The Experts below are selected from a list of 19095 Experts worldwide ranked by ideXlab platform
Paul M. Matthews - One of the best experts on this subject based on the ideXlab platform.
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mixed neural network approach for temporal Sleep Stage classification
IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2018Co-Authors: Hao Dong, Akara Supratak, Chao Wu, Paul M. MatthewsAbstract:This paper proposes a practical approach to addressing limitations posed by using of single-channel electroencephalography (EEG) for Sleep Stage classification. EEG-based characterizations of Sleep Stage progression contribute the diagnosis and monitoring of the many pathologies of Sleep. Several prior reports explored ways of automating the analysis of Sleep EEG and of reducing the complexity of the data needed for reliable discrimination of Sleep Stages at lower cost in the home. However, these reports have involved recordings from electrodes placed on the cranial vertex or occiput, which are both uncomfortable and difficult to position. Previous studies of Sleep Stage scoring that used only frontal electrodes with a hierarchical decision tree motivated this paper, in which we have taken advantage of rectifier neural network for detecting hierarchical features and long short-term memory network for sequential data learning to optimize classification performance with single-channel recordings. After exploring alternative electrode placements, we found a comfortable configuration of a single-channel EEG on the forehead and have shown that it can be integrated with additional electrodes for simultaneous recording of the electro-oculogram. Evaluation of data from 62 people (with 494 hours Sleep) demonstrated better performance of our analytical algorithm than is available from existing approaches with vertex or occipital electrode placements. Use of this recording configuration with neural network deconvolution promises to make clinically indicated home Sleep studies practical.
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automatic Sleep Stage scoring using time frequency analysis and stacked sparse autoencoders
Annals of Biomedical Engineering, 2016Co-Authors: Orestis Tsinalis, Paul M. MatthewsAbstract:We developed a machine learning methodology for automatic Sleep Stage scoring. Our time-frequency analysis-based feature extraction is fine-tuned to capture Sleep Stage-specific signal features as described in the American Academy of Sleep Medicine manual that the human experts follow. We used ensemble learning with an ensemble of stacked sparse autoencoders for classifying the Sleep Stages. We used class-balanced random sampling across Sleep Stages for each model in the ensemble to avoid skewed performance in favor of the most represented Sleep Stages, and addressed the problem of misclassification errors due to class imbalance while significantly improving worst-Stage classification. We used an openly available dataset from 20 healthy young adults for evaluation. We used a single channel of EEG from this dataset, which makes our method a suitable candidate for longitudinal monitoring using wearable EEG in real-world settings. Our method has both high overall accuracy (78%, range 75–80%), and high mean \(F_1\)-score (84%, range 82–86%) and mean accuracy across individual Sleep Stages (86%, range 84–88%) over all subjects. The performance of our method appears to be uncorrelated with the Sleep efficiency and percentage of transitional epochs in each recording.
Rajendra U Acharya - One of the best experts on this subject based on the ideXlab platform.
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Sleepeegnet automated Sleep Stage scoring with sequence to sequence deep learning approach
PLOS ONE, 2019Co-Authors: Sajad Mousavi, Rajendra U Acharya, Fatemeh AfghahAbstract:Electroencephalogram (EEG) is a common base signal used to monitor brain activities and diagnose Sleep disorders. Manual Sleep Stage scoring is a time-consuming task for Sleep experts and is limited by inter-rater reliability. In this paper, we propose an automatic Sleep Stage annotation method called SleepEEGNet using a single-channel EEG signal. The SleepEEGNet is composed of deep convolutional neural networks (CNNs) to extract time-invariant features, frequency information, and a sequence to sequence model to capture the complex and long short-term context dependencies between Sleep epochs and scores. In addition, to reduce the effect of the class imbalance problem presented in the available Sleep datasets, we applied novel loss functions to have an equal misclassified error for each Sleep Stage while training the network. We evaluated the performance of the proposed method on different single-EEG channels (i.e., Fpz-Cz and Pz-Oz EEG channels) from the Physionet Sleep-EDF datasets published in 2013 and 2018. The evaluation results demonstrate that the proposed method achieved the best annotation performance compared to current literature, with an overall accuracy of 84.26%, a macro F1-score of 79.66% and κ = 0.79. Our developed model can be applied to other Sleep EEG signals and aid the Sleep specialists to arrive at an accurate diagnosis. The source code is available at https://github.com/SajadMo/SleepEEGNet.
