Intra- and inter-epoch temporal context network (IITNet) using sub-epoch features for automatic sleep scoring on raw single-channel EEG

Seo, Hye‐Sook; Back, Seunghyeok; Lee, Seongju; Park, Deokhwan; Kim, Tae Il; Lee, Kyoobin

doi:10.1016/j.bspc.2020.102037

Cited by 152 publications

(92 citation statements)

References 38 publications

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“…Table V provides detailed comparison information about recent studies and the proposed method on the SHHS dataset. It shows that the proposed framework can achieve higher ACC and K using raw single-channel C4/A1 EEG compared to approaches using hand-crafted features as input [12] or multi-channel PSG data [27], [30] or the single-channel EEG [32], [33]. Similarly, Table VI demonstrates that the proposed model outperforms state-of-the-art methods on the Sleep-EDF dataset.…”

Section: Performance Comparisonmentioning

confidence: 72%

SingleChannelNet: A Model for Automatic Sleep Stage Classification with Raw Single-Channel EEG

Zhou

Zhang

et al. 2020

Preprint

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In diagnosing sleep disorders, sleep stage classification is a very essential yet time-consuming process. Most of the existing state-of-the-art approaches rely on hand-crafted features and multimodality polysomnography (PSG) data, where prior knowledge is compulsory and high computation cost can be expected. Besides, few studies are able to obtain high accuracy sleep staging using raw single-channel electroencephalogram (EEG). To overcome these shortcomings, this paper proposes an end-to-end framework with a deep neural network, namely SingleChannelNet, for automatic sleep stage classification based on raw single-channel EEG. The proposed model utilizes a 90s epoch as the textual input and employs two multi-convolution blocks and several max-average pooling layers to learn different scales of feature representations. To demonstrate the efficiency of the proposed model, we evaluate our model using different raw single-channel EEGs (C4/A1 and Fpz-Cz) on two different datasets (SHHS and Sleep-EDF datasets). Experimental results show that the proposed architecture can achieve better overall accuracy and Cohens kappa (SHHS: 89.2%-84.8%, Sleep-EDF: 89.4%-85%) compared with state-of-the-art approaches. Additionally, the proposed model can learn features automatically for sleep stage classification using different single-channel EEGs with distinct sampling rates from different datasets without using any hand-engineered features.

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Section: Performance Comparisonmentioning

confidence: 72%

SingleChannelNet: A Model for Automatic Sleep Stage Classification with Raw Single-Channel EEG

Zhou

Zhang

et al. 2020

Preprint

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“…Every 11 epochs were then grouped into a sequence because when the sequence length is 10 or more, the accuracy improvement is saturated according to the previous literatures. 4 , 5 Normalization was done using the mean and standard deviation calculated with the Welford’s algorithm, which was conducted because of the huge dataset size (600 GB). Finally, the entire dataset was divided into train, validation, and test sets with a ratio of 70:15:15 at the PSG level.…”

Section: Methodsmentioning

confidence: 99%

Confidence-Based Framework Using Deep Learning for Automated Sleep Stage Scoring

Hong¹,

Lee²,

Reyes³

et al. 2021

NSS

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Study Objectives Automated sleep stage scoring is not yet vigorously used in practice because of the black-box nature and the risk of wrong predictions. The objective of this study was to introduce a confidence-based framework to detect the possibly wrong predictions that would inform clinicians about which epochs would require a manual review and investigate the potential to improve accuracy for automated sleep stage scoring. Methods We used 702 polysomnography studies from a local clinical dataset (SNUBH dataset) and 2804 from an open dataset (SHHS dataset) for experiments. We adapted the state-of-the-art TinySleepNet architecture to train the classifier and modified the ConfidNet architecture to train an auxiliary confidence model. For the confidence model, we developed a novel method, Dropout Correct Rate (DCR), and the performance of it was compared with other existing methods. Results Confidence estimates (0.754) reflected accuracy (0.758) well in general. The best performance for differentiating correct and wrong predictions was shown when using the DCR method (AUROC: 0.812) compared to the existing approaches which largely failed to detect wrong predictions. By reviewing only 20% of epochs that received the lowest confidence values, the overall accuracy of sleep stage scoring was improved from 76% to 87%. For patients with reduced accuracy (ie, individuals with obesity or severe sleep apnea), the possible improvement range after applying confidence estimation was even greater. Conclusion To the best of our knowledge, this is the first study applying confidence estimation on automated sleep stage scoring. Reliable confidence estimates by the DCR method help screen out most of the wrong predictions, which would increase the reliability and interpretability of automated sleep stage scoring.

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“…This process will result in thousands of models that will be tested in 10 epochs in a certain time duration. Epoch is a hyperparameter that determines how many times the machine learning model will process the training data [26]. This research used a 10/10 scale epoch; 10 experiments were done to obtain the best model and feature prediction.…”

Section: Fig 3 Feyn Qlattice Modelmentioning

confidence: 99%

Linkage Detection of Features that Cause Stroke using Feyn Qlattice Machine Learning Model

Purwono

Ma’arif

Negara

et al. 2021

Jurnal Ilmiah Teknik Elektro Komputer Dan Informatika

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Stroke is a disease caused by brain tissue damage because of blockage in the cerebrovascular system that disrupts body sensory and motoric systems Stroke disease is one of the highest death cause in the world. Data collection from Electronic Health Records (EHR) is increasing and has been included in the health service big data. It can be processed and analyzed using machine learning to determine the risk group of stroke disease. Machine learning can be used as a predictor of stroke causes, while the predictor clarifies the influence of each cause factor of the disease. Our contribution in this research is to evaluate Feyn Qlattice machine learning models to detect the influence of stroke disease's main cause features. We attempt to obtain a correlation between features of the stroke disease, especially on the gender as a feature, whether any other features can influence the gender feature. This research utilizes 4908 data of the disease predictor using the Feyn Qlattice model. The result implies that gender highly impacts age and hypertension on stroke disease causes. Autorun in Feyn Qlattice model was run with ten epochs, resulting in 17596 test models at 57s. Query string parameter that was focused on age and hypertension features resulted in 1245 models at 4s. An increase of accuracy was found in training metrics from 0.723 to 0.732 and in testing metrics from 0.695 to 0.708. Evaluation results showed that the model is reasonably good as a predictor of stroke disease, indicated with blue lines of AUC in training and testing metrics close to ROC's left side peak curve.

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Intra- and inter-epoch temporal context network (IITNet) using sub-epoch features for automatic sleep scoring on raw single-channel EEG

Cited by 152 publications

References 38 publications

SingleChannelNet: A Model for Automatic Sleep Stage Classification with Raw Single-Channel EEG

SingleChannelNet: A Model for Automatic Sleep Stage Classification with Raw Single-Channel EEG

Confidence-Based Framework Using Deep Learning for Automated Sleep Stage Scoring

Linkage Detection of Features that Cause Stroke using Feyn Qlattice Machine Learning Model

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