Recurrent Deep Neural Networks for Real-Time Sleep Stage Classification From Single Channel EEG

Bresch, Erik; Großekathöfer, Ulf; Garcia-Molina, Gary

doi:10.3389/fncom.2018.00085

Cited by 75 publications

(57 citation statements)

References 25 publications

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“…Surprisingly enough, new works keep appearing in scientific journals claiming "good generalization performance", nevertheless missing the proper experimental design to support such statement. To our knowledge, the few exceptions to this reporting trend are the very recent works of [19] [20] [21], which have included performance generalization tests using independent external databases as part of their experimentation. In [21] a clear downgrade in performance is noticeable when comparing the results from their local database validation (Tables S1 and S2, test dataset, in [21]) with the corresponding generalization results using an external dataset (Tables S4 and S5 in [21]), hence confirming our findings.…”

Section: Discussionmentioning

confidence: 99%

Addressing database variability in learning from medical data: An ensemble-based approach using convolutional neural networks and a case of study applied to automatic sleep scoring

Álvarez-Estévez

Fernández-Varela

2020

Computers in Biology and Medicine

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In this work we examine the problematic associated to the development of machine learning models to achieve robust generalization capabilities on common-task multiple-database scenarios. Referred as the "database variability problem", we focus on a specific medical domain (sleep staging in Sleep Medicine) to show the non-triviality of translating the estimated model's local generalization capabilities to independent external databases. We analyze some of the scalability problems when multiple-database data are used as input to train a single learning model. Then, we introduce a novel approach based on an ensemble of local models, and we show its advantages in terms of inter-database generalization performance and data scalability. Further on, we analyze different model configurations and data preprocessing techniques to evaluate their effects over the overall generalization performance. For this purpose we carry out experimentation involving several sleep databases evaluating different machine learning models based on Convolutional Neural Networks.

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Section: Discussionmentioning

confidence: 99%

Addressing database variability in learning from medical data: An ensemble-based approach using convolutional neural networks and a case of study applied to automatic sleep scoring

Álvarez-Estévez

Fernández-Varela

2020

Computers in Biology and Medicine

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“…Zhang and Wu (2017) subsequently improved the CCNN and proposed the Fast Discriminate CCNN. Bresch et al (2018) and Supratak et al (2017) both proposed a sleep stage classification method which combines a convolutional neural network (CNN) and a long short-term memory (LSTM) network. Bresch et al (2018) used a CNN to extract features every 30 s from a single-channel EEG signal and used an LSTM network to perform the classification with the extracted features as the input.…”

Section: Introductionmentioning

confidence: 99%

Sleep Stage Classification Using Time-Frequency Spectra From Consecutive Multi-Time Points

Yang

Sun

et al. 2020

Front. Neurosci.

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Sleep stage classification is an open challenge in the field of sleep research. Considering the relatively small size of datasets used by previous studies, in this paper we used the Sleep Heart Health Study dataset from the National Sleep Research Resource database. A long short-term memory (LSTM) network using a time-frequency spectra of several consecutive 30 s time points as an input was used to perform the sleep stage classification. Four classical convolutional neural networks (CNNs) using a time-frequency spectra of a single 30 s time point as an input were used for comparison. Results showed that, when considering the temporal information within the time-frequency spectrum of a single 30 s time point, the LSTM network had a better classification performance than the CNNs. Moreover, when additional temporal information was taken into consideration, the classification performance of the LSTM network gradually increased. It reached its peak when temporal information from three consecutive 30 s time points was considered, with a classification accuracy of 87.4% and a Cohen's Kappa coefficient of 0.8216. Compared with CNNs, our results indicate that for sleep stage classification, the temporal information within the data or the features extracted from the data should be considered. LSTM networks take this temporal information into account, and thus, may be more suitable for sleep stage classification.

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“…A network containing hybrid of CNN-RNN provided a classification accuracy of 80%. An important advantage of this system is that, this can be used at home for consumer usage [27].…”

Section: Fig 3: Cnn-rnn Hybrid [26]mentioning

confidence: 99%

Electroencephalogram Classification Using Various Artificial Neural Networks

2020

jcr

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Electroencephalography (EEG) is the oldest diagnostic tool used in the field of neurosciences. When a neurologist sees an EEG report, he can point out important neural defects in a person but many a times diagnoses were missed and it is just impossible for human brain to process all the data in EEG. Nowadays many deep machine learning architectures are developed to understand the information contained in EEG signals. This study reviews various literatures on classification of EEG signals using various artificial neural networks like Convoluted neural network, Recurrent Neural Network, Deep Belief neural networks and hybrids.

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Recurrent Deep Neural Networks for Real-Time Sleep Stage Classification From Single Channel EEG

Cited by 75 publications

References 25 publications

Addressing database variability in learning from medical data: An ensemble-based approach using convolutional neural networks and a case of study applied to automatic sleep scoring

Addressing database variability in learning from medical data: An ensemble-based approach using convolutional neural networks and a case of study applied to automatic sleep scoring

Sleep Stage Classification Using Time-Frequency Spectra From Consecutive Multi-Time Points

Electroencephalogram Classification Using Various Artificial Neural Networks

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