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Velasco I, Sipols A, De Blas CS, Pastor L, Bayona S. Motor imagery EEG signal classification with a multivariate time series approach. Biomed Eng Online 2023; 22:29. [PMID: 36959601 PMCID: PMC10035287 DOI: 10.1186/s12938-023-01079-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2022] [Accepted: 02/10/2023] [Indexed: 03/25/2023] Open
Abstract
BACKGROUND Electroencephalogram (EEG) signals record electrical activity on the scalp. Measured signals, especially EEG motor imagery signals, are often inconsistent or distorted, which compromises their classification accuracy. Achieving a reliable classification of motor imagery EEG signals opens the door to possibilities such as the assessment of consciousness, brain computer interfaces or diagnostic tools. We seek a method that works with a reduced number of variables, in order to avoid overfitting and to improve interpretability. This work aims to enhance EEG signal classification accuracy by using methods based on time series analysis. Previous work on this line, usually took a univariate approach, thus losing the possibility to take advantage of the correlation information existing within the time series provided by the different electrodes. To overcome this problem, we propose a multivariate approach that can fully capture the relationships among the different time series included in the EEG data. To perform the multivariate time series analysis, we use a multi-resolution analysis approach based on the discrete wavelet transform, together with a stepwise discriminant that selects the most discriminant variables provided by the discrete wavelet transform analysis RESULTS: Applying this methodology to EEG data to differentiate between the motor imagery tasks of moving either hands or feet has yielded very good classification results, achieving in some cases up to 100% of accuracy for this 2-class pre-processed dataset. Besides, the fact that these results were achieved using a reduced number of variables (55 out of 22,176) can shed light on the relevance and impact of those variables. CONCLUSIONS This work has a potentially large impact, as it enables classification of EEG data based on multivariate time series analysis in an interpretable way with high accuracy. The method allows a model with a reduced number of features, facilitating its interpretability and improving overfitting. Future work will extend the application of this classification method to help in diagnosis procedures for detecting brain pathologies and for its use in brain computer interfaces. In addition, the results presented here suggest that this method could be applied to other fields for the successful analysis of multivariate temporal data.
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Affiliation(s)
- I Velasco
- Department of Computer Science and Statistics, Rey Juan Carlos University, Madrid, Spain.
| | - A Sipols
- Department of Applied Mathematics, Science and Engineering of Materials and Electronic Technology, Rey Juan Carlos University, Madrid, Spain
| | - C Simon De Blas
- Department of Computer Science and Statistics, Rey Juan Carlos University, Madrid, Spain
| | - L Pastor
- Department of Computer Science and Statistics, Rey Juan Carlos University, Madrid, Spain
- Center for Computational Simulation, Universidad Politecnica de Madrid, Madrid, Spain
| | - S Bayona
- Department of Computer Science and Statistics, Rey Juan Carlos University, Madrid, Spain
- Center for Computational Simulation, Universidad Politecnica de Madrid, Madrid, Spain
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Song Z, Deng B, Wang J, Yi G, Yue W. Epileptic seizure detection using brain-rhythmic recurrence biomarkers and ONASNet-based transfer learning. IEEE Trans Neural Syst Rehabil Eng 2022; 30:979-989. [DOI: 10.1109/tnsre.2022.3165060] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
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Li Y, Zhang Z, Teng Z, Liu X. PredAmyl-MLP: Prediction of Amyloid Proteins Using Multilayer Perceptron. COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE 2020; 2020:8845133. [PMID: 33294004 PMCID: PMC7700051 DOI: 10.1155/2020/8845133] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/14/2020] [Revised: 10/06/2020] [Accepted: 10/31/2020] [Indexed: 01/20/2023]
Abstract
Amyloid is generally an aggregate of insoluble fibrin; its abnormal deposition is the pathogenic mechanism of various diseases, such as Alzheimer's disease and type II diabetes. Therefore, accurately identifying amyloid is necessary to understand its role in pathology. We proposed a machine learning-based prediction model called PredAmyl-MLP, which consists of the following three steps: feature extraction, feature selection, and classification. In the step of feature extraction, seven feature extraction algorithms and different combinations of them are investigated, and the combination of SVMProt-188D and tripeptide composition (TPC) is selected according to the experimental results. In the step of feature selection, maximum relevant maximum distance (MRMD) and binomial distribution (BD) are, respectively, used to remove the redundant or noise features, and the appropriate features are selected according to the experimental results. In the step of classification, we employed multilayer perceptron (MLP) to train the prediction model. The 10-fold cross-validation results show that the overall accuracy of PredAmyl-MLP reached 91.59%, and the performance was better than the existing methods.
