scholarly journals 0441 An Automatic Sleep Scoring System Based on Ensemble Convolutional Neural Network and Spectrogram of Sleep Physiological Signal

SLEEP ◽  
2020 ◽  
Vol 43 (Supplement_1) ◽  
pp. A169-A169
Author(s):  
C Kuo ◽  
G Chen
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Scoring System ◽  
Sleep Stage ◽  
Sleep Stages ◽  
Neural Network Ensemble ◽  
Sleep Scoring ◽  
Physiological Signal ◽  
Time Required ◽  
Sleep Stage Scoring

Abstract Introduction Manual sleep stage scoring is time consuming and subjective. Therefore, several studies focused on developing automated sleep scoring algorithms. The previously reported the automatic sleep scoring have been develop usually using small dataset, which less than 100 subjects. In this study, an automatic sleep scoring system based on ensemble convolutional neural network (ensemble-CNN) and spectrogram of sleep physiological signal was proposed and evaluated using a large dataset with sleep disorder. Methods The spectrograms were computed from each 30-s EEG and EOG of 994 subjects from PhysioNet 2018 challenge dataset, using the continuous wavelet transform, which were fed into an ensemble-CNN classification for training. The ensemble-CNN contained five pretrained models, ResNet-101, Inception-v4, DenseNet-201, Xception, and NASNet models, because these models’ architectures are different which can learn different features from the spectrograms to obtain high accuracy. The probabilities of five models were averaged to decide the sleep stage for each spectrogram. After classifying sleep stage, a smoothing process was used for sleep continuity. Moreover, the total 80% data from PhysioNet dataset were randomly assigned to the training set, and the remaining data were assigned to the testing set. Results To validate the robustness of the proposed system, the validation procedure was repeated five times. The performance measures were averaged over the five runs. The overall agreement and kappa coefficient of the proposed method are 82% and 0.73, respectively. The sensitivity of the sleep stages of Wake, N1, N2, N3, and REM are 90.0%, 48.6%, 84.9%, 84.2%, and 81.9%, respectively. Conclusion The performance of the proposed method was achieved expert level, and it was noted that the ensemble-CNN is a promising solution for automatic sleep stage scoring. This method can assist clinical staff in reducing the time required for sleep stage scoring in the future. Support This work was supported by the Ministry of Science and Technology, Taiwan. (MOST 106-2218-E-035-013-MY2, 108-2221-E-035-064, and 108-2634-F-006-012).

Automatic sleep stage classification of single-channel EEG by using complex-valued convolutional neural network

2018 ◽  
Vol 63 (2) ◽  
pp. 177-190 ◽  
Author(s):  
Junming Zhang ◽  
Yan Wu
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Single Channel ◽  
Sleep Stage ◽  
Experimental Results ◽  
Sleep Stages ◽  
Stage Classification ◽  
Sleep Stage Classification ◽  
Complex Valued ◽  
Learned Features

AbstractMany systems are developed for automatic sleep stage classification. However, nearly all models are based on handcrafted features. Because of the large feature space, there are so many features that feature selection should be used. Meanwhile, designing handcrafted features is a difficult and time-consuming task because the feature designing needs domain knowledge of experienced experts. Results vary when different sets of features are chosen to identify sleep stages. Additionally, many features that we may be unaware of exist. However, these features may be important for sleep stage classification. Therefore, a new sleep stage classification system, which is based on the complex-valued convolutional neural network (CCNN), is proposed in this study. Unlike the existing sleep stage methods, our method can automatically extract features from raw electroencephalography data and then classify sleep stage based on the learned features. Additionally, we also prove that the decision boundaries for the real and imaginary parts of a complex-valued convolutional neuron intersect orthogonally. The classification performances of handcrafted features are compared with those of learned features via CCNN. Experimental results show that the proposed method is comparable to the existing methods. CCNN obtains a better classification performance and considerably faster convergence speed than convolutional neural network. Experimental results also show that the proposed method is a useful decision-support tool for automatic sleep stage classification.

scholarly journals Automatic Sleep Stage Classification Using 1D Convolutional Neural Network

2020 ◽  
Author(s):  
Asma Salamatian ◽  
Ali Khadem
Keyword(s):  
Neural Network ◽  
Feature Extraction ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Single Channel ◽  
Sleep Stage ◽  
Sleep Stages ◽  
Eeg Signal ◽  
Stage Classification ◽  
Sleep Stage Classification

