On the Use of Neural Network Techniques to Analyse Sleep EEG Data

Baumgart-Schmitt, R.; Wm, Herrmann; Eilers, Rebecca E.; Bes, Frédérik

doi:10.1159/000119412

Cited by 21 publications

(6 citation statements)

References 12 publications

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“…The selection of the topology of the ANN is a methodological aspect that was investigated in the present work. Various methodologies for the selection of the number and the size of hidden layers in ANNs have been used, including evolutionary strategies and genetic algorithms [28, 29], network pruning techniques [30], network growing techniques [31], as well as extensive network architecture search [32]. …”

Section: System Designmentioning

confidence: 99%

Classification of Event-Related Potentials Associated with Response Errors in Actors and Observers Based on Autoregressive Modeling

Vasios

Ventouras²,

Matsopoulos³

et al. 2009

TOMINFOJ

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Event-Related Potentials (ERPs) provide non-invasive measurements of the electrical activity on the scalp related to the processing of stimuli and preparation of responses by the brain. In this paper an ERP-signal classification method is proposed for discriminating between ERPs of correct and incorrect responses of actors and of observers seeing an actor making such responses. The classification method targeted signals containing error-related negativity (ERN) and error positivity (Pe) components, which are typically associated with error processing in the human brain. Feature extraction consisted of Multivariate Autoregressive modeling combined with the Simulated Annealing technique. The resulting information was subsequently classified by means of an Artificial Neural Network (ANN) using back-propagation algorithm under the "leave-one-out cross-validation" scenario and the Fuzzy C-Means (FCM) algorithm. The ANN consisted of a multi-layer perceptron (MLP). The approach yielded classification rates of up to 85%, both for the actors' correct and incorrect responses and the corresponding ERPs of the observers. The electrodes needed for such classifications were situated mainly at central and frontal areas. Results provide indications that the classification of the ERN is achievable. Furthermore, the availability of the Pe signals, in addition to the ERN, improves the classification, and this is more pronounced for observers' signals. The proposed ERP-signal classification method provides a promising tool to study error detection and observational-learning mechanisms in performance monitoring and joint-action research, in both healthy and patient populations.

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Section: System Designmentioning

confidence: 99%

Classification of Event-Related Potentials Associated with Response Errors in Actors and Observers Based on Autoregressive Modeling

Vasios

Ventouras²,

Matsopoulos³

et al. 2009

TOMINFOJ

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“…This has led to the development of automated systems for EEG analysis during different stages of sleep (Table 2). Most of the automated systems still rely on signal from EEG, EMG and EOG, but a few systems have tried using single-channel surface EEG 2,5,10,12,16 . The problem with automated sleep staging is that none of them show a 100% agreement with manual sleep staging.…”

Section: Discussionmentioning

confidence: 99%

Automatic detection of periods of slow wave sleep based on intracranial depth electrode recordings

Reed

Birch

Kamiński

et al. 2017

Journal of Neuroscience Methods

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Background An automated process for sleep staging based on intracranial EEG data alone is needed to facilitate research into the neural processes occurring during slow wave sleep (SWS). Current manual methods for sleep scoring require a full polysomnography (PSG) set-up, including electrooculography (EOG), electromyography (EMG), and scalp electroencephalography (EEG). This set-up can be technically difficult to place in the presence of intracranial EEG electrodes. There is thus a need for a method for sleep staging based on intracranial recordings alone. New Method Here we show a reliable automated method for the detection of periods of SWS solely based on intracranial EEG recordings. The method is utilizes the ratio of spectral power in delta, theta, and spindle frequencies relative to alpha and beta frequencies to classify 30-second segments as SWS or not. Results We evaluated this new method by comparing its performance against visually scored patients (n=9), in which we also recorded EOG and EMG simultaneously. Our method had a mean positive predictive value of 64% across all nights. Also, an ROC analysis of the performance of our algorithm compared to manually labeled nights revealed a mean average area under the curve of 0.91 across all nights. Comparison with existing method Our method had an average kappa score of 0.72 when compared to visual sleep scoring by an independent blinded sleep scorer. Conclusion This shows that this simple method is capable of differentiating between SWS and non-SWS epochs reliably based solely on intracranial EEG recordings.

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“…The sleep staging process has been modeled with various types of ANN [28], [32], [36], [51]; such as the multi-layer perception, hybrid neurofuzzy system [16], feed-forward multi-layer neural network [20], [36].…”

Section: ) Artificial Neural Network (Ann)mentioning

confidence: 99%

Computer based sleep staging: Challenges for the future

Hamida

Ahmed

2013

2013 7th IEEE GCC Conference and Exhibition (GCC)

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Studies have shown that patients suffering from sleep deprivation have a risk for hypertension, diabetes and depression that is higher than normal sleepers. Treatment for all these problems requires accurate analysis of the sleep stages and patterns in the polysomnographic signals collected in overnight recording over several months. However, manual sleep staging is a repetitive and time-consuming process as marking one typical eight hours overnight polysomnographic recording can take up to two hours to complete. Due to increased processing capabilities, it is now possible to automate this process and assist the sleep expert. A large number of algorithms have been proposed during the last few decades. This review article presents an overview of the existing automatic sleep staging methods, discusses the different challenges and proposes future prospects for new research opportunities.

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On the Use of Neural Network Techniques to Analyse Sleep EEG Data

Cited by 21 publications

References 12 publications

Classification of Event-Related Potentials Associated with Response Errors in Actors and Observers Based on Autoregressive Modeling

Classification of Event-Related Potentials Associated with Response Errors in Actors and Observers Based on Autoregressive Modeling

Automatic detection of periods of slow wave sleep based on intracranial depth electrode recordings

Computer based sleep staging: Challenges for the future

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