ECG Identification Based on Non-Fiducial Feature Extraction Using Window Removal Method
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 wande...
| Main Authors: | , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2017-11-01
|
| Series: | Applied Sciences |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2076-3417/7/11/1205 |
| _version_ | 1818133269481259008 |
|---|---|
| author | Woo-Hyuk Jung Sang-Goog Lee |
| author_facet | Woo-Hyuk Jung Sang-Goog Lee |
| author_sort | Woo-Hyuk Jung |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-12-11T08:50:02Z |
| format | Article |
| id | doaj.art-3c701e02117647dc9be43e93f33492d3 |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-12-11T08:50:02Z |
| publishDate | 2017-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-3c701e02117647dc9be43e93f33492d32022-12-22T01:14:02ZengMDPI AGApplied Sciences2076-34172017-11-01711120510.3390/app7111205app7111205ECG Identification Based on Non-Fiducial Feature Extraction Using Window Removal MethodWoo-Hyuk Jung0Sang-Goog Lee1Department of Media Engineering, The Catholic University of Korea, Bucheon-si 14992, KoreaDepartment of Media Technology and Contents, The Catholic University of Korea, Bucheon-si 14992, KoreaThis 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.https://www.mdpi.com/2076-3417/7/11/1205electrocardiogramECG identificationbiometricswindow removal methodnon-fiducial technique |
| spellingShingle | Woo-Hyuk Jung Sang-Goog Lee ECG Identification Based on Non-Fiducial Feature Extraction Using Window Removal Method Applied Sciences electrocardiogram ECG identification biometrics window removal method non-fiducial technique |
| title | ECG Identification Based on Non-Fiducial Feature Extraction Using Window Removal Method |
| title_full | ECG Identification Based on Non-Fiducial Feature Extraction Using Window Removal Method |
| title_fullStr | ECG Identification Based on Non-Fiducial Feature Extraction Using Window Removal Method |
| title_full_unstemmed | ECG Identification Based on Non-Fiducial Feature Extraction Using Window Removal Method |
| title_short | ECG Identification Based on Non-Fiducial Feature Extraction Using Window Removal Method |
| title_sort | ecg identification based on non fiducial feature extraction using window removal method |
| topic | electrocardiogram ECG identification biometrics window removal method non-fiducial technique |
| url | https://www.mdpi.com/2076-3417/7/11/1205 |
| work_keys_str_mv | AT woohyukjung ecgidentificationbasedonnonfiducialfeatureextractionusingwindowremovalmethod AT sanggooglee ecgidentificationbasedonnonfiducialfeatureextractionusingwindowremovalmethod |