An Attention-Based Deep Learning Approach for Sleep Stage Classification With Single-Channel EEG

Eldele, Emadeldeen; Chen, Zhenghua; Liu, Chengyu; Wu, Min; Kwoh, Chee Keong; Liu, Yuanyuan; Guan, Cuntai

doi:10.1109/tnsre.2021.3076234

Cited by 358 publications

(262 citation statements)

References 36 publications

Supporting

Mentioning

259

Contrasting

Order By: Relevance

“…We adopt Sleep-EDF dataset [32], which contains EEG readings from 20 healthy subjects. We select a single channel (i.e., Fpz-Cz) following previous studies [33], and include 10 different subjects to construct the five cross-domain scenarios.…”

Section: Ssc Sleep Stage Classification (Ssc) Problem Aims To Classif...mentioning

confidence: 99%

ADATIME: A Benchmarking Suite for Domain Adaptation on Time Series Data

Ragab¹,

Eldele²,

Tan³

et al. 2022

Preprint

Self Cite

View full text Add to dashboard Cite

Unsupervised domain adaptation methods aim to generalize well on unlabeled test data that may have a different (shifted) distribution from the training data. Such methods are typically developed on image data, and their application to time series data is less explored.Existing works on time series domain adaptation suffer from inconsistencies in evaluation schemes, datasets, and backbone neural network architectures. Moreover, labeled target data are usually employed for model selection, which violates the fundamental assumption of unsupervised domain adaptation. To address these issues, we develop a benchmarking evaluation suite (AdaTime) to systematically and fairly evaluate different domain adaptation methods on time series data. Specifically, we standardize the backbone neural network architectures and benchmarking datasets, while also exploring more realistic model selection approaches that can work with no labeled data or just few labeled samples. Our evaluation includes adapting state-of-the-art visual domain adaptation methods to time series data in addition to the recent methods specifically developed for time series data. We conduct extensive experiments to evaluate 10 state-of-the-art methods on four representative datasets spanning 20 cross-domain scenarios. Our results suggest that with careful selection of hyper-parameters, visual domain adaptation methods are competitive with methods proposed for time series domain adaptation. In addition, we find that hyper-parameters could be selected based on realistic model selection approaches. Our work unveils practical insights for applying domain adaptation methods on time series data and builds a solid foundation for future works in the field. The code is available at github.com/emadeldeen24/AdaTime.

show abstract

Section: Ssc Sleep Stage Classification (Ssc) Problem Aims To Classif...mentioning

confidence: 99%

ADATIME: A Benchmarking Suite for Domain Adaptation on Time Series Data

Ragab¹,

Eldele²,

Tan³

et al. 2022

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

“…When it comes to ergonomic or ease of use-some systems require recordings from a smaller number of electrodes to minimize potential data analysis and to ease the montage of the device. Single-channel recordings are becoming more and more popular and some studies proved that these systems still can provide reliable information [76][77][78][79].…”

Section: Clinical Applicationsmentioning

confidence: 99%

Advanced Bioelectrical Signal Processing Methods: Past, Present and Future Approach—Part II: Brain Signals

Martínek

Ládrová

Šidiková

et al. 2021

Sensors

View full text Add to dashboard Cite

As it was mentioned in the previous part of this work (Part I)—the advanced signal processing methods are one of the quickest and the most dynamically developing scientific areas of biomedical engineering with their increasing usage in current clinical practice. In this paper, which is a Part II work—various innovative methods for the analysis of brain bioelectrical signals were presented and compared. It also describes both classical and advanced approaches for noise contamination removal such as among the others digital adaptive and non-adaptive filtering, signal decomposition methods based on blind source separation, and wavelet transform.

show abstract

“…On the other hand, RNNs have primarily been employed for the sequence encoder due to its well-established capability in sequential modelling. However, inter-epoch sequence modelling can also be accomplished by non-recursive architectures, such as dilated CNN [63], self-attention [67], and Transformer [24]. It should be noted that not all of the networks in the table are strictly sequence-to-sequence (e.g., DeepSleepNet [18], Stephansen et al [6], and GraphSleepNet [66]) or end-to-end (e.g., DeepSleepNet [18], Stephansen et al [6] and Sun et al [56]).…”

Section: The State-of-the-artmentioning

confidence: 99%

Automatic Sleep Staging of EEG Signals: Recent Development, Challenges, and Future Directions

Phan,

Mikkelsen

2021

Preprint

View full text Add to dashboard Cite

Modern deep learning holds a great potential to transform clinical practice on human sleep. Teaching a machine to carry out routine tasks would be a tremendous reduction in workload for clinicians. Sleep staging, a fundamental step in sleep practice, is a suitable task for this and will be the focus in this article. Recently, automatic sleep staging systems have been trained to mimic manual scoring, leading to similar performance to human sleep experts, at least on scoring of healthy subjects. Despite tremendous progress, we have not seen automatic sleep scoring adopted widely in clinical environments. This review aims to give a shared view of the authors on the most recent state-of-the-art development in automatic sleep staging, the challenges that still need to be addressed, and the future directions for automatic sleep scoring to achieve clinical value.

show abstract

An Attention-Based Deep Learning Approach for Sleep Stage Classification With Single-Channel EEG

Cited by 358 publications

References 36 publications

ADATIME: A Benchmarking Suite for Domain Adaptation on Time Series Data

ADATIME: A Benchmarking Suite for Domain Adaptation on Time Series Data

Advanced Bioelectrical Signal Processing Methods: Past, Present and Future Approach—Part II: Brain Signals

Automatic Sleep Staging of EEG Signals: Recent Development, Challenges, and Future Directions

Contact Info

Product

Resources

About