Automatic feature selection for sleep/wake classification with small data sets

This paper describes an automatic feature selection algorithm integrated into a classification framework developed to discriminate between sleep and wake states during the night. The feature selection algorithm proposed in this paper uses the Mahalanobis distance and the Spearman’s ranked-order correlation as selection criteria to restrict search in a large feature space. The algorithm was tested using a leave-one-subject-out cross-validation procedure on 15 single-night PSG recordings of healthy sleepers and then compared to the results of a standard Sequential Forward Search (SFS) algorithm. It achieved comparable performance in terms of Cohen’s kappa (k = 0.62) and the Area under the Precision-Recall curve (AUCPR = 0.59), but gave a significant computational time improvement by a factor of nearly 10. The feature selection procedure, applied on each iteration of the cross-validation, was found to be stable, consistently selecting a similar list of features. It selected an average of 10.33 features per iteration, nearly half of the 21 features selected by SFS. In addition, learning curves show that the training and testing performances converge faster than for SFS and that the final training-testing performance difference is smaller, suggesting that the new algorithm is more adequate for data sets with a small number of subjects

Automatic feature selection for sleep/wake classification with small data sets

This paper describes an automatic feature selection algorithm integrated into a classification framework developed to discriminate between sleep and wake states during the night. The feature selection algorithm proposed in this paper uses the Mahalanobis distance and the Spearman’s ranked-order correlation as selection criteria to restrict search in a large feature space. The algorithm was tested using a leave-one-subject-out cross-validation procedure on 15 single-night PSG recordings of healthy sleepers and then compared to the results of a standard Sequential Forward Search (SFS) algorithm. It achieved comparable performance in terms of Cohen’s kappa (k = 0.62) and the Area under the Precision-Recall curve (AUCPR = 0.59), but gave a significant computational time improvement by a factor of nearly 10. The feature selection procedure, applied on each iteration of the cross-validation, was found to be stable, consistently selecting a similar list of features. It selected an average of 10.33 features per iteration, nearly half of the 21 features selected by SFS. In addition, learning curves show that the training and testing performances converge faster than for SFS and that the final training-testing performance difference is smaller, suggesting that the new algorithm is more adequate for data sets with a small number of subjects

Automatic Feature Selection for Sleep / Wake Classification with Small Data Sets

This paper describes an automatic feature selection algorithm integrated into a classification framework developed to discriminate between sleep and wake states during the night. The feature selection algorithm proposed in this paper uses the Mahalanobis distance and the Spearman’s ranked-order correlation as selection criteria to restrict search in a large feature space. The algorithm was tested using a leave-one-subject-out cross-validation procedure on 15 single-night PSG recordings of healthy sleepers and then compared to the results of a standard Sequential Forward Search (SFS) algorithm. It achieved comparable performance in terms of Cohen’s kappa (k = 0.62) and the Area under the Precision-Recall curve (AUCPR = 0.59), but gave a significant computational time improvement by a factor of nearly 10. The feature selection procedure, applied on each iteration of the cross-validation, was found to be stable, consistently selecting a similar list of features. It selected an average of 10.33 features per iteration, nearly half of the 21 features selected by SFS. In addition, learning curves show that the training and testing performances converge faster than for SFS and that the final training-testing performance difference is smaller, suggesting that the new algorithm is more adequate for data sets with a small number of subjects

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 Extraction and Selection in Automatic Sleep Stage Classification

Sleep stage classification is vital for diagnosing many sleep related disorders and Polysomnography (PSG) is an important tool in this regard. The visual process of sleep stage classification is time consuming, subjective and costly. To improve the accuracy and efficiency of the sleep stage classification, researchers have been trying to develop automatic classification algorithms. The automatic sleep stage classification mainly consists of three steps: pre-processing, feature extraction and classification. In this research work, we focused on feature extraction and selection steps. The main goal of this thesis was identifying a robust and reliable feature set that can lead to efficient classification of sleep stages. For achieving this goal, three types of contributions were introduced in feature selection, feature extraction and feature vector quality enhancement. Several feature ranking and rank aggregation methods were evaluated and compared for finding the best feature set. Evaluation results indicated that the decision on the precise feature selection method depends on the system design requirements such as low computational complexity, high stability or high classification accuracy. In addition to conventional feature ranking methods, in this thesis, novel methods such as Stacked Sparse AutoEncoder (SSAE) was used for dimensionality reduction. In feature extration area, new and effective features such as distancebased features were utilized for the first time in sleep stage classification. The results showed that these features contribute positively to the classification performance. For signal quality enhancement, a loss-less EEG artefact removal algorithm was proposed. The proposed adaptive algorithm led to a significant enhancement in the overall classification accuracy