Purpose: Sleep is one of the necessities of the body, such as eating, drinking, etc., that affects different aspects of human life. Sleep monitoring and sleep stage classification play an important role in the diagnosis of sleeprelated diseases and neurological disorders. Empirically, classification of sleep stages is a time-consuming, tedious, and complex task, which heavily depends on the experience of the experts. As a result, there is a crucial need for an automatic efficient sleep staging system. Materials and Methods: This study develops a 13-layer 1D Convolutional Neural Network (CNN) using singlechannel Electroencephalogram (EEG) signal for extracting features automatically and classifying the sleep stages. To overcome the negative effect of an imbalance dataset, we have used the Synthetic Minority Oversampling Technique (SMOTE). In our study, the single-channel EEG signal is given to a 1D CNN, without any feature extraction/selection processes. This deep network can self-learn the discriminative features from the EEG signal. Results: Applying the proposed method to sleep-EDF dataset resulted in overall accuracy, sensitivity, specificity, and Precision of 94.09%, 74.73%, 96.43%, and 71.02%, respectively, for classifying five sleep stages. Using single-channel EEG and providing a network with fewer trainable parameters than most of the available deep learning-based methods are the main advantages of the proposed method. Conclusion: In this study, a 13-layer 1D CNN model was proposed for sleep stage classification. This model has an end-to-end complete architecture and does not require any separate feature extraction/selection and classification stages. Having a low number of network parameters and layers while still having high classification accuracy, is the main advantage of the proposed method over most of the previous deep learning-based approaches.

scholarly journals A multiplex visibility graph motif‐based convolutional neural network for characterizing sleep stages using EEG signals

2020 ◽  
Vol 6 (4) ◽  
pp. 355-363
Author(s):  
Qing Cai ◽  
Jianpeng An ◽  
Zhongke Gao
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Sleep Stage ◽  
Network Motif ◽  
Visibility Graph ◽  
Sleep Stages ◽  
Dynamic Features ◽  
Eeg Signals ◽  
Graph Motif ◽  
Electroencephalogram Eeg

Sleep is an essential integrant in everyone’s daily life; therefore, it is an important but challenging problem to characterize sleep stages from electroencephalogram (EEG) signals. The network motif has been developed as a useful tool to investigate complex networks. In this study, we developed a multiplex visibility graph motif‐based convolutional neural network (CNN) for characterizing sleep stages using EEG signals and then introduced the multiplex motif entropy as the quantitative index to distinguish the six sleep stages. The independent samples t‐test shows that the multiplex motif entropy values have significant differences among the six sleep stages. Furthermore, we developed a CNN model and employed the multiplex motif sequence as the input of the model to classify the six sleep stages. Notably, the classification accuracy of the six‐state stage detection was 85.27%. Results demonstrated the effectiveness of the multiplex motif in characterizing the dynamic features underlying different sleep stages, whereby they further provide an essential strategy for future sleep‐stage detection research.

scholarly journals Automatic Sleep Stage Classification Based on Convolutional Neural Network and Fine-Grained Segments

Complexity ◽  
2018 ◽  
Vol 2018 ◽  
pp. 1-13 ◽  
Author(s):  
Zhihong Cui ◽  
Xiangwei Zheng ◽  
Xuexiao Shao ◽  
Lizhen Cui
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Convolutional Neural Network ◽  
Sleep Stage ◽  
Multiscale Entropy ◽  
Sleep Stages ◽  
Fine Grained ◽  
Stage Classification ◽  
Sleep Stage Classification ◽  
Input Time

Sleep stage classification plays an important role in the diagnosis of sleep-related diseases. However, traditional automatic sleep stage classification is quite challenging because of the complexity associated with the establishment of mathematical models and the extraction of handcrafted features. In addition, the rapid fluctuations between sleep stages often result in blurry feature extraction, which might lead to an inaccurate assessment of electroencephalography (EEG) sleep stages. Hence, we propose an automatic sleep stage classification method based on a convolutional neural network (CNN) combined with the fine-grained segment in multiscale entropy. First, we define every 30 seconds of the multichannel EEG signal as a segment. Then, we construct an input time series based on the fine-grained segments, which means that the posterior and current segments are reorganized as an input containing several segments and the size of the time series is decided based on the scale chosen depending on the fine-grained segments. Next, each segment in this series is individually put into the designed CNN and feature maps are obtained after two blocks of convolution and max-pooling as well as a full-connected operation. Finally, the results from the full-connected layer of each segment in the input time sequence are put into the softmax classifier together to get a single most likely sleep stage. On a public dataset called ISRUC-Sleep, the average accuracy of our proposed method is 92.2%. Moreover, it yields an accuracy of 90%, 86%, 93%, 97%, and 90% for stage W, stage N1, stage N2, stage N3, and stage REM, respectively. Comparative analysis of performance suggests that the proposed method is better, as opposed to that of several state-of-the-art ones. The sleep stage classification methods based on CNN and the fine-grained segments really improve a key step in the study of sleep disorders and expedite sleep research.

