Department of EEE, Birla Institute of Technology, Mesra, Ranchi, India.
Department of EEE, Birla Institute of Technology, Offshore Campus, Ras Al Khaimah, UAE.
Med Biol Eng Comput. 2021 Jan;59(1):23-39. doi: 10.1007/s11517-020-02278-7. Epub 2020 Nov 14.
Nowadays, sleep disorders are contemplated as the major issue in the human lives. The current work aims at extraction of time-frequency information from recorded dataset and provides an efficient sleep stage detection method. Recordings of brain signal namely electroencephalogram (EEG), electrooculogram (EOG), and electromyogram (EMG) were carried out under defined clinical condition for the classification of sleep EEG. Subsequent upon the extraction of various features from the raw EEG data, neuro-fuzzy system is trained to classify the sleep stages into three major classes namely awake, slow wave sleep (SWS), and rapid eye movement sleep (REM). This classification would enable medical professionals to diagnose sleep related disorders accurately. The results obtained clearly indicate that the mean performance for SWS stage is profound as compared to REM and awake stage. Specificity and sensitivity of the proposed method are obtained as 95.4% and 80%, respectively. The average accuracy of the system employing neuro-fuzzy approach is found to be 90.6% in which SWS stage was best detected among the other stages of sleep EEG.Graphical abstract.
如今，睡眠障碍被认为是人类生活中的主要问题。本工作旨在从记录的数据集提取时频信息，并提供一种有效的睡眠阶段检测方法。在定义的临床条件下记录脑信号，即脑电图（EEG）、眼电图（EOG）和肌电图（EMG），以对睡眠 EEG 进行分类。在从原始 EEG 数据中提取各种特征之后，使用神经模糊系统对睡眠阶段进行分类，分为三个主要类别：清醒、慢波睡眠（SWS）和快速眼动睡眠（REM）。这种分类将使医疗专业人员能够准确诊断与睡眠相关的障碍。结果清楚地表明，与 REM 和清醒阶段相比，SWS 阶段的平均性能更为显著。所提出方法的特异性和敏感性分别为 95.4%和 80%。采用神经模糊方法的系统的平均准确性为 90.6%，其中 SWS 阶段在睡眠 EEG 的其他阶段中检测效果最佳。
