Fei Wang1,2, Li Li1, Yinxing Wan1, Zhuorong Li1, Lixian Luo3, Bangshun Hu1, Jiahui Pan1,2, Zhenfu Wen4, Haiyun Huang1,2,*
CMC-Computers, Materials & Continua, Vol.76, No.2, pp. 2301-2316, 2023, DOI:10.32604/cmc.2023.037727
Abstract Sleep spindles are an electroencephalogram (EEG) biomarker of non-rapid eye movement (NREM) sleep and have important implications for clinical diagnosis and prognosis. However, it is challenging to accurately detect sleep spindles due to the complexity of the human brain and the uncertainty of neural mechanisms. To improve the reliability and objectivity of sleep spindle detection and to compensate for the limitations of manual annotation, this study proposes a new automatic detection algorithm based on Matching Pursuit (MP) and Least Squares Boosting (LSBoost), where the automatic sleep spindle detection algorithm can help reduce the visual annotation workload of sleep clinicians. Specifically,… More >
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