A Comparative Study on Classification of Sleep Stage Based on EEG Signals Using Feature Selection and Classification Algorithms (original) (raw)
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A Novel Method for Automated Diagnosis of Epilepsy using Complex-Valued Classifiers
IEEE journal of biomedical and health informatics, 2015
The study reported herein proposes a new method for the diagnosis of epilepsy from electroencephalography (EEG) signals based on complex classifiers. To carry out the study, first the features of EEG data are extracted using a dual-tree complex wavelet transformation (DTCWT) at different levels of granularity to obtain size reduction. In subsequent phases, five features (based on statistical measurements-maximum value, minimum value, arithmetic mean, standard deviation, median value) are obtained by using the feature vectors, and are presented as the input dimension to the complex-valued neural networks (CVANN). The evaluation of the proposed method is conducted using the k-fold cross-validation methodology, reporting on classification accuracy, sensitivity and specificity. The proposed method is tested using a benchmark EEG dataset and high accuracy rates were obtained. The stated results show that the proposed method can be used to design an accurate classification system for epil...
Neurocomputing, 2016
Sleep staging is a significant step in the diagnosis and treatment of sleep disorders. Sleep scoring is a time-consuming and difficult process. Given that sleep scoring requires expert knowledge, it is generally undertaken by sleep experts. In this study, a new hybrid machine learning method consisting of complex-valued nonlinear features (CVNF) and a complex-valued neural network (CVANN) has been presented for automatic sleep scoring using single channel electroencephalography (EEG) signals. First of all, we should note that in this context, nine nonlinear features have been obtained as those are often preferred for the classification of EEG signals. These obtained features were then converted into a complex-valued number format using a phase encoding method. In this way, a new complex-valued feature set was obtained for sleep scoring. The obtained attributes have been presented as input to the CVANN algorithm. We have used a number of different statistical parameters during the evaluation process. The results that have been obtained are based on two sleep standards: Rechtschaffen & Kales (R&K) and American Academy of Sleep Medicine (AASM). Finally, a 91.57% accuracy rate was obtained according to R&K standard; a 93.84% accuracy rate was obtained according to the AASM standard using the proposed method. We therefore observed that the proposed method is promising in terms of the sleep scoring.
A new complex-valued intelligent system for automated epilepsy diagnosis using EEG signals
The study proposes a new method for the diagnosis of epilepsy from EEG signals based on complex classifiers. First of all, 8 effective feature extraction algorithms were used in order to identify meaningful information from EEG signals. In later phases, 8 feature values were presented as introduction to the complex valued neural network (CVANN). Two different classification experiments were undertaken with the help of the developed model: 1) the classification of healthy volunteers, epilepsy patients during seizure and epilepsy patients during a seizure-free interval, 2) the classification of epilepsy patients during seizure and healthy volunteers. The evolution of the proposed system is conducted using k-fold cross-validation, classification accuracy (CA), sensitivity and specificity values. The proposed approach identified EEG signals with 97.01% and 100% accuracy in the first and second experiments respectively. The stated results show that the proposed method is capable of designing a new intelligent assistance diagnostic system.
Classification of EEG signals for epileptic seizure prediction using ANN
2012
In this paper, we developed a model for classification of EEG signals. The aim of the study is to determine whether this model can be used for epileptic seizure prediction if "pre-ictal" stages were successfully detected. We analyzed long-term Freiburg EEG data. Each of 21 patients contains datasets called "ictal" (seizure) and "inter-ictal" (seizure-free). We extracted 4096-samples (or 16 seconds) long segments from both datasets of each patient. These segments were decomposed into time-frequency representations using Discrete Wavelet Transform (DWT). The statistical features from the DWT sub-bands of EEG segments were calculated and fed as inputs to Multilayer Perceptron (MLP) and Radial Basis Function (RBF) network classifiers using 10-fold cross validation. We also applied multiscale PCA (MSPCA) de-noising method to determine if it can further enhance the classifiers' performance. MLP-based approach outperformed RBF classifier with or without MSPCA, which significantly improved the classification accuracy of both classifiers. The proposed MLP-approach with MSPCAachieved a classification accuracy of 95.09%. We showed that a high classification accuracy of EEG signals can be accomplished in cases when additional "pre-ictal" class is introduced. Therefore, the proposed approach may become an efficient tool to predict epileptic seizures from EEG recordings.
IJERT-Detection of Epileptic Activity In The Human EEG-Based Wavelet Transforms and SVM
International Journal of Engineering Research and Technology (IJERT), 2013
https://www.ijert.org/detection-of-epileptic-activity-in-the-human-eeg-based-wavelet-transforms-and-svm https://www.ijert.org/research/detection-of-epileptic-activity-in-the-human-eeg-based-wavelet-transforms-and-svm-IJERTV2IS1006.pdf Epilepsy is a chronic neurological disorder which is identified by successive unexpected seizures. Electroencephalogram (EEG) is the electrical signal of brain which contains valuable information about its normal or epileptic activity. In this work EEG and its frequency sub-bands have been analyzed to detect epileptic seizures. A discrete wavelet transform (DWT) has been applied to decompose the EEG into its sub bands. Statistical features Energy, Covariance are calculated for each sub-band. The extracted features are applied to Feed Forward Neural Network For system for classifications got classification accuracy of 98%.
