Hierarchical fusion detection algorithm for sleep spindle detection
BackgroundSleep spindles are a vital sign implying that human beings have entered the second stage of sleep. In addition, they can effectively reflect a person’s learning and memory ability, and clinical research has shown that their quantity and density are crucial markers of brain function. The “g...
| Main Authors: | , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2023-03-01
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| Series: | Frontiers in Neuroscience |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/fnins.2023.1105696/full |
| _version_ | 1827994291606126592 |
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| author | Chao Chen Chao Chen Jiayuan Meng Abdelkader Nasreddine Belkacem Lin Lu Fengyue Liu Weibo Yi Penghai Li Jun Liang Zhaoyang Huang Zhaoyang Huang Dong Ming |
| author_facet | Chao Chen Chao Chen Jiayuan Meng Abdelkader Nasreddine Belkacem Lin Lu Fengyue Liu Weibo Yi Penghai Li Jun Liang Zhaoyang Huang Zhaoyang Huang Dong Ming |
| author_sort | Chao Chen |
| collection | DOAJ |
| description | BackgroundSleep spindles are a vital sign implying that human beings have entered the second stage of sleep. In addition, they can effectively reflect a person’s learning and memory ability, and clinical research has shown that their quantity and density are crucial markers of brain function. The “gold standard” of spindle detection is based on expert experience; however, the detection cost is high, and the detection time is long. Additionally, the accuracy of detection is influenced by subjectivity.MethodsTo improve detection accuracy and speed, reduce the cost, and improve efficiency, this paper proposes a layered spindle detection algorithm. The first layer used the Morlet wavelet and RMS method to detect spindles, and the second layer employed an improved k-means algorithm to improve spindle detection efficiency. The fusion algorithm was compared with other spindle detection algorithms to prove its effectiveness.ResultsThe hierarchical fusion spindle detection algorithm showed good performance stability, and the fluctuation range of detection accuracy was minimal. The average value of precision was 91.6%, at least five percentage points higher than other methods. The average value of recall could reach 89.1%, and the average value of specificity was close to 95%. The mean values of accuracy and F1-score in the subject sample data were 90.4 and 90.3%, respectively. Compared with other methods, the method proposed in this paper achieved significant improvement in terms of precision, recall, specificity, accuracy, and F1-score.ConclusionA spindle detection method with high steady-state accuracy and fast detection speed is proposed, which combines the Morlet wavelet with window RMS and an improved k-means algorithm. This method provides a powerful tool for the automatic detection of spindles and improves the efficiency of spindle detection. Through simulation experiments, the sampled data were analyzed and verified to prove the feasibility and effectiveness of this method. |
| first_indexed | 2024-04-10T04:37:37Z |
| format | Article |
| id | doaj.art-92602531d687478a9cec1e24f845d763 |
| institution | Directory Open Access Journal |
| issn | 1662-453X |
| language | English |
| last_indexed | 2024-04-10T04:37:37Z |
| publishDate | 2023-03-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Neuroscience |
| spelling | doaj.art-92602531d687478a9cec1e24f845d7632023-03-09T13:39:37ZengFrontiers Media S.A.Frontiers in Neuroscience1662-453X2023-03-011710.3389/fnins.2023.11056961105696Hierarchical fusion detection algorithm for sleep spindle detectionChao Chen0Chao Chen1Jiayuan Meng2Abdelkader Nasreddine Belkacem3Lin Lu4Fengyue Liu5Weibo Yi6Penghai Li7Jun Liang8Zhaoyang Huang9Zhaoyang Huang10Dong Ming11Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin, ChinaKey Laboratory of Complex System Control Theory and Application, Tianjin University of Technology, Tianjin, ChinaAcademy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin, ChinaDepartment of Computer and Network Engineering, College of Information Technology, United Arab Emirates University, Al Ain, United Arab EmiratesZhonghuan Information College Tianjin University of Technology, Tianjin, ChinaKey Laboratory of Complex System Control Theory and Application, Tianjin University of Technology, Tianjin, ChinaBeijing Machine and Equipment Institute, Beijing, ChinaKey Laboratory of Complex System Control Theory and Application, Tianjin University of Technology, Tianjin, ChinaDepartment of Rehabilitation, Tianjin Medical University General Hospital, Tianjin, ChinaDepartment of Neurology, Xuanwu Hospital, Capital Medical University, Beijing, ChinaBeijing Key Laboratory of Neuromodulation, Beijing, ChinaAcademy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin, ChinaBackgroundSleep spindles are a vital sign implying that human beings have entered the second stage of sleep. In addition, they can effectively reflect a person’s learning and memory ability, and clinical research has shown that their quantity and density are crucial markers of brain function. The “gold standard” of spindle detection is based on expert experience; however, the detection cost is high, and the detection time is long. Additionally, the accuracy of detection is influenced by subjectivity.MethodsTo improve detection accuracy and speed, reduce the cost, and improve efficiency, this paper proposes a layered spindle detection algorithm. The first layer used the Morlet wavelet and RMS method to detect spindles, and the second layer employed an improved k-means algorithm to improve spindle detection efficiency. The fusion algorithm was compared with other spindle detection algorithms to prove its effectiveness.ResultsThe hierarchical fusion spindle detection algorithm showed good performance stability, and the fluctuation range of detection accuracy was minimal. The average value of precision was 91.6%, at least five percentage points higher than other methods. The average value of recall could reach 89.1%, and the average value of specificity was close to 95%. The mean values of accuracy and F1-score in the subject sample data were 90.4 and 90.3%, respectively. Compared with other methods, the method proposed in this paper achieved significant improvement in terms of precision, recall, specificity, accuracy, and F1-score.ConclusionA spindle detection method with high steady-state accuracy and fast detection speed is proposed, which combines the Morlet wavelet with window RMS and an improved k-means algorithm. This method provides a powerful tool for the automatic detection of spindles and improves the efficiency of spindle detection. Through simulation experiments, the sampled data were analyzed and verified to prove the feasibility and effectiveness of this method.https://www.frontiersin.org/articles/10.3389/fnins.2023.1105696/fullsleep spindle detectionhierarchical fusion detection algorithmEEGMorlet waveletSVM |
| spellingShingle | Chao Chen Chao Chen Jiayuan Meng Abdelkader Nasreddine Belkacem Lin Lu Fengyue Liu Weibo Yi Penghai Li Jun Liang Zhaoyang Huang Zhaoyang Huang Dong Ming Hierarchical fusion detection algorithm for sleep spindle detection Frontiers in Neuroscience sleep spindle detection hierarchical fusion detection algorithm EEG Morlet wavelet SVM |
| title | Hierarchical fusion detection algorithm for sleep spindle detection |
| title_full | Hierarchical fusion detection algorithm for sleep spindle detection |
| title_fullStr | Hierarchical fusion detection algorithm for sleep spindle detection |
| title_full_unstemmed | Hierarchical fusion detection algorithm for sleep spindle detection |
| title_short | Hierarchical fusion detection algorithm for sleep spindle detection |
| title_sort | hierarchical fusion detection algorithm for sleep spindle detection |
| topic | sleep spindle detection hierarchical fusion detection algorithm EEG Morlet wavelet SVM |
| url | https://www.frontiersin.org/articles/10.3389/fnins.2023.1105696/full |
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