Automatic Estimation ofMacro-Sleep-Architecture using a Single channel ofEEG
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Scoring of Macro Sleep Architecture (MSA) is a critical process in assessing several sleep disorders. MSA is defined as classification of sleep into three major states of sleep, State Wake, State REM and State NREM. Existing methods of MSA analysis require the recording of six channels of electro physiological signals such as the EEG, EOG and EMG. They depend on the manual scoring of overnight data records using the R&K Criteria (1968), developed for visual analysis of signals based on morphological features. Manual analysis of MSA is tedious, subjective and suffers from both inter and intra scorer variability In addition to this due to dependency of MSA on several biological signals, makes it impossible to incorporate in portable apnea screening devices. Non-availability of MSA ham pers these devices accuracy making them non-acceptable among medical community. In this paper we propose a novel method for MSA analysis, which requires just one channel of only EEG data. We also develop a fully automated, objective MSA analysis technique, which uses a single one-dimensional slice of the Bisprectrum of EEG, representing a nonlinear transformation of a system function that can be considered as the EEG generator. The method was evaluated on an overnight clinical database of 23 patients. The results were compared with those obtained by an experienced human scorer. The method proposed in this paper led to agreements in the range of 70°.4-87%, comparable to that possible between two expert human scorers.Keywords:
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Sleep specialists often conduct manual sleep stage scoring by visually inspecting the patient’s neurophysiological signals collected at sleep labs. This is, generally, a very difficult, tedious and time-consuming task. The limitations of manual sleep stage scoring have escalated the demand for developing Automatic Sleep Stage Classification (ASSC) systems. Sleep stage classification refers to identifying the various stages of sleep and is a critical step in an effort to assist physicians in the diagnosis and treatment of related sleep disorders. The aim of this paper is to survey the progress and challenges in various existing Electroencephalogram (EEG) signal-based methods used for sleep stage identification at each phase; including pre-processing, feature extraction and classification; in an attempt to find the research gaps and possibly introduce a reasonable solution. Many of the prior and current related studies use multiple EEG channels, and are based on 30 s or 20 s epoch lengths which affect the feasibility and speed of ASSC for real-time applications. Thus, in this paper, we also present a novel and efficient technique that can be implemented in an embedded hardware device to identify sleep stages using new statistical features applied to 10 s epochs of single-channel EEG signals. In this study, the PhysioNet Sleep European Data Format (EDF) Database was used. The proposed methodology achieves an average classification sensitivity, specificity and accuracy of 89.06%, 98.61% and 93.13%, respectively, when the decision tree classifier is applied. Finally, our new method is compared with those in recently published studies, which reiterates the high classification accuracy performance.
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Automatic and real-time sleep scoring is necessary to develop user interfaces that trigger stimuli in specific sleep stages. However, most automatic sleep scoring systems have been focused on offline data analysis. We present the first, real-time sleep staging system that uses deep learning without the need for servers in a smartphone application for a wearable EEG. We employ real-time adaptation of a single channel Electroencephalography (EEG) to infer from a Time-Distributed Convolutional Neural Network (CNN). Polysomnography (PSG) -the gold standard for sleep staging-requires a human scorer and is both complex and resource-intensive. Our work demonstrates an end-to-end, smartphone-based pipeline that can infer sleep stages in just single 30-second epochs, with an overall accuracy of 83.5% on 20-fold cross validation for 5-stage classification of sleep stages using the open Sleep-EDF dataset. For comparison, inter-rater reliability among sleep-scoring experts is about 80% (Cohen's k=0\pmb.68 to \pmb0.76). We further propose an on-device metric independent of the deep learning model which increases the average accuracy of classifying deep-sleep (N3) to more than 97.2% on 4 test nights using power spectral analysis.
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Scoring of Macro Sleep Architecture (MSA) is a critical process in assessing several sleep disorders. MSA is defined as classification of sleep into three major states of sleep, State Wake, State REM and State NREM. Existing methods of MSA analysis require the recording of six channels of electrophysiological signals such as the EEG, EOG and EMG. They depend on the manual scoring of overnight data records using the R&K Criteria (1968), developed for visual analysis of signals based on morphological features. Manual analysis of MSA is tedious, subjective and suffers from both inter and intra scorer variability. In addition to this due to dependency of MSA on several biological signals, makes it impossible to incorporate in portable apnea screening devices. Non-availability of MSA hampers these devices accuracy making them non-acceptable among medical community. In this paper we propose a novel method for MSA analysis, which requires just one channel of only EEG data. We also develop a fully automated, objective MSA analysis technique, which uses a single one-dimensional slice of the Bisprectrum of EEG, representing a nonlinear transformation of a system function that can be considered as the EEG generator. The method was evaluated on an overnight clinical database of 23 patients. The results were compared with those obtained by an experienced human scorer. The method proposed in this paper led to agreements in the range of 70%-87%, comparable to that possible between two expert human scorers.
