A study on feature selection using multi-domain feature extraction for automated k-complex detection
BackgroundK-complex detection plays a significant role in the field of sleep research. However, manual annotation for electroencephalography (EEG) recordings by visual inspection from experts is time-consuming and subjective. Therefore, there is a necessity to implement automatic detection methods b...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2023-09-01
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| Series: | Frontiers in Neuroscience |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fnins.2023.1224784/full |
| _version_ | 1827822298164363264 |
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| author | Yabing Li Yabing Li Yabing Li Xinglong Dong Kun Song Xiangyun Bai Hongye Li Fakhreddine Karray |
| author_facet | Yabing Li Yabing Li Yabing Li Xinglong Dong Kun Song Xiangyun Bai Hongye Li Fakhreddine Karray |
| author_sort | Yabing Li |
| collection | DOAJ |
| description | BackgroundK-complex detection plays a significant role in the field of sleep research. However, manual annotation for electroencephalography (EEG) recordings by visual inspection from experts is time-consuming and subjective. Therefore, there is a necessity to implement automatic detection methods based on classical machine learning algorithms. However, due to the complexity of EEG signal, current feature extraction methods always produce low relevance to k-complex detection, which leads to a great performance loss for the detection. Hence, finding compact yet effective integrated feature vectors becomes a crucially core task in k-complex detection.MethodIn this paper, we first extract multi-domain features based on time, spectral analysis, and chaotic theory. Those features are extracted from a 0.5-s EEG segment, which is obtained using the sliding window technique. As a result, a vector containing twenty-two features is obtained to represent each segment. Next, we explore several feature selection methods and compare their performance in detecting k-complex. Based on the analysis of the selected features, we identify compact features which are fewer than twenty-two features and deemed as relevant and proceeded to the next step. Additionally, three classical classifiers are employed to evaluate the performance of the feature selection models.ResultsThe results demonstrate that combining different features significantly improved the k-complex detection performance. The best performance is achieved by applying the feature selection method, which results in an accuracy of 93.03%±7.34, sensitivity of 93.81%±5.62%, and specificity 94.13±5.81, respectively, using a smaller number of the combined feature sets.ConclusionThe proposed method in this study can serve as an efficient tool for the automatic detection of k-complex, which is useful for neurologists or doctors in the diagnosis of sleep research. |
| first_indexed | 2024-03-12T01:56:03Z |
| format | Article |
| id | doaj.art-5080a74ce35b47d49bab7ebe0f1e6e6d |
| institution | Directory Open Access Journal |
| issn | 1662-453X |
| language | English |
| last_indexed | 2024-03-12T01:56:03Z |
| publishDate | 2023-09-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Neuroscience |
| spelling | doaj.art-5080a74ce35b47d49bab7ebe0f1e6e6d2023-09-08T05:48:23ZengFrontiers Media S.A.Frontiers in Neuroscience1662-453X2023-09-011710.3389/fnins.2023.12247841224784A study on feature selection using multi-domain feature extraction for automated k-complex detectionYabing Li0Yabing Li1Yabing Li2Xinglong Dong3Kun Song4Xiangyun Bai5Hongye Li6Fakhreddine Karray7School of Computer Science and Technology, Xi’an University of Posts and Telecommunications, Xi’an, Shaanxi, ChinaShaanxi Key Laboratory of Network Data Analysis and Intelligent Processing, Xi’an, Shaanxi, ChinaXi’an Key Laboratory of Big Data and Intelligent Computing, Xi'an, Shaanxi, ChinaSchool of Computer Science and Technology, Xi’an University of Posts and Telecommunications, Xi’an, Shaanxi, ChinaMachine Learning Department, Mohamed bin Zayed University of Artificial Intelligence, Abu Dhabi, United Arab EmiratesSchool of Computer Science and Technology, Xi’an University of Posts and Telecommunications, Xi’an, Shaanxi, ChinaSchool of Computer Science and Technology, Xi’an University of Posts and Telecommunications, Xi’an, Shaanxi, ChinaMachine Learning Department, Mohamed bin Zayed University of Artificial Intelligence, Abu Dhabi, United Arab EmiratesBackgroundK-complex detection plays a significant role in the field of sleep research. However, manual annotation for electroencephalography (EEG) recordings by visual inspection from experts is time-consuming and subjective. Therefore, there is a necessity to implement automatic detection methods based on classical machine learning algorithms. However, due to the complexity of EEG signal, current feature extraction methods always produce low relevance to k-complex detection, which leads to a great performance loss for the detection. Hence, finding compact yet effective integrated feature vectors becomes a crucially core task in k-complex detection.MethodIn this paper, we first extract multi-domain features based on time, spectral analysis, and chaotic theory. Those features are extracted from a 0.5-s EEG segment, which is obtained using the sliding window technique. As a result, a vector containing twenty-two features is obtained to represent each segment. Next, we explore several feature selection methods and compare their performance in detecting k-complex. Based on the analysis of the selected features, we identify compact features which are fewer than twenty-two features and deemed as relevant and proceeded to the next step. Additionally, three classical classifiers are employed to evaluate the performance of the feature selection models.ResultsThe results demonstrate that combining different features significantly improved the k-complex detection performance. The best performance is achieved by applying the feature selection method, which results in an accuracy of 93.03%±7.34, sensitivity of 93.81%±5.62%, and specificity 94.13±5.81, respectively, using a smaller number of the combined feature sets.ConclusionThe proposed method in this study can serve as an efficient tool for the automatic detection of k-complex, which is useful for neurologists or doctors in the diagnosis of sleep research.https://www.frontiersin.org/articles/10.3389/fnins.2023.1224784/fullk-complexelectroencephalography (EEG)multi-domain featuresfeature selectiondetection |
| spellingShingle | Yabing Li Yabing Li Yabing Li Xinglong Dong Kun Song Xiangyun Bai Hongye Li Fakhreddine Karray A study on feature selection using multi-domain feature extraction for automated k-complex detection Frontiers in Neuroscience k-complex electroencephalography (EEG) multi-domain features feature selection detection |
| title | A study on feature selection using multi-domain feature extraction for automated k-complex detection |
| title_full | A study on feature selection using multi-domain feature extraction for automated k-complex detection |
| title_fullStr | A study on feature selection using multi-domain feature extraction for automated k-complex detection |
| title_full_unstemmed | A study on feature selection using multi-domain feature extraction for automated k-complex detection |
| title_short | A study on feature selection using multi-domain feature extraction for automated k-complex detection |
| title_sort | study on feature selection using multi domain feature extraction for automated k complex detection |
| topic | k-complex electroencephalography (EEG) multi-domain features feature selection detection |
| url | https://www.frontiersin.org/articles/10.3389/fnins.2023.1224784/full |
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