An end-to-end framework for real-time automatic sleep stage classification.

Sleep staging is a fundamental but time consuming process in any sleep laboratory. To greatly speed up sleep staging without compromising accuracy, we developed a novel framework for performing real-time automatic sleep stage classification. The client-server architecture adopted here provides an end-to-end solution for anonymizing and efficiently transporting polysomnography data from the client to the server and for receiving sleep stages in an interoperable fashion. The framework intelligently partitions the sleep staging task between the client and server in a way that multiple low-end clients can work with one server, and can be deployed both locally as well as over the cloud. The framework was tested on four datasets comprising ≈1700 polysomnography records (≈12000 hr of recordings) collected from adolescents, young, and old adults, involving healthy persons as well as those with medical conditions. We used two independent validation datasets: one comprising patients from a sleep disorders clinic and the other incorporating patients with Parkinson's disease. Using this system, an entire night's sleep was staged with an accuracy on par with expert human scorers but much faster (≈5 s compared with 30-60 min). To illustrate the utility of such real-time sleep staging, we used it to facilitate the automatic delivery of acoustic stimuli at targeted phase of slow-sleep oscillations to enhance slow-wave sleep

Automatic Sleep Stages Classification

In this thesis, we first develop an efficient automated classification algorithm for sleep stages identification. Polysomnography recordings (PSGs) from twenty subjects were used in this study and features were extracted from the time{frequency representation of the electroencephalography (EEG) signal. The classification of the extracted features was done using random forest classifier. The performance of the new approach is tested by evaluating the accuracy of each sleep stages and total accuracy. The results shows improvement in all five sleep stages compared to previous works. Then, we present a simulation decision algorithm for switching between sleep interventions. This method improves the percentage of average amount of sleep in each stage. The results shows that sleep efficiency can be maximized by switching between intervention chains

Clasificación de las fases del sueño utilizando señales EEG

La identificación eficaz de las fases del sueño es de gran ayuda para el tratamiento de problemas del sueño como la apnea obstructiva (OSA), insomnio o narcolepsia. De esta manera, se puede mejorar la calidad de vida de los pacientes. La clasificación de estas fases pueden realizarla expertos del sueño de manera manual, basándose en señales PSG (Polisomnograma).No obstante, esto requiere mucho tiempo y para realizar una polisomnografía se necesitan muchas señales. Con un clasificador automáticobasado en señales EEGla detección sería más rápida y efectiva. En este trabajo se ha realizado una investigación de estudios ya realizados de la detección automática de las fases del sueño y se ha experimentadocon las señales EEG de 4sujetos sanos: se hanextraído un conjuntode características y se ha evaluadodel rendimiento de diferentes clasificadores. Con el clasificador KNN,7 características y 8 canales EEG, se han clasificado las fases del sueño con un F1 scoredel 51,41%. Como líneas de investigación futuras para mejorar los resultados se ha propuesto añadir característicasespectrales y reducir el número de canales, entre otras

EEG sleep stages identification based on weighted undirected complex networks

Sleep scoring is important in sleep research because any errors in the scoring of the patient's sleep electroencephalography (EEG) recordings can cause serious problems such as incorrect diagnosis, medication errors, and misinterpretations of patient's EEG recordings. The aim of this research is to develop a new automatic method for EEG sleep stages classification based on a statistical model and weighted brain networks. Methods each EEG segment is partitioned into a number of blocks using a sliding window technique. A set of statistical features are extracted from each block. As a result, a vector of features is obtained to represent each EEG segment. Then, the vector of features is mapped into a weighted undirected network. Different structural and spectral attributes of the networks are extracted and forwarded to a least square support vector machine (LS-SVM) classifier. At the same time the network's attributes are also thoroughly investigated. It is found that the network's characteristics vary with their sleep stages. Each sleep stage is best represented using the key features of their networks. Results In this paper, the proposed method is evaluated using two datasets acquired from different channels of EEG (Pz-Oz and C3-A2) according to the R&K and the AASM without pre-processing the original EEG data. The obtained results by the LS-SVM are compared with those by Naïve, k-nearest and a multi-class-SVM. The proposed method is also compared with other benchmark sleep stages classification methods. The comparison results demonstrate that the proposed method has an advantage in scoring sleep stages based on single channel EEG signals. Conclusions An average accuracy of 96.74% is obtained with the C3-A2 channel according to the AASM standard, and 96% with the Pz-Oz channel based on the R&K standard

Comparison of Different Features and Classifiers for Driver Fatigue Detection Based on a Single EEG Channel

Driver fatigue has become an important factor to traffic accidents worldwide, and effective detection of driver fatigue has major significance for public health. The purpose method employs entropy measures for feature extraction from a single electroencephalogram (EEG) channel. Four types of entropies measures, sample entropy (SE), fuzzy entropy (FE), approximate entropy (AE), and spectral entropy (PE), were deployed for the analysis of original EEG signal and compared by ten state-of-theart classifiers. Results indicate that optimal performance of single channel is achieved using a combination of channel CP4, feature FE, and classifier Random Forest (RF). The highest accuracy can be up to 96.6%, which has been able to meet the needs of real applications. The best combination of channel + features + classifier is subject-specific. In this work, the accuracy of FE as the feature is far greater than the Acc of other features. The accuracy using classifier RF is the best, while that of classifier SVM with linear kernel is the worst. The impact of channel selection on the Acc is larger. The performance of various channels is very different

Performance comparison of machine learning techniques in sleep scoring based on wavelet features and neighboring component analysis

Introduction: Sleep scoring is an important step in the treatment of sleep disorders. Manual annotation of sleep stages is time-consuming and experience-relevant and, therefore, needs to be done using machine learning techniques. Methods: Sleep-EDF polysomnography was used in this study as a dataset. Support vector machines and artificial neural network performance were compared in sleep scoring using wavelet tree features and neighborhood component analysis. Results: Neighboring component analysis as a combination of linear and non-linear feature selection method had a substantial role in feature dimension reduction. Artificial neural network and support vector machine achieved 90.30 and 89.93 accuracy, respectively. Discussion and Conclusion: Similar to the state of the art performance, the introduced method in the present study achieved an acceptable performance in sleep scoring. Furthermore, its performance can be enhanced using a technique combined with other techniques in feature generation and dimension reduction. It is hoped that, in the future, intelligent techniques can be used in the process of diagnosing and treating sleep disorders. © 2018 Alizadeh Savareh et al