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Proper scoring of sleep stages may offer more intuitive clinical information for assessing the sleep health and improving the diagnosis of sleep disorders in the smart home healthcare. It usually depends on an accurate analysis of the collected physiological signals, especially for the raw sleep Electroencephalogram (EEG). Most of the methods currently available just rely on the pre-processing or handcrafted features that need prior knowledge and preliminary analysis from the sleep experts and only a few of them take full advantage of the temporal information such as the inter-epoch dependency or transition rules among stages, which are more effective for identifying the differences among the sleep stages. In such cases, we proposed a novel hybrid neural network named HNSleepNet. It utilizes a two-branch CNN with multi-scale convolution kernels to capture the time-invariant features from the adjacent sleep EEG epochs both in time and frequency domains automatically, and attention-based residual encoder-decoder LSTM layers to learn the inter-epoch dependency and transition rules at the Sequence-wise level. After the two-step training, HNSleepNet can perform sequence-to-sequence automatic sleep staging with a raw single channel EEG in an end-to-end way. As the experimental results demonstrated, its performance achieved a better overall accuracy and macro F1-score (MASS: 88%, 0.85, Sleep-EDF: 87%-80%, 0.79-0.74) compared with the state-of-the-art approaches on various single-channels (F4-EOG (Left), Fpz-Cz and Pz-Oz) in two public datasets with different scoring standards (AASM and R&K), We hope this progress can make clinically practical value in promoting home sleep studies on various home health-care devices.
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Background: Sleep disorders become one of the early warnings of Non-Communicable Diseases (NCDs).In sleep stage classification process one of the important stages to be sleep score recording. Generally the first step of diagnosis of sleep disorder is to be Polysomnography (PSG) test.The PSG test is a formal method to diagnose sleep disorders, during this test we have considered many biomedical signals such as electroencephalogram (EEG),electrooculogram (EOG) and electromyogram (EMG).Sleep Stage classification (SSC) process is time taking and there must be a presence of sleep experts or technicians definitely to be stay with subject through the whole recording time period, which is somehow overburden for clinicians and it may hamper sometime to record the correct results from subjects. For that reason now researchers obtained Automatic Sleep Stage Classification (ASSC) methods in order to find disturbances during sleep and it's quite faster and efficient in towards data recording accuracy from PSG signals.Method: In this study, we have proposed an alternative approach for sleep stage scoring by considering different age subjects from same gender with their optimal features. In this proposed study we have considered dual channels of EEG signals such as C4-A1 and O2-A1. In data pre-processing stage, the datasets were analyzed and normalized using feature extraction and feature selection methods. The main important part of this research work is to be comparing between dual channels and its accuracy of best discrimination in between wake and sleep stages. In addition, we have obtained three base classifier such as support vector machines (SVM), decision tree (DT) and K-nearest neighbors (KNN). In addition we have also adopted ensemble classifier (Boosting), to make a proper comparison the classification performances in between them. For validation purpose in between training data and test data, we have used 10-fold cross validation techniques.Results: In this study, we have made a comparison the performances in terms channel effectiveness and classification algorithm effectiveness with regard to discriminate in between the sleep stages. As per outcome from the proposed system the SVM classification techniques achieved best accuracy in comparable to other classifiers.Regading to channel effectiveness, C4-A1 recording is more appropriate for sleep stage scoring.Discussion and Conclusion: As per the related research work in this field , the introduced approach in the present study achieved an acceptable performance in sleep scoring in order to classifying wake stage and sleep stage from dual channels of EEG signals. Our experiment design compares the accuracy of classification in between two channels and find out which channel recordings and classification techniques to be most effective in towards classifying sleep stages. In future its performance to be increase through proper enhancing through different intelligent techniques in related to process of diagnosing and treatment of sleep disorders.
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Sleep staging is considered as an effective indicator for auxiliary diagnosis of sleep diseases and related psychiatric diseases, so it attracts a lot of attention from sleep researchers. Nevertheless, sleep staging based on visual inspection of tradition is subjective, time-consuming, and error-prone due to the large bulk of data which have to be processed. Therefore, automatic sleep staging is essential in order to solve these problems. In this article, an electroencephalogram- (EEG-) based scheme that is able to automatically classify sleep stages is proposed. Firstly, EEG data are preprocessed to remove artifacts, extract features, and normalization. Secondly, the normalized features and other context information are stored using an ontology-based model (OBM). Thirdly, an improved method of self-adaptive correlation analysis is designed to select the most effective EEG features. Based on these EEG features and weighting features analysis, the improved random forest (RF) is considered as the classifier to achieve the classification of sleep stages. To investigate the classification ability of the proposed method, several sets of experiments are designed and conducted to classify the sleep stages into two, three, four, and five states. The accuracy of five-state classification is 89.37%, which is improved compared to the accuracy using unimproved RF (84.37%) or previously reported classifiers. In addition, a set of controlled experiments is executed to verify the effect of the number of sleep segments (epochs) on the classification, and the results demonstrate that the proposed scheme is less affected by the sleep segments.
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Traditionally the analysis of sleep has used two distinct manual EEG analysis methods: one for general structure, the other for short time-scale events. Both methods suffer from high levels of inter-expert variability. In this paper we present a system which uses a neural network classifier to analyse each second of sleep. Postprocessing techniques are described which result in outputs which mimic both of the traditional manual analysis methods. This combination of methods results in a comprehensive sleep analysis system providing information on both the macro and microstructure of sleep. Our results show that it is possible to use a combined approach to sleep analysis and that there is strong correlation between expert scoring and the post-processed neural network output.
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