Hierarchical fusion detection algorithm for sleep spindle detection

• Published in: Frontiers in neuroscience Vol. 17; p. 1105696
• Main Authors: Chen, Chao, Meng, Jiayuan, Belkacem, Abdelkader Nasreddine, Lu, Lin, Liu, Fengyue, Yi, Weibo, Li, Penghai, Liang, Jun, Huang, Zhaoyang, Ming, Dong
• Format: Journal Article
• Language: English
• Published: Switzerland Frontiers Research Foundation 09.03.2023; Frontiers Media S.A
• Subjects: Accuracy; Activity patterns; Age; Algorithms; Alzheimer's disease; Brain research; Data collection; EEG; Electrodes; Electroencephalography; Experiments; hierarchical fusion detection algorithm; Methods; Morlet wavelet; Neuroscience; Sleep; Sleep disorders; sleep spindle detection; Support vector machines; SVM; Wavelet transforms; United States > US; Pittsburgh Pennsylvania; sleep spindle detection; SVM; Morlet wavelet; EEG; hierarchical fusion detection algorithm
• ISSN: 1662-453X; 1662-4548; 1662-453X
• DOI: 10.3389/fnins.2023.1105696

Sleep 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. To 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. The 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. A 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.