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a review of automated Sleep Stage scoring based on physiological signals for the new millennia
Computer Methods and Programs in Biomedicine, 2019Co-Authors: Oliver Faust, Hajar Razaghi, Ragab Barika, Edward J Ciaccio, Rajendra U AcharyaAbstract:Abstract Background and Objective Sleep is an important part of our life. That importance is highlighted by the multitude of health problems which result from Sleep disorders. Detecting these Sleep disorders requires an accurate interpretation of physiological signals. Prerequisite for this interpretation is an understanding of the way in which Sleep Stage changes manifest themselves in the signal waveform. With that understanding it is possible to build automated Sleep Stage scoring systems. Apart from their practical relevance for automating Sleep disorder diagnosis, these systems provide a good indication of the amount of Sleep Stage related information communicated by a specific physiological signal. Methods This article provides a comprehensive review of automated Sleep Stage scoring systems, which were created since the year 2000. The systems were developed for Electrocardiogram (ECG), Electroencephalogram (EEG), Electrooculogram (EOG), and a combination of signals. Results Our review shows that all of these signals contain information for Sleep Stage scoring. Conclusions The result is important, because it allows us to shift our research focus away from information extraction methods to systemic improvements, such as patient comfort, redundancy, safety and cost.
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nonlinear dynamics measures for automated eeg based Sleep Stage detection
European Neurology, 2015Co-Authors: Rajendra U Acharya, Oliver Faust, Shreya Bhat, Hojjat Adeli, Eric Chernpin Chua, Wei Jie Eugene Lim, Joel En Wei KohAbstract:Background: The brain's continuous neural activity during Sleep can be monitored by electroencephalogram (EEG) signals. The EEG wave pattern and frequency vary during five Stages of Sleep. These subtle variations in Sleep EEG signals cannot be easily detected through visual inspection. Summary: A range of time, frequency, time-frequency and nonlinear analysis methods can be applied to understand the complex physiological signals and their chaotic behavior. This paper presents a comprehensive comparative review and analysis of 29 nonlinear dynamics measures for EEG-based Sleep Stage detection. Key Messages: The characteristic ranges of these features are reported for the five different Sleep Stages. All nonlinear measures produce clinically significant results, that is, they can discriminate the individual Sleep Stages. Feature ranking based on the statistical F-value, however, shows that the third order cumulant of higher order spectra yields the most discriminative result. The distinct value ranges for each Sleep Stage and the discriminative power of the features can be used for Sleep disorder diagnosis, treatment monitoring, and drug efficacy assessment.
Yunhao Liu - One of the best experts on this subject based on the ideXlab platform.
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Sleep Hunter: Towards Fine Grained Sleep Stage Tracking with Smartphones
IEEE Transactions on Mobile Computing, 2016Co-Authors: Longfei Shangguan, Zheng Yang, Yunhao LiuAbstract:Sleep quality plays a vital role in personal health. A great deal of effort has been paid to design Sleep quality monitoring systems, providing services ranging from bedtime monitoring to Sleep activity detection. However, as Sleep quality is closely related to the distribution of Sleep duration over different Sleep Stages, neither the bedtime nor the intensity of Sleep activities is able to reflect Sleep quality precisely. We present Sleep Hunter, a mobile service that provides a fine-grained detection of Sleep Stage transition for Sleep quality monitoring and intelligent wake-up call. The rationale is that each Sleep Stage is accompanied by specific body movements and acoustic signals. Leveraging the built-in sensors on smartphones, Sleep Hunter integrates these physical activities with Sleep environment, inherent temporal relation, and personal factors by a statistical model for a fine-grained Sleep Stage detection. Based on the duration of each Sleep Stage, Sleep Hunter further provides Sleep quality report and smart call service for users. Experimental results from over 30 sets of nocturnal Sleep data show that our system is superior to existing actigraphy-based Sleep quality monitoring systems, and achieves satisfying detection accuracy compared with dedicated polysomnography-based devices.
