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Development of Automated Sleep Stage Classification System Using Multivariate Projection-Based Fixed Boundary Empirical Wavelet Transform and Entropy Features Extracted from Multichannel EEG Signals
Entropy ( IF 2.7 ) Pub Date : 2020-10-09 , DOI: 10.3390/e22101141
Rajesh Kumar Tripathy , Samit Kumar Ghosh , Pranjali Gajbhiye , U. Rajendra Acharya

The categorization of sleep stages helps to diagnose different sleep-related ailments. In this paper, an entropy-based information–theoretic approach is introduced for the automated categorization of sleep stages using multi-channel electroencephalogram (EEG) signals. This approach comprises of three stages. First, the decomposition of multi-channel EEG signals into sub-band signals or modes is performed using a novel multivariate projection-based fixed boundary empirical wavelet transform (MPFBEWT) filter bank. Second, entropy features such as bubble and dispersion entropies are computed from the modes of multi-channel EEG signals. Third, a hybrid learning classifier based on class-specific residuals using sparse representation and distances from nearest neighbors is used to categorize sleep stages automatically using entropy-based features computed from MPFBEWT domain modes of multi-channel EEG signals. The proposed approach is evaluated using the multi-channel EEG signals obtained from the cyclic alternating pattern (CAP) sleep database. Our results reveal that the proposed sleep staging approach has obtained accuracies of 91.77%, 88.14%, 80.13%, and 73.88% for the automated categorization of wake vs. sleep, wake vs. rapid eye movement (REM) vs. Non-REM, wake vs. light sleep vs. deep sleep vs. REM sleep, and wake vs. S1-sleep vs. S2-sleep vs. S3-sleep vs. REM sleep schemes, respectively. The developed method has obtained the highest overall accuracy compared to the state-of-art approaches and is ready to be tested with more subjects before clinical application.

中文翻译:

使用基于多变量投影的固定边界经验小波变换和从多通道 EEG 信号中提取的熵特征开发自动睡眠阶段分类系统

睡眠阶段的分类有助于诊断不同的睡眠相关疾病。在本文中,引入了一种基于熵的信息理论方法,用于使用多通道脑电图 (EEG) 信号对睡眠阶段进行自动分类。这种方法包括三个阶段。首先,使用新的基于多元投影的固定边界经验小波变换 (MPFBEWT) 滤波器组将多通道 EEG 信号分解为子带信号或模式。其次,根据多通道 EEG 信号的模式计算气泡和分散熵等熵特征。第三,基于类特定残差的混合学习分类器使用稀疏表示和与最近邻居的距离,使用从多通道 EEG 信号的 MPFBEWT 域模式计算的基于熵的特征自动对睡眠阶段进行分类。所提出的方法是使用从循环交替模式 (CAP) 睡眠数据库中获得的多通道 EEG 信号进行评估的。我们的结果表明,对于清醒与睡眠、清醒与快速眼动 (REM) 与非 RE​​M 的自动分类,所提出的睡眠分期方法的准确度分别为 91.77%、88.14%、80.13% 和 73.88%,分别是唤醒 vs. 轻度睡眠 vs. 深度睡眠 vs. REM 睡眠,唤醒 vs. S1-sleep vs. S2-sleep vs. S3-sleep vs. REM 睡眠方案。
更新日期:2020-10-09
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