Sleep Stage Classification: A Deep Learning Approach

Sleep occupies significant part of human life. The diagnoses of sleep related disorders are of great importance. To record specific physical and electrical activities of the brain and body, a multi-parameter test, called polysomnography (PSG), is normally used. The visual process of sleep stage classification is time consuming, subjective and costly. To improve the accuracy and efficiency of the sleep stage classification, automatic classification algorithms were developed. In this research work, we focused on pre-processing (filtering boundaries and de-noising algorithms) and classification steps of automatic sleep stage classification. The main motivation for this work was to develop a pre-processing and classification framework to clean the input EEG signal without manipulating the original data thus enhancing the learning stage of deep learning classifiers. For pre-processing EEG signals, a lossless adaptive artefact removal method was proposed. Rather than other works that used artificial noise, we used real EEG data contaminated with EOG and EMG for evaluating the proposed method. The proposed adaptive algorithm led to a significant enhancement in the overall classification accuracy. In the classification area, we evaluated the performance of the most common sleep stage classifiers using a comprehensive set of features extracted from PSG signals. Considering the challenges and limitations of conventional methods, we proposed two deep learning-based methods for classification of sleep stages based on Stacked Sparse AutoEncoder (SSAE) and Convolutional Neural Network (CNN). The proposed methods performed more efficiently by eliminating the need for conventional feature selection and feature extraction steps respectively. Moreover, although our systems were trained with lower number of samples compared to the similar studies, they were able to achieve state of art accuracy and higher overall sensitivity

Feature ranking and rank aggregation for automatic sleep stage classification: a comparative study

Abstract Background Nowadays, sleep quality is one of the most important measures of healthy life, especially considering the huge number of sleep-related disorders. Identifying sleep stages using polysomnographic (PSG) signals is the traditional way of assessing sleep quality. However, the manual process of sleep stage classification is time-consuming, subjective and costly. Therefore, in order to improve the accuracy and efficiency of the sleep stage classification, researchers have been trying to develop automatic classification algorithms. Automatic sleep stage classification mainly consists of three steps: pre-processing, feature extraction and classification. Since classification accuracy is deeply affected by the extracted features, a poor feature vector will adversely affect the classifier and eventually lead to low classification accuracy. Therefore, special attention should be given to the feature extraction and selection process. Methods In this paper the performance of seven feature selection methods, as well as two feature rank aggregation methods, were compared. Pz-Oz EEG, horizontal EOG and submental chin EMG recordings of 22 healthy males and females were used. A comprehensive feature set including 49 features was extracted from these recordings. The extracted features are among the most common and effective features used in sleep stage classification from temporal, spectral, entropy-based and nonlinear categories. The feature selection methods were evaluated and compared using three criteria: classification accuracy, stability, and similarity. Results Simulation results show that MRMR-MID achieves the highest classification performance while Fisher method provides the most stable ranking. In our simulations, the performance of the aggregation methods was in the average level, although they are known to generate more stable results and better accuracy. Conclusions The Borda and RRA rank aggregation methods could not outperform significantly the conventional feature ranking methods. Among conventional methods, some of them slightly performed better than others, although the choice of a suitable technique is dependent on the computational complexity and accuracy requirements of the user

Investigating the contribution of distance-based features to automatic sleep stage classification

This work was partially funded by FCT Strategic Program UID/EEA/00066/203 of UNINOVA, CTS. Sem PDF conforme despacho.Objective: In this paper, the contribution of distance-based features to automatic sleep stage classification is investigated. The potency of these features is analyzed individually and in combination with 48 conventionally used features. Methods: The distance-based set consists of 32 features extracted by calculating Itakura, Itakura-Saito and COSH distances of autoregressive and spectral coefficients of Electrocardiography (EEG) (C3-A2), Left EOG, Chin EMG and ECG signals. All the evaluations are performed on three feature sets: distance-based, conventional and total (combined distance based and conventional). Six ranking methods were used to find the top features with the highest discrimination ability in each set. The ranked feature lists were evaluated using k-Nearest Neighbor (kNN), Artificial Neural Network (ANN), and Decision-tree-based multi-SVM (DSVM) classifiers for five sleep stages including Wake, REM, N1, N2 and N3. Furthermore, the ability of distance-based and conventional features to discriminate between each pair of sleep stages was evaluated using t-test, a hypothesis testing method. Results: Distance-based features occupied 25% of top-ranked features. Simulation results showed that using distance-based features together with conventional features can lead to an enhancement of accuracy. The best classification accuracy (85.5%) was achieved by DSVM classifier and 13 features selected by mRMR-MID and normalized with Min-Max method for total feature set, where two of them were from the distance-based feature set. The t-test results show that distance-based features outperform conventional features in discriminating between N1 and REM stages that is usually a challenge for classification systems. Conclusion: Distance-based features have a positive contribution to sleep stage classification, including enhancement of accuracy and better REM-N1 discrimination ability. Significance: The main motivation for this work was to evaluate new features to characterize each sleep stage in such a way that extracted features were more powerful than conventional features, to distinguish sleep stages from each other, and to improve classifiers accuracy.publishe