Sleep Stage Scoring of Single-Channel EEG Signal based on RUSBoost Classifier

Sheykhivand, Sobhan; Rezaii, Tohid Yousefi; Farzamnia, Ali; Vazifehkhahi, Masoud

doi:10.1109/iicaiet.2018.8638465

Cited by 12 publications

(12 citation statements)

References 24 publications

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“…In sleep disorder diagnosis, the authors in [57][58][59][60], used the RUSBoosted Trees as classifier to dissociate the six sleep stages in sleep spindles detection from single-channel of EEG signal. In the former approach, the detection of sleep spindles was burdensome because the amount of sleep spindles was small compared with the entire EEG data, so This work is licensed under a Creative Commons Attribution 4.0 License.…”

Section: Research Contributionsmentioning

confidence: 99%

Improving Outcome Prediction for Traumatic Brain Injury From Imbalanced Datasets Using RUSBoosted Trees on Electroencephalography Spectral Power

Noor¹,

Ibrahim²,

Lah³

et al. 2021

IEEE Access

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Reliable prediction of traumatic brain injury (TBI) outcomes based on machine learning (ML) that is derived from quantitative electroencephalography (EEG) features has renewed interest in recent years. Nevertheless, the approach has suffered from imbalanced datasets. Hence, to get a reliable predictive model for predicting outcomes, specifically in a high proportion of moderate TBI with good outcomes, could be challenging. This work proposes an improved outcome predictive model that combines the absolute power spectral density (PSD) as input features for training random under-sampling boosting decision trees (RUSBoosted Trees) as a classifier. Resting-state, eyes-closed EEG data were obtained from 27 moderate TBI patients with follow-up visits. Patient outcome at 4-10 weeks to 12-month was dichotomized based on the Glasgow Outcome Scale as poor (GOS score ≤ 4) and good outcomes (GOS score = 5). The predictive values of absolute PSD from five frequency bands: δ (0.5-4Hz), θ (4-7Hz), α (7-13Hz), β (13-30Hz) and γ (30-100Hz) were evaluated to identify the most informative predictors for reliable prediction outcomes. RUSBoosted Trees performed best at discriminating patients into two outcomes categories (G-Mean = 92.95%, TP rate =100%, TN rate = 86.4%) of absolute PSD in δ and γ bands, which was excellent compared to the other state-of-the-art methods. The highest area under the curve (AUC) of absolute PSD in δ (AUC δ = 0.97) and γ (AUC γ = 0.95) revealed their predictive values as robust prognostic markers for prediction outcomes. The RUSBoosted Trees presents a promising result in prognosis prediction of highly imbalanced data, making it an accessible prediction tool for clinical decision-making, unlike the black-box approaches.

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Section: Research Contributionsmentioning

confidence: 99%

Improving Outcome Prediction for Traumatic Brain Injury From Imbalanced Datasets Using RUSBoosted Trees on Electroencephalography Spectral Power

Noor¹,

Ibrahim²,

Lah³

et al. 2021

IEEE Access

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“…In order to achieve better results from the BPNN model, the number of layers and hyperparameters are adjusted by different data types. The parameters of the minimum, maximum, skewness, crest factor, variance, root mean square (RMS), mean, and kurtosis are chosen as the manual features of the time domain (time features) [47]. The testing accuracy of the different methods based on the feature learning from raw data and the manual features are presented in Table 6, where the result of the proposed CBWRLSU method is marked in bold.…”

Section: Simulation Resultsmentioning

confidence: 99%

Automatic Identification of Epileptic Seizures From EEG Signals Using Sparse Representation-Based Classification

et al. 2020

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Identifying seizure activities in non-stationary electroencephalography (EEG) is a challenging task since it is time-consuming, burdensome, and dependent on expensive human resources and subject to error and bias. A computerized seizure identification scheme can eradicate the above problems, assist clinicians, and benefit epilepsy research. So far, several attempts were made to develop automatic systems to help neurophysiologists accurately identify epileptic seizures. In this research, a fully automated system is presented to automatically detect the various states of the epileptic seizure. This study is based on sparse representation-based classification (SRC) theory and the proposed dictionary learning using electroencephalogram (EEG) signals. Furthermore, this work does not require additional preprocessing and extraction of features, which is common in the existing methods. This study reached the sensitivity, specificity, and accuracy of 100% in 8 out of 9 scenarios. It is also robust to the measurement noise of level as much as 0 dB. Compared to state-of-the-art algorithms and other common methods, our method outperformed them in terms of sensitivity, specificity, and accuracy. Moreover, it includes the most comprehensive scenarios for epileptic seizure detection, including different combinations of 2 to 5 class scenarios. The proposed automatic identification of epileptic seizures method can reduce the burden on medical professionals in analyzing large data through visual inspection as well as in deprived societies suffering from a shortage of functional magnetic resonance imaging (fMRI) equipment and specialized physician.

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“…CNN has two phases including feed-forward and BackPropagation (BP) phases [47] . It has three essential layers including Fully Connected (FC), convolution, and pooling layers [48] . The convolution layer output is known as the feature mapping.…”

Section: Methodsmentioning

confidence: 99%

COVID-19 detection using chest X-ray images based on a developed deep neural network

et al. 2022

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Sleep Stage Scoring of Single-Channel EEG Signal based on RUSBoost Classifier

Cited by 12 publications

References 24 publications

Improving Outcome Prediction for Traumatic Brain Injury From Imbalanced Datasets Using RUSBoosted Trees on Electroencephalography Spectral Power

Improving Outcome Prediction for Traumatic Brain Injury From Imbalanced Datasets Using RUSBoosted Trees on Electroencephalography Spectral Power

Automatic Identification of Epileptic Seizures From EEG Signals Using Sparse Representation-Based Classification

COVID-19 detection using chest X-ray images based on a developed deep neural network

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