A Novel Sleep Stage Scoring System: Combining Expert-Based Rules with a Decision Tree Classifier

Gunnarsdottir, Kristin M.; Gamaldo, Charlene E.; Salas, Rachel Marie E.; Ewen, Joshua B.; Allen, Richard P.; Sarma, Sridevi V.

doi:10.1109/embc.2018.8513039

Cited by 25 publications

(4 citation statements)

References 16 publications

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“…In [21] used PSG data for automated sleep staging. The extracted properties are classified using decision table classifiers.…”

Section: B Significancementioning

confidence: 99%

Machine Learning Approaches with Automated Sleep Staging System based on Two-Layer Heterogeneous Ensemble Learning Stacking Model

Satapathy¹,

Loganathan²

2022

IJCDS

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Sleep is an essential requirement for human health and well-being, but many people face sleep problems. These problems can lead to several neurological and physical disorders and adversely affect the overall quality of life. Artificial intelligence (AI)based methods for automated sleep stage classification is a fundamental approach to evaluating and treating this public health challenge.The main contribution of this research work is to develop an Automated Sleep Staging System based on Two-Layer Heterogeneous Ensemble Learning Stacking Model (ASSS-TL-HELSM) for sleep staging under the American Academy of Sleep Medicine (AASM) sleep scoring rules. The main aim of this model is to enhance sleep staging accuracy, reduce overfitting and handle overdrift. For signal preprocessing, we use two different feature selection techniques, Fisher Score (FS), and ReliefF (ReF). For feature extraction, we obtain a total of 28 features.The proposed model analyzes the sleep behavior of the subject using the seasonal and trend components. Sleep recordings from two different subgroups of Institute of Systems and Robotics University of Coimbra (ISRUC-Sleep) were obtained for our experiments.Compared with recent studies using single-channel electro encephalogram (EEG) signals, our proposed ASSS-TL-HELSM model shows the best sleep staging classification accuracy performance on a five sleep stages classification (SC-5) task. The overall classification accuracy is 97.93%, and 97% for features selected through FS and ReF respectively, with the subgroup-I(SG-I) data; similarly, for the subgroup-III(SG-III) data, the features selected through FS, and ReF show a classification accuracy of 98.16% and 98.78% respectively. The comparisons between the proposed model and the existing model show that the proposed model gives better sleep staging accuracy for the five-sleep state's classification.

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“…In [21] used PSG data for automated sleep staging. The extracted properties are classified using decision table classifiers.…”

Section: B Significancementioning

confidence: 99%

Machine Learning Approaches with Automated Sleep Staging System based on Two-Layer Heterogeneous Ensemble Learning Stacking Model

Satapathy¹,

Loganathan²

2022

IJCDS

View full text Add to dashboard Cite

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“…(4) nonlinear features. (Gunnarsdottir et al, 2018) extracted timedomain and frequency-domain features from PSG signals, using data from healthy people, and using a decision Most of the existing feature extraction methods extract features from a single channel (Terzano et al, 2001;Tagliazucchi et al, 2013;Tagliazucchi and Laufs, 2014;Lv et al, 2015;Desjardins et al, 2017;Stevner et al, 2019;Fu et al, 2021), the calculation is also performed separately on a single channel. The amount of information obtained through a single channel does not fully characterize the changes in brain activities during sleep, making it difficult to explore sleep stage information from a global level.…”

Section: Introductionmentioning

confidence: 99%

“…(4) nonlinear features. ( Gunnarsdottir et al, 2018 ) extracted time-domain and frequency-domain features from PSG signals, using data from healthy people, and using a decision table classifier to classify the extracted attributes, with an overall classification accuracy of 80.70%. da Silveira et al (2016) used discrete wavelet transform techniques to analyze the changes in sleep behavior in different frequency ranges, extracted skewness, kurtosis, and variance features from the corresponding input channels, and evaluated the ability of random forest classifiers to distinguish different sleep stages.…”

Section: Introductionmentioning

confidence: 99%

Exploration of sleep function connection and classification strategies based on sub-period sleep stages

Zhao

Liu

et al. 2023

Front. Neurosci.

