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Appl. Sci. 2017, 7(11), 1205; https://doi.org/10.3390/app7111205

ECG Identification Based on Non-Fiducial Feature Extraction Using Window Removal Method

1
Department of Media Engineering, The Catholic University of Korea, Bucheon-si 14992, Korea
2
Department of Media Technology and Contents, The Catholic University of Korea, Bucheon-si 14992, Korea
*
Author to whom correspondence should be addressed.
Received: 12 October 2017 / Revised: 10 November 2017 / Accepted: 20 November 2017 / Published: 22 November 2017
(This article belongs to the Special Issue Smart Environment and Healthcare)
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Abstract

This study proposes electrocardiogram (ECG) identification based on non-fiducial feature extraction using window removal method, nearest neighbor (NN), support vector machine (SVM), and linear discriminant analysis (LDA). In the pre-processing stage, Daubechies 4 is used to remove the baseline wander and noise of the original signal. In the feature extraction and selection stage, windows are set at a time interval of 5 s in the preprocessed signal, while autocorrelation, scaling, and discrete cosine transform (DCT) are applied to extract and select features. Thereafter, the window removal method is applied to all of the generated windows to remove those that are unrecognizable. Lastly, in the classification stage, the NN, SVM, and LDA classifiers are used to perform individual identification. As a result, when the NN is used in the Normal Sinus Rhythm (NSR), PTB diagnostic, and QT database, the results indicate that the subject identification rates are 100%, 99.40% and 100%, while the window identification rates are 99.02%, 97.13% and 98.91%. When the SVM is used, all of the subject identification rates are 100%, while the window identification rates are 96.92%, 95.82% and 98.32%. When the LDA is used, all of the subject identification rates are 100%, while the window identification rates are 98.67%, 98.65% and 99.23%. The proposed method demonstrates good results with regard to data that not only includes normal signals, but also abnormal signals. In addition, the window removal method improves the individual identification accuracy by removing windows that cannot be recognized. View Full-Text
Keywords: electrocardiogram; ECG identification; biometrics; window removal method; non-fiducial technique electrocardiogram; ECG identification; biometrics; window removal method; non-fiducial technique
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).
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Jung, W.-H.; Lee, S.-G. ECG Identification Based on Non-Fiducial Feature Extraction Using Window Removal Method. Appl. Sci. 2017, 7, 1205.

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