Analysis of EEG Signal to Classify Sleep Stages Using Machine Learning

Santaji, Sagar; Desai, Veena

doi:10.1007/s41782-020-00101-9

Cited by 28 publications

(18 citation statements)

References 26 publications

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“…As Figure 9 demonstrates, the use of SVM resulted in a wide range of performance in tasks including ER, MWL, and SD, when frequency-domain and time-frequency domain feature extraction methods, such as FFT and WT were chosen. RF had competitive performance in tasks that included ER, MWL, MI, SD, and SS, particularly in references [ 59 , 104 , 119 , 185 , 211 ]. However, in the domain associated with ND tasks, the RF algorithm did not perform as well as in other tasks.…”

Section: Resultsmentioning

confidence: 99%

“…Kuo and Liang [ 277 ] have proposed multiscale permutation entropy analysis for sleep scoring tasks along with the AR model and LDA, and have achieved a sensitivity of 89.1% for ten participants. Similarly, Santaji and Desai [ 59 ] have compared the performance of three classifiers: RF, SVM, and DT. 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.…”

Section: Discussionmentioning

confidence: 99%

“…Wavelet transform (WT) [ 58 , 59 , 60 , 61 ] is a spectral estimation method that converts a time-domain signal into a time and frequency domain signal. After decomposition of wavelet transformation on EEG signals and during artifact removal, WT localizes the features and maintains them during the filtering process by defining a threshold for the elimination of noise signals.…”

Section: Theoretical Backgroundmentioning

confidence: 99%

“… 62 channels Own database CSP SVM Accuracy = 93.5 [ 226 ] 2008 ER 10 subj. Own database Statistics SVM ANNs NB Accuracy = 80 [ 59 ] 2020 SS 125 subj. Sleep-EDF database Entropy RF SVM DT Accuracy = 97.8 [ 180 ] 2018 SD 25 subj.…”

Section: Table A1mentioning

confidence: 99%

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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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Section: Resultsmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Theoretical Backgroundmentioning

confidence: 99%

Section: Table A1mentioning

confidence: 99%

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

et al. 2021

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“…Santaji and Desai [ 13 ] proposed a method for sleep stage classification utilizing machine learning techniques to analyze electroencephalogram (EEG) signals over a 10-s time window. The study deployed decision tree, support vector machine, and random forest models that were used to extract and train on statistical characteristics with varying percentages of the testing dataset with the random forest model revealing a 97.8% accuracy score.…”

Section: Literature Reviewmentioning

confidence: 99%

Automated Analysis of Sleep Study Parameters Using Signal Processing and Artificial Intelligence

Sohaib

Ghaffar

Shin

et al. 2022

IJERPH

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An automated sleep stage categorization can readily face noise-contaminated EEG recordings, just as other signal processing applications. Therefore, the denoising of the contaminated signals is inevitable to ensure a reliable analysis of the EEG signals. In this research work, an empirical mode decomposition is used in combination with stacked autoencoders to conduct automatic sleep stage classification with reliable analytical performance. Due to the decomposition of the composite signal into several intrinsic mode functions, empirical mode decomposition offers an effective solution for denoising non-stationary signals such as EEG. Preliminary results showed that through these intrinsic modes, a signal with a high signal-to-noise ratio can be obtained, which can be used for further analysis with confidence. Therefore, later, when statistical features were extracted from the denoised signals and were classified using stacked autoencoders, improved results were obtained for Stage 1, Stage 2, Stage 3, Stage 4, and REM stage EEG signals using this combination.

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