Automatic sleep staging with a single-channel EEG based on ensemble empirical mode decomposition

Liu, Cong; Tan, Bin; Fu, Mingyu; Jin-lian, LI; Wang, Jun; Hou, Fengzhen; Yang, Albert C.

doi:10.1016/j.physa.2020.125685

Cited by 34 publications

(17 citation statements)

References 52 publications

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“…Many ML and DL-based models proposed in the literature achieved better accuracy and kappa (κ) values. Although ASSC systems produce better results, a set of specific challenges exist, such as database variability, channel mismatch, class imbalance, inter-class distinction, computational complexity, and scoring issues ( 63 , 65 ). Most of the ML and DL-based models proposed in the literature used distinct datasets; these data had been collected from different individuals.…”

Section: Discussionmentioning

confidence: 99%

“…An in-home ASSC system based on single-channel EEG is required to alleviate the problems in manual scoring and enable the development of a convenient, comfortable, and less expensive in-home sleep monitoring system ( 63 ). Single-channel systems have excellent scope in terms of convenience and cost-effectiveness.…”

Section: Discussionmentioning

confidence: 99%

“…), clustering (KNN), and an amalgamation of distinct models known as ensemble learning (stacking, boosting, bagging, and blending). These approaches achieved 74.5 to 91.9 % accuracy levels for a standard five-class classification ( 57 , 59 – 63 ).…”

Section: Role Of Ai In Sleep Stage Classificationmentioning

confidence: 99%

See 2 more Smart Citations

Efficacy of Single-Channel EEG: A Propitious Approach for In-home Sleep Monitoring

Radhakrishnan

Kirubakaran

Jebadurai

et al. 2022

Front. Public Health

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

See 1 more Smart Citation

Efficacy of Single-Channel EEG: A Propitious Approach for In-home Sleep Monitoring

Radhakrishnan

Kirubakaran

Jebadurai

et al. 2022

Front. Public Health

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“…The accuracy, F1-score, and kappa coefficient are 86.4%, 0.8, and 0.81, respectively. In [ 49 ], Cong et al proposed a classification model with the XGBoost algorithm and tested it using fivefold cross-validation on three different databases. In the tasks of 4-class and 5-class sleep staging, the proposed method achieved an accuracy of 87.5% and 85.8% in the SHHS database, respectively, and the kappa coefficient is 0.79 and 0.81, respectively.…”

Section: Experimental Analysismentioning

confidence: 99%

Physiological Status Prediction Based on a Novel Hybrid Intelligent Scheme

Liu

et al. 2022

Computational Intelligence and Neuroscience

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Physiological status plays an important role in clinical diagnosis. However, the temporal physiological data change dynamically with time, and the amount of data is large; furthermore, obtaining a complete history of data has become difficult. We propose a hybrid intelligent scheme for physiological status prediction, which can be effectively utilized to predict the physiological status of patients and provide a reference for clinical diagnosis. Our proposed scheme initially extracted the attribute information of nonlinear dynamic changes in physiological signals. The maximum discriminant feature subset was selected by employing conditional relevance mutual information feature selection. An optimal subset of features was fed into the particle swarm optimization–support vector machine classifier to perform classification. For the prediction task, the proposed hybrid intelligent scheme was tested on the Sleep Heart Health Study dataset for sleep status prediction. Experimental results demonstrate that our proposed intelligent scheme outperforms the conventional machine learning classification methods.

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“…In 2017, Hassan et al used the wavelet transform to decompose the EEG into multiple sub-bands [12], extracted four statistical features of the sub-bands, and used a bootstrap aggregation model (bagging) to classify the EEG in the Sleep-EDF database and the Dreams Subjects database for Class 4 and Class 5 sleep staging, with accuracies of 94.36% and 93.69% and 83.78% and 78.95%, respectively. In 2020, Liu et al used the ensemble empirical model algorithm (EEMD) to decompose the EEG and extract features of the time domain, statistics, and nonlinear dynamics [13] and used the gradient boosting algorithm to classify the Sleep-EDF database, DREAMS Subjects database, and Sleep Heart Health Study (SHHS) database for 4 and 5 categories of sleep staging of the EEG with accuracies of 93.1% and 91.9%, 86.4% and 83.4%, and 87.5% and 85.8%, respectively.…”

Section: Introductionmentioning

confidence: 99%

Automatic Sleep Staging Based on Single-Channel EEG Signal Using Null Space Pursuit Decomposition Algorithm

Xiao

Linghu

et al. 2022

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Sleep quality is related to people’s physical and mental health, so an accurate assessment of sleep quality is key to recognizing sleep disorders and taking effective interventions. To address the shortcomings of traditional manual and automatic staging methods, such as being time-consuming and having low classification accuracy, an automatic sleep staging method based on the null space pursuit (NSP) decomposition algorithm of single-channel electroencephalographic (EEG) signals is proposed, which provides a new way for EEG signal decomposition and automatic identification of sleep stages. First, the single-channel EEG signal data from the Sleep-EDF database, DREAMS Subject database, and Sleep Heart Health Study database (SHHS), available on PhysioNet, were preprocessed, respectively. Second, the preprocessed single-channel EEG signals were decomposed by the NSP algorithm. Third, we extracted nine features in the time domain of the nonlinear dynamics and statistics from the original EEG signal and the six simple signals that were decomposed. Finally, the extreme gradient boosting (XGBOOST) algorithm was used to construct a classification model to classify and identify the 63 extracted EEG signal features for automatic sleep staging. The experimental results showed that, on the Sleep-EDF database, the accuracy of four and five categories were 93.59% and 92.89%, respectively; on the DREAMS Subject database, the accuracy rates of four and five categories were 91.32% and 90.01%, respectively; on the SHHS database, the accuracy rates of four and five categories were 90.25% and 88.37%, respectively. The experimental results show that the automatic sleep staging model proposed in this work has high classification accuracy and efficiency, as well as strong applicability and robustness.

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Automatic sleep staging with a single-channel EEG based on ensemble empirical mode decomposition

Cited by 34 publications

References 52 publications

Efficacy of Single-Channel EEG: A Propitious Approach for In-home Sleep Monitoring

Efficacy of Single-Channel EEG: A Propitious Approach for In-home Sleep Monitoring

Physiological Status Prediction Based on a Novel Hybrid Intelligent Scheme

Automatic Sleep Staging Based on Single-Channel EEG Signal Using Null Space Pursuit Decomposition Algorithm

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