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intelligent Sleep Stage mining service with smartphones
Ubiquitous Computing, 2014Co-Authors: Zheng Yang, Longfei Shangguan, Wei Sun, Kun Jin, Yunhao LiuAbstract:Sleep quality plays a significant role in personal health. A great deal of effort has been paid to design Sleep quality monitoring systems, providing services ranging from bedtime monitoring to Sleep activity detection. However, as Sleep quality is closely related to the distribution of Sleep duration over different Sleep Stages, neither the bedtime nor the intensity of Sleep activities is able to reflect Sleep quality precisely. To this end, we present Sleep Hunter, a mobile service that provides a fine-grained detection of Sleep Stage transition for Sleep quality monitoring and intelligent wake-up call. The rationale is that each Sleep Stage is accompanied by specific yet distinguishable body movements and acoustic signals. Leveraging the built-in sensors on smartphones, Sleep Hunter integrates these physical activities with Sleep environment, inherent temporal relation and personal factors by a statistical model for a fine-grained Sleep Stage detection. Based on the duration of each Sleep Stage, Sleep Hunter further provides Sleep quality report and smart call service for users. Experimental results from over 30 sets of nocturnal Sleep data show that our system is superior to existing actigraphy-based Sleep quality monitoring systems, and achieves satisfying detection accuracy compared with dedicated polysomnography-based devices.
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UbiComp - Intelligent Sleep Stage mining service with smartphones
Proceedings of the 2014 ACM International Joint Conference on Pervasive and Ubiquitous Computing - UbiComp '14 Adjunct, 2014Co-Authors: Zheng Yang, Longfei Shangguan, Wei Sun, Kun Jin, Yunhao LiuAbstract:Sleep quality plays a significant role in personal health. A great deal of effort has been paid to design Sleep quality monitoring systems, providing services ranging from bedtime monitoring to Sleep activity detection. However, as Sleep quality is closely related to the distribution of Sleep duration over different Sleep Stages, neither the bedtime nor the intensity of Sleep activities is able to reflect Sleep quality precisely. To this end, we present Sleep Hunter, a mobile service that provides a fine-grained detection of Sleep Stage transition for Sleep quality monitoring and intelligent wake-up call. The rationale is that each Sleep Stage is accompanied by specific yet distinguishable body movements and acoustic signals. Leveraging the built-in sensors on smartphones, Sleep Hunter integrates these physical activities with Sleep environment, inherent temporal relation and personal factors by a statistical model for a fine-grained Sleep Stage detection. Based on the duration of each Sleep Stage, Sleep Hunter further provides Sleep quality report and smart call service for users. Experimental results from over 30 sets of nocturnal Sleep data show that our system is superior to existing actigraphy-based Sleep quality monitoring systems, and achieves satisfying detection accuracy compared with dedicated polysomnography-based devices.
Alexandre Gramfort - One of the best experts on this subject based on the ideXlab platform.
