A Unified Framework and Method for EEG-Based Early Epileptic Seizure Detection and Epilepsy Diagnosis

Chen, Zixu; Lu, Guoliang; Xie, Zhijun; Shang, Wenjing

doi:10.1109/access.2020.2969055

Cited by 78 publications

(39 citation statements)

References 45 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…17.1 Gardener et al [44] 7.58 Aarahi et al [45] 11.0 Zhang et al [46] 10.8 Gatien Hocepied et al [47] 14.8 N Arunkumar et al [48] 1.14 P.T. Krishnan et al [49] 0.48 Z. Chen et al [15] 0.31 J-L. Song et al [34] 7.71 Proposed SPPCA 1.02 Proposed SUBXPCA 0.71 F. Time complexity reduction using PCA, SPPCA and SUBXPCA Let us consider P 1 , P 2 , P 3 , ..., P N be the input patterns of class P with a dimension d. By applying PCA, the time complexity can be determined as:…”

Section: Frequency Domain Features Extractionmentioning

confidence: 99%

Detection of Epileptic Seizures from EEG Signals by Combining Dimensionality Reduction Algorithms with Machine Learning Models

Zubair¹

2021

Preprint

View full text Add to dashboard Cite

<pre>In this paper, more emphasis has been given to develop a support vector machine (SVM) model using SPPCA and SUBXPCA dimensionality reduction algorithms to increase the classification accuracy. Firstly, Discrete Wavelet Transform (DWT) is applied to EEG signals for extracting the time-frequency domain features of epilepsy such as the energy of each sub-pattern, spike rhythmicity, relative spike amplitude, Dominant Frequency (DF) and Spectral Entropy (SE). The features obtained after performing DWT on an EEG signal are extensive in number, to select the prominent features and to retain their properties, correlation feature sub-pattern-based PCA (SPPCA), and cross sub-pattern correlation-based PCA (SUBXPCA) are used as a dimensionality reduction techniques. To validate the proposed work, performance evaluation parameter such as the accuracy of the time-frequency domain features from different combinations of the dataset has been compared with the latest state-of-the-art works. Simulation results show that the proposed algorithm combined with machine learning classifiers. The best accuracy of 97% for SPPCA is achieved by CatBoost and for SUBXPCA the best accuracy of 98% is achieved by random forest classifier which clearly outperformed the other related works, both in terms of accuracy and computational complexity.</pre>

show abstract

Section: Frequency Domain Features Extractionmentioning

confidence: 99%

Detection of Epileptic Seizures from EEG Signals by Combining Dimensionality Reduction Algorithms with Machine Learning Models

Zubair¹

2021

Preprint

View full text Add to dashboard Cite

show abstract

“…The analysis of EEG signal in diagnosis of neural disorder is implemented based on the procedure of classification of this type of signals [3], [7], [42]. Typically, signal classification includes two steps [18], [44]: preprocessing of initial signal and classification of data formed after the preprocessing.…”

Section: Design Of Approachmentioning

confidence: 99%

“…Generally, the raw measurement of EEG signals contains noise, artifacts and other defects. These can be caused by eyes blinking, muscular activity or other physiological processes in a human body [3] - [8]. Moreover, measured EEG signals are functions of time.…”

Section: A Feature Extractionmentioning

confidence: 99%

“…Applications of Electroencephalogram (EEG) signal in different areas have been intensively developed in last time. The most dynamically developed areas are humancomputer interaction studied in [1], [2] and medicine [3] - [8]. In medicine, the EEG signal analysis is used in epilepsy diagnosis [3], [6], [7], depression [4], stress [5], and other diagnoses [8].…”

Section: Introductionmentioning

confidence: 99%

“…These seizures are consequences of the brain activity that can be characterized by the unexpected and sudden electrical disturbance of brain and excessive neuronal discharge. This activity is recorded using the EEG [11], therefore, the analysis of EEG signal is one of the most important tools in neurology diagnostics [3], [7], [12].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Review of Methods for EEG Signal Classification and Development of New Fuzzy Classification-Based Approach

2020

View full text Add to dashboard Cite

The analysis of EEG signal is a relevant problem in health informatics, and its development can help in detection of epileptic's seizures. The diagnosis is based on classification of EEG signal. Different methods and algorithms for classification of EEG signals with an accepted level of reliability and accuracy have been developed over years. All these methods have two steps that are signal preprocessing and classification. The goal of the preprocessing step is removing noise and reduction of the initial signal dimensionality. The signal dimensionality reduction is required by classification methods, but its result is a loss of small information before the classification. In this paper, an approach for EEG signal classification that takes this loss of information into account is considered. The novelty of the considered approach is usage of fuzzy classifier in the classification step. This classifier allows taking uncertainty of initial data into account, which is caused by loss of some information during dimensionality reduction of initial signal. However, application of fuzzy classifier needs modification of the preprocessing step because it requires data in fuzzy form. Therefore, fuzzification procedure is added to the preprocessing step. In this paper, Fuzzy Decision Tree (FDT) is used as the fuzzy classifier for the epileptic's seizure detection. Its application allows achieving 99.5% accuracy of the classification of epileptic's seizure. The comparison with other studies shows that FDT is very effective for task of epileptic's seizure detection.

show abstract

A novel EEG channel selection and classification methodology for multi‐class motor imagery‐based BCI system design

Jindal

Upadhyay

Singh

2022

Int J Imaging Syst Tech

View full text Add to dashboard Cite

Multi-class MI EEG analysis is an extensively used paradigm in BCI. However, multiple EEG channels lead to redundant information extraction and would reduce the distinction among various MI tasks. Therefore, optimal channel selection from multi-channel EEG activity still remains a challenging task. In this study, to enhance the multi-class BCI system's performance, a novel channel selection, and features optimization methodology have been proposed.First, multi-channel EEG dataset is reduced to an optimum no. of channels subset using developed MDA-SOGWO based EEG channel selection criterion.After that, the discriminable feature-set is generated from time, frequency, and FAWT based time-frequency domain of EEG dataset. Then, an informative feature-set is constructed from extracted features through CCA-RFE based feature selection criterion. Finally, training and validation of selected feature-set are carried out using ELM, LDA, RF, and MLP classifiers. The proposed methodology is evaluated on multi-class MI datasets of BCI Competition IV 2a and BCI Competition III 3a, yielding classification accuracy of 85.65% and 95.15%, respectively. The classification results pose the proposed methodology as an efficient method for future BCI system design.

show abstract

A Unified Framework and Method for EEG-Based Early Epileptic Seizure Detection and Epilepsy Diagnosis

Cited by 78 publications

References 45 publications

Detection of Epileptic Seizures from EEG Signals by Combining Dimensionality Reduction Algorithms with Machine Learning Models

Detection of Epileptic Seizures from EEG Signals by Combining Dimensionality Reduction Algorithms with Machine Learning Models

Review of Methods for EEG Signal Classification and Development of New Fuzzy Classification-Based Approach

A novel EEG channel selection and classification methodology for multi‐class motor imagery‐based BCI system design

Contact Info

Product

Resources

About