FIGURE 5 - available via license: Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International
Content may be subject to copyright.
![Positions of EEG electrodes according to the 10-20 electrode system (Redrawn from [27]).](publication/369459760/figure/fig3/AS:11431281129527982@1679576938769/Positions-of-EEG-electrodes-according-to-the-10-20-electrode-system-Redrawn-from-27.png)
Source publication
Existing automatic sleep stage detection methods predominantly use convolutional neural network classifiers (CNNs) trained on features extracted from single-modality signals such as electroencephalograms (EEG). On the other hand, multimodal approaches propose very complexly stacked network structures with multiple CNN branches merged by a fully con...
Context in source publication
Context 1
... labeled with six sleep stage categories: wake (W), sleep sub-stages (S1, S2, S3, S4), and rapid eye movement (R). Each modality was represented by samples captured by groups of sensors. In the case of EEG, the recordings included signals recorded from 16 electrodes placed at different positions on the patient's head [3], [27], as illustrated in Fig. 5. The ECG signals were collected from two electrodes, ECG1 and ECG2, placed on the patient's chest [3], [28]. Finally, the EMG samples included EMG measurements of the submentalis muscle and bilateral anterior tibial EMG [3], [29]. ...
Similar publications
Study Objectives
U-Sleep is a publicly-available automated sleep stager, but has not been independently validated using pediatric data. We aimed to a) test the hypothesis that U-Sleep performance is equivalent to trained humans, using a concordance dataset of 50 pediatric polysomnogram excerpts scored by multiple trained scorers, and b) identify cl...
The influence of the coupled electroencephalography (EEG) signal in electrooculography (EOG) on EOG-based automatic sleep staging has been ignored. Since the EOG and prefrontal EEG are collected at close range, it is not clear whether EEG couples in EOG or not, and whether or not the EOG signal can achieve good sleep staging results due to its intr...
Sleep disturbances are common among critically ill patients, significantly impacting recovery and overall health outcomes. Sleep in the Intensive Care Unit (ICU) is often fragmented, with reduced deeper sleep stages and disrupted circadian rhythms. This review explores a range of tools for assessing sleep quality in ICU settings, including both obj...
Driver drowsiness is a widely recognized cause of motor vehicle accidents. Therefore, a reduction in drowsy driving crashes is required. Many studies evaluating the crash risk of drowsy driving and developing drowsiness detection systems, have used observer rating of drowsiness (ORD) as a reference standard (i.e. ground truth) of drowsiness. ORD is...
Objective:
Epileptic encephalopathy with spike-wave activation in sleep (EE-SWAS) is a challenging neurodevelopmental disease characterized by abundant epileptiform spikes during non-rapid eye movement (NREM) sleep accompanied by cognitive dysfunction. The mechanism of cognitive dysfunction is unknown, but treatment with high-dose diazepam may imp...
Citations
... The OCNN model achieved accuracies of 88.4% and 87.6% with the UCD and MIT-BIH datasets, respectively. In [19], the author employed multi-modal classification and decision-making systems for sleep staging, incorporating an external neural network. The experimental work utilized the CAP sleep dataset, and the results indicated that the model performed well compared to an individual CNN model. ...
The goal is to enhance an automated sleep staging system's performance by leveraging the diverse signals captured through multi-modal polysomnography recordings. Three modalities of PSG signals, namely electroencephalogram (EEG), electrooculogram (EOG), and electromyogram (EMG), were considered to obtain the optimal fusions of the PSG signals, where 63 features were extracted. These include frequency-based, time-based, statistical-based, entropy-based, and non-linear-based features. We adopted the ReliefF (ReF) feature selection algorithms to find the suitable parts for each signal and superposition of PSG signals. Twelve top features were selected while correlated with the extracted feature sets' sleep stages. The selected features were fed into the AdaBoost with Random Forest (ADB + RF) classifier to validate the chosen segments and classify the sleep stages. This study's experiments were investigated by obtaining two testing schemes: epoch-wise testing and subject-wise testing. The suggested research was conducted using three publicly available datasets: ISRUC-Sleep subgroup1 (ISRUC-SG1), sleep-EDF(S-EDF), Physio bank CAP sleep database (PB-CAPSDB), and S-EDF-78 respectively. This work demonstrated that the proposed fusion strategy overestimates the common individual usage of PSG signals.
