Classification of Brainwaves for Sleep Stages by High-Dimensional FFT Features from EEG Signals

Delimayanti, Mera Kartika; Purnama, Bedy; Nguyen, Nhat Minh; Faisal, Mohammad Reza; Mahmudah, Kunti Robiatul; Indriani, Fatma; Kubo, Mamoru; Satou, Kenji

doi:10.3390/app10051797

Cited by 56 publications

(25 citation statements)

References 25 publications

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“…As shown in Table 4, a variety of deep learning networks achieved good performance. 10,[29][30][31][32][33][34] With combined channels of EEG and EOG, our results were at a leading level. Recently, researchers have begun to consider how to design algorithms to be small, efficient, and robust.…”

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confidence: 56%

Automatic Sleep Stage Classification of Children with Sleep-Disordered Breathing Using the Modularized Network

Wang¹,

Lin²,

Li³

et al. 2021

NSS

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To develop an automatic sleep stage analysis model for children and evaluate the effect of the model on the diagnosis of sleep-disordered breathing (SDB). Patients and Methods: Three hundred and forty-four SDB patients aged between 2 to 18 years who completed polysomnography (PSG) to assess the severity of the disease were enrolled in this study. We developed deep neural networks to stage sleep from electroencephalography (EEG), electrooculography (EOG) and electromyogram (EMG). The model performance was estimated by accuracy, precision, recall, F1-score, and Cohen's Kappa coefficient (ĸ). And we compared the difference in calculation of sleep parameters among the technicians, the model ensemble, and the single-channel EEG model. Results:The numbers of raw data divided into training, validation, and testing were 240, 36, and 68, respectively. The best performance appeared in the model ensemble of which the accuracy was 83.36% (ĸ=0.7817) in 5-stages, and the accuracy was 96.76% (ĸ=0.8236) in 2-stages. The single-channel EEG model showed the classification satisfyingly as well. There was no significant difference in TST, SE, SOL, time in W, time in N1+N2, time in N3, and OAHI between technician and the model (P>0.05). On the datasets from sleep-EDF-13 and sleep-EDF-18, the average classification accuracies achieved were 92.76% and 91.94% in 5-stages by using the proposed method, respectively. Conclusion: This research established the model for pediatric automatic sleep stage classification with satisfying reliability and generalizability. In addition, it could be applied for calculating quantitative sleep parameters and evaluating the severity of SDB.

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confidence: 56%

Automatic Sleep Stage Classification of Children with Sleep-Disordered Breathing Using the Modularized Network

Wang¹,

Lin²,

Li³

et al. 2021

NSS

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“…The RF algorithm, when trained with extracted statistical features outperformed the other two algorithms in classification, with high specificity, sensitivity, and accuracy of 96.35%, 96.12%, and 97.8%, respectively. Delimayanti et al [ 204 ] have applied FFT to elicit high-dimensional features and enhanced the classification performance of SS tasks by using the SVM algorithm with RBF kernel, and have achieved an average accuracy of 87.84%. Moreover, the performance of the AdaBoost classifier has been analyzed in reference [ 203 , 278 ].…”

Section: Discussionmentioning

confidence: 99%

“… Sleep-EDF database Entropy RF SVM DT Accuracy = 97.8 [ 180 ] 2018 SD 25 subj. 100 channels BONN database PCA RBF-SVM Accuracy = 100 [ 185 ] 2021 SD BONN database VMD RF Accuracy = 98.7–100 [ 204 ] 2020 SS 2 channels Sleep-EDF database FFT RBF-SVM Accuracy = 87.8 [ 196 ] 2020 MWL 36 subj. 19 channels PhysioBank database FFT KNN SVM Accuracy = 99.4 [ 154 ] 2020 Stress 33 subj.…”

Section: Table A1mentioning

confidence: 99%

Neural Decoding of EEG Signals with Machine Learning: A Systematic Review

et al. 2021

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Electroencephalography (EEG) is a non-invasive technique used to record the brain’s evoked and induced electrical activity from the scalp. Artificial intelligence, particularly machine learning (ML) and deep learning (DL) algorithms, are increasingly being applied to EEG data for pattern analysis, group membership classification, and brain-computer interface purposes. This study aimed to systematically review recent advances in ML and DL supervised models for decoding and classifying EEG signals. Moreover, this article provides a comprehensive review of the state-of-the-art techniques used for EEG signal preprocessing and feature extraction. To this end, several academic databases were searched to explore relevant studies from the year 2000 to the present. Our results showed that the application of ML and DL in both mental workload and motor imagery tasks has received substantial attention in recent years. A total of 75% of DL studies applied convolutional neural networks with various learning algorithms, and 36% of ML studies achieved competitive accuracy by using a support vector machine algorithm. Wavelet transform was found to be the most common feature extraction method used for all types of tasks. We further examined the specific feature extraction methods and end classifier recommendations discovered in this systematic review.

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“…Decomposes the signal into a series of sine-based functions, the absolute values of the Fourier Transform represent the signals' frequency behavior [23][24][25]. In this work, Fourier Layers were introduced as a starting point for the convolutional neural networks so that the features can be extracted from a different representation that of the data which appear to be more useful compared to the time domain features.…”

Section: Fourier Transformmentioning

confidence: 99%

Schizophrenia Diagnosis via FFT and Wavelet Convolutional Neural Networks utilizing EEG signals

Saeedi

Mohammadi

2022

Preprint

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BackgroundSchizophrenia is a chronic mental illness in which a person's perception of reality is distorted. Early diagnosis can help to manage symptoms and increase long-term treatment. The electroencephalogram (EEG) is now used to diagnose certain mental disorders. MethodIn this paper, we developed an artificial intelligence methodology built on deep convolutional neural networks with specialized layers. In the first phase, we used the Gramian Angular Field (GAF) including two methods: Gramian Angular Summation Field (GASF) and Gramian Angular Difference Field (GADF) to represent the EEG signals as various types of images. Then, well-known CNN architectures includes of Transformer CNN-LSTM and two new custom architectures which utilizing two-dimensional Fast Fourier transform layers (CNN-FFT) and wavelet transform layers (CNN-Wavelet) are preformed to extract useful information from the data. These layers allow automated feature extraction from EEG representation in the time and frequency domains. ResultsCNN-FFT and Transformer models derive most valuable features from signals based on the findings. CNN-FFT obtained the highest accuracy of 99.04 percent. Transformer, which has a 98.32 percent accuracy rate, also performs admirably. ConclusionThis experiment outperformed other previous studies. Consequently, the strategy can aid medical practitioners in automated detection and early treatment of schizophrenia. The code of the work is also publicly available on github: https://github.com/i1idan/schizophreniadiagnosis-eeg-signals

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Classification of Brainwaves for Sleep Stages by High-Dimensional FFT Features from EEG Signals

Cited by 56 publications

References 25 publications

Automatic Sleep Stage Classification of Children with Sleep-Disordered Breathing Using the Modularized Network

Automatic Sleep Stage Classification of Children with Sleep-Disordered Breathing Using the Modularized Network

Neural Decoding of EEG Signals with Machine Learning: A Systematic Review

Schizophrenia Diagnosis via FFT and Wavelet Convolutional Neural Networks utilizing EEG signals

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