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Affiliation(s)
- Yanjuan Li
- College of Information and Computer Engineering, Northeast Forestry University, Harbin 150040, China
| | - Zitong Zhang
- College of Information and Computer Engineering, Northeast Forestry University, Harbin 150040, China
| | - Zhixia Teng
- College of Information and Computer Engineering, Northeast Forestry University, Harbin 150040, China
| | - Xiaoyan Liu
- College of Computer Science and Technology, Harbin Institute of Technology, Harbin 150040, China
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Identification of epileptic seizures in EEG signals using time-scale decomposition (ITD), discrete wavelet transform (DWT), phase space reconstruction (PSR) and neural networks. Artif Intell Rev 2019. [DOI: 10.1007/s10462-019-09755-y] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/15/2023]
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Yıldırım Ö, Baloglu UB, Acharya UR. A deep convolutional neural network model for automated identification of abnormal EEG signals. Neural Comput Appl 2018. [DOI: 10.1007/s00521-018-3889-z] [Citation(s) in RCA: 63] [Impact Index Per Article: 10.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
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Ahmedt-Aristizabal D, Fookes C, Dionisio S, Nguyen K, Cunha JPS, Sridharan S. Automated analysis of seizure semiology and brain electrical activity in presurgery evaluation of epilepsy: A focused survey. Epilepsia 2017; 58:1817-1831. [DOI: 10.1111/epi.13907] [Citation(s) in RCA: 29] [Impact Index Per Article: 4.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 08/28/2017] [Indexed: 11/28/2022]
Affiliation(s)
- David Ahmedt-Aristizabal
- The Speech, Audio, Image and Video Technologies (SAIVT) and Science and Engineering Faculty; Queensland University of Technology; Brisbane Queensland Australia
| | - Clinton Fookes
- The Speech, Audio, Image and Video Technologies (SAIVT) and Science and Engineering Faculty; Queensland University of Technology; Brisbane Queensland Australia
| | - Sasha Dionisio
- Mater Centre for Neurosciences; Brisbane Queensland Australia
| | - Kien Nguyen
- The Speech, Audio, Image and Video Technologies (SAIVT) and Science and Engineering Faculty; Queensland University of Technology; Brisbane Queensland Australia
| | - João Paulo S. Cunha
- The Institute of Systems and Computer Engineering; Technology and Science; and Faculty of Engineering; University of Porto; Porto Portugal
| | - Sridha Sridharan
- The Speech, Audio, Image and Video Technologies (SAIVT) and Science and Engineering Faculty; Queensland University of Technology; Brisbane Queensland Australia
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Jaiswal AK, Banka H. Local Transformed Features for Epileptic Seizure Detection in EEG Signal. J Med Biol Eng 2017. [DOI: 10.1007/s40846-017-0286-5] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
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Jaiswal AK, Banka H. Local pattern transformation based feature extraction techniques for classification of epileptic EEG signals. Biomed Signal Process Control 2017. [DOI: 10.1016/j.bspc.2017.01.005] [Citation(s) in RCA: 56] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
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Li M, Chen W, Zhang T. Automatic epilepsy detection using wavelet-based nonlinear analysis and optimized SVM. Biocybern Biomed Eng 2016. [DOI: 10.1016/j.bbe.2016.07.004] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
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Memarian N, Kim S, Dewar S, Engel J, Staba RJ. Multimodal data and machine learning for surgery outcome prediction in complicated cases of mesial temporal lobe epilepsy. Comput Biol Med 2015; 64:67-78. [PMID: 26149291 PMCID: PMC4554822 DOI: 10.1016/j.compbiomed.2015.06.008] [Citation(s) in RCA: 55] [Impact Index Per Article: 6.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2015] [Revised: 06/04/2015] [Accepted: 06/10/2015] [Indexed: 11/21/2022]
Abstract
BACKGROUND This study sought to predict postsurgical seizure freedom from pre-operative diagnostic test results and clinical information using a rapid automated approach, based on supervised learning methods in patients with drug-resistant focal seizures suspected to begin in temporal lobe. METHOD We applied machine learning, specifically a combination of mutual information-based feature selection and supervised learning classifiers on multimodal data, to predict surgery outcome retrospectively in 20 presurgical patients (13 female; mean age±SD, in years 33±9.7 for females, and 35.3±9.4 for males) who were diagnosed with mesial temporal lobe epilepsy (MTLE) and subsequently underwent standard anteromesial temporal lobectomy. The main advantage of the present work over previous studies is the inclusion of the extent of ipsilateral neocortical gray matter atrophy and spatiotemporal properties of depth electrode-recorded seizures as training features for individual patient surgery planning. RESULTS A maximum relevance minimum redundancy (mRMR) feature selector identified the following features as the most informative predictors of postsurgical seizure freedom in this study's sample of patients: family history of epilepsy, ictal EEG onset pattern (positive correlation with seizure freedom), MRI-based gray matter thickness reduction in the hemisphere ipsilateral to seizure onset, proportion of seizures that first appeared in ipsilateral amygdala to total seizures, age, epilepsy duration, delay in the spread of ipsilateral ictal discharges from site of onset, gender, and number of electrode contacts at seizure onset (negative correlation with seizure freedom). Using these features in combination with a least square support vector machine (LS-SVM) classifier compared to other commonly used classifiers resulted in very high surgical outcome prediction accuracy (95%). CONCLUSIONS Supervised machine learning using multimodal compared to unimodal data accurately predicted postsurgical outcome in patients with atypical MTLE.