SN Computer Science, 2021
Sleep staging is one of the important methods for the diagnosis of the different types of sleep-related diseases. Manual inspection of sleep scoring is a very time-consuming process, labor-intensive, and requires more human interpretations, which may produce biased results. Therefore, in this paper, we propose an efficient automated sleep staging system to improve sleep staging accuracy. In this work, we extracted both linear and non-linear properties from the input signal. Next to that, a set of optimal features was selected from the extracted feature vector by using a feature reduction technique based on the ReliefF weight algorithm. Finally, the selected features were classified through four machine learning techniques like support vector machine, K-nearest neighbor, decision tree, and random forest. The proposed methodology performed using dualchannel EEG signals from the ISRUC-Sleep dataset under the AASM sleep scoring rules. The performance of the proposed methodology compared with the existing similar methods. In this work, we considered the 10-Fold cross validation strategy; our proposed methods reported the highest classification accuracy of 91.67% with the C4-A1 channel, and 93.8% with the O2-A1 channel using the Random forest classification model. The result of the proposed methodology outperformed the earlier contribution for two-class sleep states classification. The proposed dual-channel sleep staging method can be helpful for the clinicians during the sleep scoring and treatment for the different sleep-related diseases.
Traitement du Signal
Epilepsy is one of the earnest neurological disorders that require further social attention. Based on the International League Against Epilepsy (ILAE), which classifies the epilepsy term as a number of several seizures that occur in the brain. Electroencephalography (EEG) is considered our brain window to the electrical activity. It is a significant device used for diagnosing multiple brain disorders such as Epilepsy. Moreover, this study used data from Temple University Hospital Seizure Corpus (TUH), which represents an accurate description of the clinical cases for five types of epileptic seizures. Initially, to extract information from EEG signals, three types of feature extraction have been used namely Fast Fourier Transform, Entropy, and Approximate Entropy. Due to the high degree of variance of EEG signals, we implemented a band-pass filter to divide the signals into sub-bands called delta rhythm (0.1-4Hz), theta rhythm (5-9Hz), alpha rhythm (10-14Hz), beta rhythm (15-31Hz), and gamma rhythm (32-100). The feature extraction outcome underwent normalization techniques and was used as input for the classifiers. Support Vector Machine (SVM), Decision Tree (DT), Naive Bayes (NB), and K-Nearest Neighbor (KNN) classifier have implemented in order to classify (1) second epoch length window. In the first scenario, we applied the FFT features to the classifiers, the results showed that SVM obtained the highest value compared to the other classifiers with 96% accuracy, whereas KNN was 92% and the DT and NB were 76% and 67%, respectively. The second scenario was applying entropy features to the classifiers, the results of classification were 91% for SVM and 88% for KNN, while the DT and NB were 76% and 67%, respectively. The last scenario was ApEn, which shows that SVM still gains the highest value, which was 83%, and 76% for KNN, where the DT and NB were 65% and 69%, respectively. From the aforementioned results, we deduced that SVM achieved the best accuracy when applied with the three feature extractions.
Classification of EEG using PCA, ICA and Neural Network
2012
clinicians and researchers alike buried in a sea of EEG paper records. The advent of computers and the technologies associated with them has made it possible to effectively apply a host of methods to quantify EEG changes [4]. The EEG spectrum contains some characteristic waveforms that fall primarily within four frequency bands: delta (<4 Hz), theta (4-8 Hz), alpha (8-13 Hz) and beta (13-30 Hz). Since the EEG signals are non-stationary, the parametric methods are not suitable for frequency decomposition of these signals. A powerful method was proposed in the late 1980s to perform time-scale analysis of signals: the wavelet transforms (WT). This method provides a unified framework for different techniques that have been developed for various applications. Since the WT is appropriate for analysis of non-stationary signals and this represents a major advantage over spectral analysis, it is well suited to locating transient events, which may occur during epileptic seizures. Wavelet’s...
Journal of Healthcare Engineering, 2022
Epileptic patients suffer from an epileptic brain seizure caused by the temporary and unpredicted electrical interruption. Conventionally, the electroencephalogram (EEG) signals are manually studied by medical practitioners as it records the electrical activities from the brain. This technique consumes a lot of time, and the outputs are unreliable. In a bid to address this problem, a new structure for detecting an epileptic seizure is proposed in this study. The EEG signals obtained from the University of Bonn, Germany, and real-time medical records from the Senthil Multispecialty Hospital, India, were used. These signals were disintegrated into six frequency subbands that employed discrete wavelet transform (DWT) and extracted twelve statistical functions. In particular, seven best features were identified and further fed into k-Nearest Neighbor (kNN), naïve Bayes, Support Vector Machine (SVM), and Decision Tree classifiers for two-type and three-type classifications. Six statistic...