TinySleepNet: An Efficient Deep Learning Model for Sleep Stage Scoring based on Raw Single-Channel EEG

Supratak, Akara; Guo, Yike

doi:10.1109/embc44109.2020.9176741

Cited by 140 publications

(91 citation statements)

References 9 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…There are many studies in automatic sleep stage classification methods based on multiple signals such as EEG, EOG, and EMG [28][29][30], or single-channel EEG [31][32][33]. The classification goal is often accomplished by statistical rules and deep learning technology.…”

Section: Methods In Diagnosis Of Docmentioning

confidence: 99%

A Systematic Review of Sleep in Patients with Disorders of Consciousness: From Diagnosis to Prognosis

Pan

Liu

et al. 2021

Brain Sciences

View full text Add to dashboard Cite

With the development of intensive care technology, the number of patients who survive acute severe brain injury has increased significantly. At present, it is difficult to diagnose the patients with disorders of consciousness (DOCs) because motor responses in these patients may be very limited and inconsistent. Electrophysiological criteria, such as event-related potentials or motor imagery, have also been studied to establish a diagnosis and prognosis based on command-following or active paradigms. However, the use of such task-based techniques in DOC patients is methodologically complex and requires careful analysis and interpretation. The present paper focuses on the analysis of sleep patterns for the evaluation of DOC and its relationships with diagnosis and prognosis outcomes. We discuss the concepts of sleep patterns in patients suffering from DOC, identification of this challenging population, and the prognostic value of sleep. The available literature on individuals in an unresponsive wakefulness syndrome (UWS) or minimally conscious state (MCS) following traumatic or nontraumatic severe brain injury is reviewed. We can distinguish patients with different levels of consciousness by studying sleep patients with DOC. Most MCS patients have sleep and wake alternations, sleep spindles and rapid eye movement (REM) sleep, while UWS patients have few EEG changes. A large number of sleep spindles and organized sleep–wake patterns predict better clinical outcomes. It is expected that this review will promote our understanding of sleep EEG in DOC.

show abstract

Section: Methods In Diagnosis Of Docmentioning

confidence: 99%

A Systematic Review of Sleep in Patients with Disorders of Consciousness: From Diagnosis to Prognosis

Pan

Liu

et al. 2021

Brain Sciences

View full text Add to dashboard Cite

show abstract

“…DeepSleepNet is a representative research that uses the CNN‒RNN framework, but it cannot extract detailed features in the sleep stage. TinySleepNet [ 22 ] is an improvement and upgrade to DeepSleepNet; a data enhancement scheme and simpler network framework are adopted. The results of this method in Table 4 are reproduced based on the original paper code on the half-hour W stage data before and after sleep dataset.…”

Section: Experimental Results and Analysismentioning

confidence: 99%

CCRRSleepNet: A Hybrid Relational Inductive Biases Network for Automatic Sleep Stage Classification on Raw Single-Channel EEG

Neng

2021

Brain Sciences

View full text Add to dashboard Cite

In the inference process of existing deep learning models, it is usually necessary to process the input data level-wise, and impose a corresponding relational inductive bias on each level. This kind of relational inductive bias determines the theoretical performance upper limit of the deep learning method. In the field of sleep stage classification, only a single relational inductive bias is adopted at the same level in the mainstream methods based on deep learning. This will make the feature extraction method of deep learning incomplete and limit the performance of the method. In view of the above problems, a novel deep learning model based on hybrid relational inductive biases is proposed in this paper. It is called CCRRSleepNet. The model divides the single channel Electroencephalogram (EEG) data into three levels: frame, epoch, and sequence. It applies hybrid relational inductive biases from many aspects based on three levels. Meanwhile, multiscale atrous convolution block (MSACB) is adopted in CCRRSleepNet to learn the features of different attributes. However, in practice, the actual performance of the deep learning model depends on the nonrelational inductive biases, so a variety of matching nonrelational inductive biases are adopted in this paper to optimize CCRRSleepNet. The CCRRSleepNet is tested on the Fpz-Cz and Pz-Oz channel data of the Sleep-EDF dataset. The experimental results show that the method proposed in this paper is superior to many existing methods.

show abstract

“…Furthermore, Mousavi et al ( 2019 ) conducted a sliding window-based DA technique to increase the number of training EEG samples for sleep stage recognition. Supratak and Guo ( 2020 ) also focused on the sleep stage classification task but augmented the training dataset using the shifting technique. Finally, Sakai et al ( 2017 ) used shifting to augment their cognition classification task, classifying EEG signals acquired at motivated status and unmotivated statuses.…”

Section: Advances In Data Augmentationmentioning

confidence: 99%

“…Zhao X. et al ( 2020 ) also effectively acquired artificial ictal EEG samples with a discrete cosine transform (DCT)-based spectral transformation. Finally, Fan et al ( 2020 ) and Supratak and Guo ( 2020 ) performed the temporal segmentation and recombination-based DA technique to increase the training data for the sleep stage classification.…”

Section: Advances In Data Augmentationmentioning

confidence: 99%

A Survey on Deep Learning-Based Short/Zero-Calibration Approaches for EEG-Based Brain–Computer Interfaces

Jeon

Jeong

et al. 2021

Front. Hum. Neurosci.

View full text Add to dashboard Cite

Brain–computer interfaces (BCIs) utilizing machine learning techniques are an emerging technology that enables a communication pathway between a user and an external system, such as a computer. Owing to its practicality, electroencephalography (EEG) is one of the most widely used measurements for BCI. However, EEG has complex patterns and EEG-based BCIs mostly involve a cost/time-consuming calibration phase; thus, acquiring sufficient EEG data is rarely possible. Recently, deep learning (DL) has had a theoretical/practical impact on BCI research because of its use in learning representations of complex patterns inherent in EEG. Moreover, algorithmic advances in DL facilitate short/zero-calibration in BCI, thereby suppressing the data acquisition phase. Those advancements include data augmentation (DA), increasing the number of training samples without acquiring additional data, and transfer learning (TL), taking advantage of representative knowledge obtained from one dataset to address the so-called data insufficiency problem in other datasets. In this study, we review DL-based short/zero-calibration methods for BCI. Further, we elaborate methodological/algorithmic trends, highlight intriguing approaches in the literature, and discuss directions for further research. In particular, we search for generative model-based and geometric manipulation-based DA methods. Additionally, we categorize TL techniques in DL-based BCIs into explicit and implicit methods. Our systematization reveals advances in the DA and TL methods. Among the studies reviewed herein, ~45% of DA studies used generative model-based techniques, whereas ~45% of TL studies used explicit knowledge transferring strategy. Moreover, based on our literature review, we recommend an appropriate DA strategy for DL-based BCIs and discuss trends of TLs used in DL-based BCIs.

show abstract

TinySleepNet: An Efficient Deep Learning Model for Sleep Stage Scoring based on Raw Single-Channel EEG

Cited by 140 publications

References 9 publications

A Systematic Review of Sleep in Patients with Disorders of Consciousness: From Diagnosis to Prognosis

A Systematic Review of Sleep in Patients with Disorders of Consciousness: From Diagnosis to Prognosis

CCRRSleepNet: A Hybrid Relational Inductive Biases Network for Automatic Sleep Stage Classification on Raw Single-Channel EEG

A Survey on Deep Learning-Based Short/Zero-Calibration Approaches for EEG-Based Brain–Computer Interfaces

Contact Info

Product

Resources

About