A convolutional neural network for sleep stage scoring from raw single-channel EEG

2018 ◽  
Vol 42 ◽  
pp. 107-114 ◽  
Author(s):  
Arnaud Sors ◽  
Stéphane Bonnet ◽  
Sébastien Mirek ◽  
Laurent Vercueil ◽  
Jean-François Payen
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Single Channel ◽  
Sleep Stage ◽  
Sleep Stage Scoring

scholarly journals 0434 Clinical Application of Computer Aided Cloud Sleep Scoring System

SLEEP ◽  
2020 ◽  
Vol 43 (Supplement_1) ◽  
pp. A166-A167
Author(s):  
W Lin ◽  
P Kuo ◽  
M Liu ◽  
C Li ◽  
C Lin ◽  
...  
Keyword(s):  
Scoring System ◽  
Sleep Stage ◽  
Sleep Onset ◽  
Onset Time ◽  
Fine Tuning ◽  
Physiological Signals ◽  
Sleep Stages ◽  
Sleep Scoring ◽  
Computer Aided ◽  
Sleep Centers

Abstract Introduction According to a survey by World Sleep Society, 45% of the population suffered from sleep disorders. The best way to diagnose these patients is to use Polysomnography (PSG), recording their physiological signals throughout the night. Mostly, sleep technologists manually score sleep stages. Manual scoring is quite subjective and time-consuming. Although the technologist’s judgments are based on scoring standards of the American Academy of Sleep Medicine, fine-tuning scoring results because of different considerations in different sleep centers may be happened. In order to assess the consistency of scoring standards in sleep technologists, we tried to establish a cloud sleep scoring system and evaluate its feasibility in 4 sleep centers in southern Taiwan. Methods We constructed a computer-aided cloud sleep scoring system. Each sleep technologist could score the same test data of PSG online without being restricted by places and hardware equipment. After comparing scoring results of all participants, the scoring system could provide the following reports, including an overall agreement, agreement of each sleep stage and each sleep index. Besides, multi-person scoring results of each epoch with displaying physiological signals were analyzed. Results Seven sleep technologists from 4 hospitals in Tainan, Taiwan joined this study. Standard deviations (SDs) of each sleep stage included 2.64 in Wake stage, 6.90 in N1, 8.31 in N2, 6.87 in N3, 1.38 in REM, respectively. SDs of sleep indexes were 2.64 in sleep efficiency, 2.14 in sleep onset time, 8.35 in wake after sleep onset time, 10.03 in total sleep time, individually. The overall agreement was 89.6%. The satisfaction of this scoring system operation was 85.7%. Conclusion With the cloud sleep scoring system assistance, it was feasible to evaluate the scoring consistency among sleep technologists in different sleep centers. Support This work is supported by the Ministry of Science and Technology, Taiwan. (MOST 108-2634-F-006-012)

nCPAP Treatment of Obstructive Sleep Apnea Increases Slow Wave Sleep in Prefrontal EEG

2007 ◽  
Vol 38 (3) ◽  
pp. 148-154 ◽  
Author(s):  
Veera Eskelinen ◽  
Toomas Uibu ◽  
Sari-Leena Himanen
Keyword(s):  
Obstructive Sleep Apnea ◽  
Sleep Apnea ◽  
Treatment Period ◽  
Slow Wave ◽  
Slow Wave Sleep ◽  
Sleep Stage ◽  
Sleep Eeg ◽  
Sleep Stages ◽  
Obstructive Sleep ◽  
Sleep Stage Scoring

According to standard sleep stage scoring, sleep EEG is studied from the central area of parietal lobes. However, slow wave sleep (SWS) has been found to be more powerful in frontal areas in healthy subjects. Obstructive sleep apnea syndrome (OSAS) patients often suffer from functional disturbances in prefrontal lobes. We studied the effects of nasal Continuous Positive Airway Pressure (nCPAP) treatment on sleep EEG, and especially on SWS, in left prefrontal and central locations in 12 mild to moderate OSAS patients. Sleep EEG was recorded by polysomnography before treatment and after a 3 month nCPAP treatment period. Recordings were classified into sleep stages. No difference was found in SWS by central sleep stage scoring after the nCPAP treatment period, but in the prefrontal lobe all night S3 sleep stage increased during treatment. Furthermore, prefrontal SWS increased in the second and decreased in the fourth NREM period. There was more SWS in prefrontal areas both before and after nCPAP treatment, and SWS increased significantly more in prefrontal than central areas during treatment. Regarding only central sleep stage scoring, nCPAP treatment did not increase SWS significantly. Frontopolar recording of sleep EEG is useful in addition to central recordings in order to better evaluate the results of nCPAP treatment.

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