Development of Automated Sleep Stage Classification System Using Multivariate Projection-Based Fixed Boundary Empirical Wavelet Transform and Entropy Features Extracted from Multichannel EEG Signals

Abstract

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.

Assessment of Dispersion and Bubble Entropy Measures for Enhancing Preterm Birth Prediction Based on Electrohysterographic Signals

“…Both fuzzy entropy and spectral entropy were previously proposed to distinguish preterm and term records [ 17 ]. As far as we know, this is the first time EHG has been characterized using dispersion entropy and bubble entropy, which have also been used for quantifying the regularity of other biomedical signals [ 25 , 31 , 32 , 33 ].…”

“…Tripathy et al developed an automated sleep stage classification system, in which after some transformation of temporal EEG signals, they computed dispersion and bubble entropy. Their results could discriminate between different sleep stages with greater accuracy (>85%) [ 33 ].…”

Automatic Sleep-Stage Scoring in Healthy and Sleep Disorder Patients Using Optimal Wavelet Filter Bank Technique with EEG Signals

“…Timplalexis et al [ 23 ] carried out 5-class sleep stage classification using a combination of time- and frequency-based features and obtained an overall classification accuracy of 88.88% using an EBT classifier. Tripathi et al [ 24 ] used dispersion entropy and bubble entropy features and a hybrid classifier. They used only 25 subjects among which six were healthy (H), seven were insomniac (Ins), one brux patient, one sleep-disordered breathing (SDB) patient and 10 REM-behaviour disorder (RBD) patients from the CAP database.…”

Multichannel Multiscale Two-Stage Convolutional Neural Network for the Detection and Localization of Myocardial Infarction Using Vectorcardiogram Signal

“…The MVMD based multi-scale approach is used in this study to decompose the VCG signal. The other multi-scale analysis methods, such as multivariate empirical mode decomposition (MEMD) [40], multivariate projection based empirical wavelet transform (MPEWT) [41], and fast and adaptive based MEMD [42] can be used for the decomposition of VCG signals.…”