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A deep learning architecture for temporal Sleep Stage classification using multivariate and multimodal time series
IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2018Co-Authors: Stanislas Chambon, Mathieu Galtier, Pierrick Arnal, Gilles Wainrib, Alexandre GramfortAbstract:Sleep Stage classification constitutes an important preliminary exam in the diagnosis of Sleep disorders. It is traditionally performed by a Sleep expert who assigns to each 30 s of signal a Sleep Stage, based on the visual inspection of signals such as electroencephalograms (EEG), electrooculograms (EOG), electrocardiograms (ECG) and electromyograms (EMG). We introduce here the first deep learning approach for Sleep Stage classification that learns end-to-end without computing spectrograms or extracting hand-crafted features, that exploits all multivariate and multimodal Polysomnography (PSG) signals (EEG, EMG and EOG), and that can exploit the temporal context of each 30 s window of data. For each modality the first layer learns linear spatial filters that exploit the array of sensors to increase the signal-to-noise ratio, and the last layer feeds the learnt representation to a softmax classifier. Our model is compared to alternative automatic approaches based on convolutional networks or decisions trees. Results obtained on 61 publicly available PSG records with up to 20 EEG channels demonstrate that our network architecture yields state-of-the-art performance. Our study reveals a number of insights on the spatio-temporal distribution of the signal of interest: a good trade-off for optimal classification performance measured with balanced accuracy is to use 6 EEG with 2 EOG (left and right) and 3 EMG chin channels. Also exploiting one minute of data before and after each data segment offers the strongest improvement when a limited number of channels is available. As Sleep experts, our system exploits the multivariate and multimodal nature of PSG signals in order to deliver state-of-the-art classification performance with a small computational cost.
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a deep learning architecture for temporal Sleep Stage classification using multivariate and multimodal time series
IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2018Co-Authors: Stanislas Chambon, Mathieu Galtier, Gilles Wainrib, Pierrick J Arnal, Alexandre GramfortAbstract:Sleep Stage classification constitutes an important preliminary exam in the diagnosis of Sleep disorders. It is traditionally performed by a Sleep expert who assigns to each 30 s of the signal of a Sleep Stage, based on the visual inspection of signals such as electroencephalograms (EEGs), electrooculograms (EOGs), electrocardiograms, and electromyograms (EMGs). We introduce here the first deep learning approach for Sleep Stage classification that learns end-to-end without computing spectrograms or extracting handcrafted features, that exploits all multivariate and multimodal polysomnography (PSG) signals (EEG, EMG, and EOG), and that can exploit the temporal context of each 30-s window of data. For each modality, the first layer learns linear spatial filters that exploit the array of sensors to increase the signal-to-noise ratio, and the last layer feeds the learnt representation to a softmax classifier. Our model is compared to alternative automatic approaches based on convolutional networks or decisions trees. Results obtained on 61 publicly available PSG records with up to 20 EEG channels demonstrate that our network architecture yields the state-of-the-art performance. Our study reveals a number of insights on the spatiotemporal distribution of the signal of interest: a good tradeoff for optimal classification performance measured with balanced accuracy is to use 6 EEG with 2 EOG (left and right) and 3 EMG chin channels. Also exploiting 1 min of data before and after each data segment offers the strongest improvement when a limited number of channels are available. As Sleep experts, our system exploits the multivariate and multimodal nature of PSG signals in order to deliver the state-of-the-art classification performance with a small computational cost.
Orestis Tsinalis - One of the best experts on this subject based on the ideXlab platform.
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automatic Sleep Stage scoring using time frequency analysis and stacked sparse autoencoders
Annals of Biomedical Engineering, 2016Co-Authors: Orestis Tsinalis, Paul M. MatthewsAbstract:We developed a machine learning methodology for automatic Sleep Stage scoring. Our time-frequency analysis-based feature extraction is fine-tuned to capture Sleep Stage-specific signal features as described in the American Academy of Sleep Medicine manual that the human experts follow. We used ensemble learning with an ensemble of stacked sparse autoencoders for classifying the Sleep Stages. We used class-balanced random sampling across Sleep Stages for each model in the ensemble to avoid skewed performance in favor of the most represented Sleep Stages, and addressed the problem of misclassification errors due to class imbalance while significantly improving worst-Stage classification. We used an openly available dataset from 20 healthy young adults for evaluation. We used a single channel of EEG from this dataset, which makes our method a suitable candidate for longitudinal monitoring using wearable EEG in real-world settings. Our method has both high overall accuracy (78%, range 75–80%), and high mean \(F_1\)-score (84%, range 82–86%) and mean accuracy across individual Sleep Stages (86%, range 84–88%) over all subjects. The performance of our method appears to be uncorrelated with the Sleep efficiency and percentage of transitional epochs in each recording.
