华中科技大学学报（自然科学版）2024，Vol.52Issue(5)：64-69,6.DOI:10.13245/j.hust.240370
基于多特征信息的双层睡眠分期模型
Dual-layer sleep stage classification model based on multiple features
摘要
Abstract
Aiming at the issue of sleep staging in diagnosis and treatment of sleep-related disorders,the ADASYN algorithm was used to balance the sleep samples,and a series of features reflecting different sleep stages,such as composite multiscale permutation entropy,sample entropy,and energy values in different frequency bands was effectively utilized for training.To enhance the recognition performance of easily confused sleep stages in classification,a dual-layer classification model was constructed,transforming the five-class sleep staging problem into two three-class problems.The proposed method was validated on the Sleep-EDF dataset.Results show that the proposed model achieves an accuracy of 88.3%for healthy subjects,which is an improvement of 1%～3%compared to previous models,with a classification accuracy of N1 stage reaching 69.5%,which is an increase of about 10%compared to previous models,so it is proved that the proposed dual-layer classification model outperforms traditional sleep classification models.关键词
睡眠分期/脑电信号/特征提取/复合多尺度排列熵/双层分类模型Key words
sleep stage classification/electroencephalogram/feature extraction/composite multiscale permutation entropy/dual-layer classification model分类
医药卫生引用本文复制引用
张虹淼,王梓,廖肖剑,李伟达..基于多特征信息的双层睡眠分期模型[J].华中科技大学学报（自然科学版）,2024,52(5):64-69,6.基金项目
广东省重点领域研发资助项目(2019B090915002). （2019B090915002）
