2020
DOI: 10.1109/jbhi.2020.2978004
|View full text |Cite
|
Sign up to set email alerts
|

A Residual Based Attention Model for EEG Based Sleep Staging

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
2
1
1
1

Citation Types

0
53
0

Year Published

2020
2020
2024
2024

Publication Types

Select...
5
2
2

Relationship

0
9

Authors

Journals

citations
Cited by 123 publications
(53 citation statements)
references
References 31 publications
0
53
0
Order By: Relevance
“…However, the network structure was complex, and the recognition rate of the N1 period was low. In Qu et al (2020) used CNN to extract features and combined the attention mechanism and residual neural network for sleep staging; they obtained an accuracy of more than 0.84 and solved the problem of a large number of layers of deep learning-based methods. However, the accuracy rate was low, the network structure was complex, and the staging results fluctuated significantly.…”
Section: Discussionmentioning
confidence: 99%
“…However, the network structure was complex, and the recognition rate of the N1 period was low. In Qu et al (2020) used CNN to extract features and combined the attention mechanism and residual neural network for sleep staging; they obtained an accuracy of more than 0.84 and solved the problem of a large number of layers of deep learning-based methods. However, the accuracy rate was low, the network structure was complex, and the staging results fluctuated significantly.…”
Section: Discussionmentioning
confidence: 99%
“…Similarly, Table VI demonstrates that the proposed model outperforms state-of-the-art methods on the Sleep-EDF dataset. Some studies [4], [13], [43] extract features manually or multi-channel signals are used as input [6], [29] or some methods adopt single-channel EEG [31], [34], [35]. Considering Table V and Table VI, the proposed framework can achieve promising performance on SHHS and Sleep-EDF datasets.…”
Section: Performance Comparisonmentioning
confidence: 99%
“…In order to adapt to different PSG acquisition devices and individual differences of different subjects, EEG signals of each subject were normalized by using the 5-th and 95-th quantiles [34], as shown in Equation (11).…”
Section: Preprocessingmentioning
confidence: 99%
“…The method has been applied to the machine fault detection and achieved good results [33]. The sleep stage classification based on residual network also achieved good results [34]. But central and occipital EEG from right hemisphere, left and right EOG, and chin EMG channels were extracted from each PSG study.…”
Section: Introductionmentioning
confidence: 99%