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BackgroundAs a medium for developing brain-computer interface systems, EEG signals are complex and difficult to identify due to their complexity, weakness, and differences between subjects. At present, most of the current research on sleep EEG signals are single-channel and dual-channel, ignoring the research on the relationship between different brain regions. Brain functional connectivity is considered to be closely related to brain activity and can be used to study the interaction relationship between brain areas.MethodsPhase-locked value (PLV) is used to construct a functional connection network. The connection network is used to analyze the connection mechanism and brain interaction in different sleep stages. Firstly, the entire EEG signal is divided into multiple sub-periods. Secondly, Phase-locked value is used for feature extraction on the sub-periods. Thirdly, the PLV of multiple sub-periods is used for feature fusion. Fourthly, the classification performance optimization strategy is used to discuss the impact of different frequency bands on sleep stage classification performance and to find the optimal frequency band. Finally, the brain function network is constructed by using the average value of the fusion features to analyze the interaction of brain regions in different frequency bands during sleep stages.ResultsThe experimental results have shown that when the number of sub-periods is 30, the α (8–13 Hz) frequency band has the best classification effect, The classification result after 10-fold cross-validation reaches 92.59%.ConclusionThe proposed algorithm has good sleep staging performance, which can effectively promote the development and application of an EEG sleep staging system.

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“…[12] combined structural graph similarity and the k-means to identify six sleep stages. [13] developed likelihood ratio decision tree classifier and extracted features from EEG, electromyogram (EMG), and electrooculogram (EOG) signals. Although those traditional machine learning methods are widely used, they must separate feature extraction and classification, and design their methods separately.…”

Section: Introductionmentioning

confidence: 99%

A Novel Sleep Stage Classification Using CNN Generated by an Efficient Neural Architecture Search with a New Data Processing Trick

Xue¹,

Yuan²,

Słowik³

2021

Preprint

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With the development of automatic sleep stage classification (ASSC) techniques, many classical methods such as k-means, decision tree, and SVM have been used in automatic sleep stage classification. However, few methods explore deep learning on ASSC. Meanwhile, most deep learning methods require extensive expertise and suffer from a mass of handcrafted steps which are time-consuming especially when dealing with multi-classification tasks. In this paper, we propose an efficient five-sleep-stage classification method using convolutional neural networks (CNNs) with a novel data processing trick and we design neural architecture search (NAS) technique based on genetic algorithm (GA), NAS-G, to search for the best CNN architecture. Firstly, we attach each kernel with an adaptive coefficient to enhance the signal processing of the inputs. This can enhance the propagation of informative features and suppress the propagation of useless features in the early stage of the network. Then, we make full use of GA's heuristic search and the advantage of no need for the gradient to search for the best architecture of CNN. This can achieve a CNN with better performance than a handcrafted one in a large search space at the minimum cost. We verify the convergence of our data processing trick and compare the performance of traditional CNNs before and after using our trick. Meanwhile, we compare the performance between the CNN generated through NAS-G and the traditional CNNs with our trick. The experiments demonstrate that the convergence of CNNs with data processing trick is faster than without data processing trick and the CNN with data pro-cessing trick generated by NAS-G outperforms the handcrafted counterparts that use the data processing trick too. Our research suggests that deep learning has a great potential on the electroencephalogram signal processing especially with the intensification of NAS. Meanwhile, NAS can exert greater power in practical engineering applications.

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A Novel Sleep Stage Scoring System: Combining Expert-Based Rules with a Decision Tree Classifier

Cited by 25 publications

References 16 publications

Machine Learning Approaches with Automated Sleep Staging System based on Two-Layer Heterogeneous Ensemble Learning Stacking Model

Machine Learning Approaches with Automated Sleep Staging System based on Two-Layer Heterogeneous Ensemble Learning Stacking Model

Exploration of sleep function connection and classification strategies based on sub-period sleep stages

A Novel Sleep Stage Classification Using CNN Generated by an Efficient Neural Architecture Search with a New Data Processing Trick

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