An effective feature extraction method based on GDS for atrial fibrillation detection
[Display omitted] •An effective extraction method is proposed for atrial fibrillation detection in short-term ECG.•Obtain the gradient set (GDS) of equal-length ECG segments.•Extracting the statistical and information quantity features of the GDS.•No beat detection, no denoising and normalization, s...
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| Published in | Journal of biomedical informatics Vol. 119; p. 103819 |
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| Main Authors | , , , , , , |
| Format | Journal Article |
| Language | English |
| Published |
Elsevier Inc
01.07.2021
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| Subjects | |
| Online Access | Get full text |
| ISSN | 1532-0464 1532-0480 1532-0480 |
| DOI | 10.1016/j.jbi.2021.103819 |
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| Abstract | [Display omitted]
•An effective extraction method is proposed for atrial fibrillation detection in short-term ECG.•Obtain the gradient set (GDS) of equal-length ECG segments.•Extracting the statistical and information quantity features of the GDS.•No beat detection, no denoising and normalization, suitable for most classifiers.•A simple fully connected Deep Neural Network (DNN).
Atrial fibrillation (AF) is a common and extremely harmful arrhythmia disease. Automatic detection of AF based on ECG helps accurate and timely detection of the condition. However, the existing AF detection methods are mostly based on complex signal transformation or precise waveform localization. This is a big challenge for complex, variable, and susceptible ECG signals. Therefore, we propose a simple feature extraction method based on gradient set (GDS) for AF detection. The method first calculates the GDS of the ECG segment and then calculates the statistical distribution feature and the information quantity feature of the GDS as the input of the classifier. Experiments on four databases include 146 subjects show that the feature extraction method for detecting AF proposed in this paper has the characteristics of simple calculation, noise tolerance, and high adaptability to all kinds of classifiers, and got the best performance on the DNN classifier we designed. Therefore, it is a good choice for feature extraction in AF detection. |
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| AbstractList | Atrial fibrillation (AF) is a common and extremely harmful arrhythmia disease. Automatic detection of AF based on ECG helps accurate and timely detection of the condition. However, the existing AF detection methods are mostly based on complex signal transformation or precise waveform localization. This is a big challenge for complex, variable, and susceptible ECG signals. Therefore, we propose a simple feature extraction method based on gradient set (GDS) for AF detection. The method first calculates the GDS of the ECG segment and then calculates the statistical distribution feature and the information quantity feature of the GDS as the input of the classifier. Experiments on four databases include 146 subjects show that the feature extraction method for detecting AF proposed in this paper has the characteristics of simple calculation, noise tolerance, and high adaptability to all kinds of classifiers, and got the best performance on the DNN classifier we designed. Therefore, it is a good choice for feature extraction in AF detection.Atrial fibrillation (AF) is a common and extremely harmful arrhythmia disease. Automatic detection of AF based on ECG helps accurate and timely detection of the condition. However, the existing AF detection methods are mostly based on complex signal transformation or precise waveform localization. This is a big challenge for complex, variable, and susceptible ECG signals. Therefore, we propose a simple feature extraction method based on gradient set (GDS) for AF detection. The method first calculates the GDS of the ECG segment and then calculates the statistical distribution feature and the information quantity feature of the GDS as the input of the classifier. Experiments on four databases include 146 subjects show that the feature extraction method for detecting AF proposed in this paper has the characteristics of simple calculation, noise tolerance, and high adaptability to all kinds of classifiers, and got the best performance on the DNN classifier we designed. Therefore, it is a good choice for feature extraction in AF detection. [Display omitted] •An effective extraction method is proposed for atrial fibrillation detection in short-term ECG.•Obtain the gradient set (GDS) of equal-length ECG segments.•Extracting the statistical and information quantity features of the GDS.•No beat detection, no denoising and normalization, suitable for most classifiers.•A simple fully connected Deep Neural Network (DNN). Atrial fibrillation (AF) is a common and extremely harmful arrhythmia disease. Automatic detection of AF based on ECG helps accurate and timely detection of the condition. However, the existing AF detection methods are mostly based on complex signal transformation or precise waveform localization. This is a big challenge for complex, variable, and susceptible ECG signals. Therefore, we propose a simple feature extraction method based on gradient set (GDS) for AF detection. The method first calculates the GDS of the ECG segment and then calculates the statistical distribution feature and the information quantity feature of the GDS as the input of the classifier. Experiments on four databases include 146 subjects show that the feature extraction method for detecting AF proposed in this paper has the characteristics of simple calculation, noise tolerance, and high adaptability to all kinds of classifiers, and got the best performance on the DNN classifier we designed. Therefore, it is a good choice for feature extraction in AF detection. |
| ArticleNumber | 103819 |
| Author | Wang, Haiyan Wang, Zongmin Zhou, Yanjie Zhou, Bing Lu, Peng Dai, Honghua Zhang, Hongpo |
| Author_xml | – sequence: 1 givenname: Haiyan surname: Wang fullname: Wang, Haiyan organization: State Key Laboratory of Mathematical Engineering and Advanced Computing, Zhengzhou 450003, China – sequence: 2 givenname: Honghua surname: Dai fullname: Dai, Honghua organization: Collaborative Innovation Centre for Internet Healthcare, Zhengzhou University, Zhengzhou 450052, China – sequence: 3 givenname: Yanjie surname: Zhou fullname: Zhou, Yanjie email: ieyjzhou@zzu.edu.cn organization: School of Management Engineering, Zhengzhou University, Zhengzhou 450001, China – sequence: 4 givenname: Bing surname: Zhou fullname: Zhou, Bing organization: Collaborative Innovation Centre for Internet Healthcare, Zhengzhou University, Zhengzhou 450052, China – sequence: 5 givenname: Peng surname: Lu fullname: Lu, Peng organization: Collaborative Innovation Centre for Internet Healthcare, Zhengzhou University, Zhengzhou 450052, China – sequence: 6 givenname: Hongpo surname: Zhang fullname: Zhang, Hongpo organization: State Key Laboratory of Mathematical Engineering and Advanced Computing, Zhengzhou 450003, China – sequence: 7 givenname: Zongmin surname: Wang fullname: Wang, Zongmin organization: State Key Laboratory of Mathematical Engineering and Advanced Computing, Zhengzhou 450003, China |
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| Keywords | Information quantity features Feature extraction DNN Atrial fibrillation Gradient set Statistical distribution features |
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•An effective extraction method is proposed for atrial fibrillation detection in short-term ECG.•Obtain the gradient set (GDS) of... Atrial fibrillation (AF) is a common and extremely harmful arrhythmia disease. Automatic detection of AF based on ECG helps accurate and timely detection of... |
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| SubjectTerms | Atrial fibrillation DNN Feature extraction Gradient set Information quantity features Statistical distribution features |
| Title | An effective feature extraction method based on GDS for atrial fibrillation detection |
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