature extraction is accomplished by curve fitting and wavelet transform for vectors of extracted segments. The feature vectors are used to train an active learning support vector machine (SVM) classifier. Finally, a test set is used to assess the trained SVM classifier. The evaluation system shows that J wave segment could be detected quickly and accurately, which can help cardiologists make reasonable diagnosis in clinical cases.
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Li, D., Liu, X., Zhao, J. (2015). An Approach for J Wave Auto-Detection Based on Support Vector Machine. In: Wang, Y., Xiong, H., Argamon, S., Li, X., Li, J. (eds) Big Data Computing and Communications. BigCom 2015. Lecture Notes in Computer Science(), vol 9196. Springer, Cham. https://doi.org/10.1007/978-3-319-22047-5_37
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DOI: https://doi.org/10.1007/978-3-319-22047-5_37
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