Analysis of the electromyogram of rapid eye movement sleep using wavelet techniques

Non-negative matrix factorization and sparse representation for sleep signal classification

Real-life signals such as biomedical signals are non-stationary and random in their pattern, and cannot be characterized by any specific waveform or spectral content. Processing of these natural signals involves consideration of certain significant attributes such as their non-stationary behavior over time, scaling behavior, translation invariance. Due to their random behavior, the existing discriminative methods often fail to provide a reasonable quantification performance, thereby resulting in poor classification rates. In order to address this issue, there exists a need for defining a suitable theoretical framework for biomedical signals. We have proposed, a robust Time-Frequency Nonnegative Matrix Factorization (TF-NMF) framework that uses sparse representation for quantification of sleep signals. This scheme incorporates a novel feature extraction algorithm. For signals that are nonstationary in nature, the degree of sparsity is lower compared to the stationary signals. This results into poor classification accuracy. However our proposed approach has proven that using NMF as input to the sparse representation for classification will improve the discrimination performance. Overall, maximum cross-validation performance of 87:9% was obtained, using the leave-one-out (LOO) approach for sleep abnormality detection using EMG signals. Although the computational complexity of the proposed algorithm might be higher compared to the other similar methods, this TF-NMF based method shows great potential for quantification and localization of time varying signals.

Automatic detection of REM sleep in subjects without atonia

Idiopathic Rapid-Rye-Movement (REM) sleep Behavior Disorder (iRBD) is a strong early marker of Parkinson's Disease and is characterized by REM sleep without atonia (RSWA) and increased phasic muscle activity. Current proposed methods for detecting RSWA assume the presence of a manually scored hypnogram. In this study a full automatic REM sleep detector, using the EOG and EEG channels, is proposed. Based on statistical features, combined with subject specific feature scaling and post-processing of the classifier output, it was possible to obtain an mean accuracy of 0.96 with a mean sensitivity and specificity of 0.94 and 0.96 respectively.

Application of empirical mode decomposition and Teager energy operator to EEG signals for mental task classification

This paper presents a novel method for mental task classification from EEG signals using Empirical Mode Decomposition and Teager energy operator techniques on EEG data. The efficacy of these techniques for non-stationary and non-linear data has already been demonstrated, which therefore lend themselves well to EEG signals, which are also non-stationary and non-linear in nature. The method described in this paper decomposed the EEG signals (6 EEG signals per task per subject, for a total of 5 tasks over multiple trials) into their constituent oscillatory modes, called intrinsic mode functions, and separated out the trend from the signal. Teager energy operator was used to calculate the average energy of both the detrended signal and the trend. The average energy was used to construct separate feature vectors with a small number of parameters for the detrended signal and the trend. Based on these parameters, one-versus-one classification of mental tasks was performed using Linear Discriminant Analysis. Using both feature vectors, an average correct classification rate of more than 85% was achieved, demonstrating the effectiveness of the method used. Furthermore, this method used all the intrinsic mode functions, as opposed to similar studies, demonstrating that the trend of the EEG signal also contains important discriminatory information.