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Affiliation(s)
- Negar Memarian
- Department of Psychology, David Geffen School of Medicine and at UCLA, Los Angeles, CA 90095, United States; Department of Neurology, David Geffen School of Medicine and at UCLA, Los Angeles, CA 90095, United States.
| | - Sally Kim
- Department of Neurology, David Geffen School of Medicine and at UCLA, Los Angeles, CA 90095, United States
| | - Sandra Dewar
- Department of Neurosurgery, David Geffen School of Medicine and at UCLA, Los Angeles, CA 90095, United States
| | - Jerome Engel
- Department of Neurology, David Geffen School of Medicine and at UCLA, Los Angeles, CA 90095, United States; Department of Neurosurgery, David Geffen School of Medicine and at UCLA, Los Angeles, CA 90095, United States; Department of Neurobiology, David Geffen School of Medicine and at UCLA, Los Angeles, CA 90095, United States; Department of Psychiatry & Biobehavioral Sciences, David Geffen School of Medicine and at UCLA, Los Angeles, CA 90095, United States
| | - Richard J Staba
- Department of Neurology, David Geffen School of Medicine and at UCLA, Los Angeles, CA 90095, United States
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Faust O, Acharya UR, Adeli H, Adeli A. Wavelet-based EEG processing for computer-aided seizure detection and epilepsy diagnosis. Seizure 2015; 26:56-64. [DOI: 10.1016/j.seizure.2015.01.012] [Citation(s) in RCA: 206] [Impact Index Per Article: 22.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2014] [Revised: 01/15/2015] [Accepted: 01/18/2015] [Indexed: 11/25/2022] Open
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Kerr WT, Anderson A, Lau EP, Cho AY, Xia H, Bramen J, Douglas PK, Braun ES, Stern JM, Cohen MS. Automated diagnosis of epilepsy using EEG power spectrum. Epilepsia 2012; 53:e189-92. [PMID: 22967005 DOI: 10.1111/j.1528-1167.2012.03653.x] [Citation(s) in RCA: 21] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Abstract
Interictal electroencephalography (EEG) has clinically meaningful limitations in its sensitivity and specificity in the diagnosis of epilepsy because of its dependence on the occurrence of epileptiform discharges. We have developed a computer-aided diagnostic (CAD) tool that operates on the absolute spectral energy of the routine EEG and has both substantially higher sensitivity and negative predictive value than the identification of interictal epileptiform discharges. Our approach used a multilayer perceptron to classify 156 patients admitted for video-EEG monitoring. The patient population was diagnostically diverse; 87 were diagnosed with either generalized or focal seizures. The remainder of the patients were diagnosed with nonepileptic seizures. The sensitivity was 92% (95% confidence interval [CI] 85-97%) and the negative predictive value was 82% (95% CI 67-92%). We discuss how these findings suggest that this CAD can be used to supplement event-based analysis by trained epileptologists.
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Affiliation(s)
- Wesley T Kerr
- Medical Scientist Training Program and Department of Biomathematics, University of California at Los Angeles, 760 Westwood Plaza, Los Angeles, CA 90095, U.S.A.
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