EEG feature selection for ADHD detection in children
Attention deficit and hyperactivity disorder (ADHD) is a medical condition that affects approximately 7% of children worldwide. The diagnosis of ADHD can be done using psychological tests and electroencephalography (EEG). However, the variability and complexity of EEG signals affects its diagnostic...
- Autores:
-
Mercado-Aguirre, Isabela M.
Gutierrez-Ruiz, Karol P.
Contreras Ortiz, Sonia Helena
- Tipo de recurso:
- Fecha de publicación:
- 2020
- Institución:
- Universidad Tecnológica de Bolívar
- Repositorio:
- Repositorio Institucional UTB
- Idioma:
- eng
- OAI Identifier:
- oai:repositorio.utb.edu.co:20.500.12585/9970
- Acceso en línea:
- https://hdl.handle.net/20.500.12585/9970
https://www.spiedigitallibrary.org/conference-proceedings-of-spie/11583/115830S/EEG-feature-selection-for-ADHD-detection-in-children/10.1117/12.2579625.short?SSO=1
- Palabra clave:
- Bioinformatics
Classification (of information)
Electroencephalography
Electrophysiology
Entropy
Feature extraction
Power spectrum
Regression analysisTesting
LEMB
- Rights
- closedAccess
- License
- http://purl.org/coar/access_right/c_14cb
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EEG feature selection for ADHD detection in children |
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EEG feature selection for ADHD detection in children |
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EEG feature selection for ADHD detection in children Bioinformatics Classification (of information) Electroencephalography Electrophysiology Entropy Feature extraction Power spectrum Regression analysisTesting LEMB |
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EEG feature selection for ADHD detection in children |
| title_full |
EEG feature selection for ADHD detection in children |
| title_fullStr |
EEG feature selection for ADHD detection in children |
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EEG feature selection for ADHD detection in children |
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EEG feature selection for ADHD detection in children |
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Mercado-Aguirre, Isabela M. Gutierrez-Ruiz, Karol P. Contreras Ortiz, Sonia Helena |
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Mercado-Aguirre, Isabela M. Gutierrez-Ruiz, Karol P. Contreras Ortiz, Sonia Helena |
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Bioinformatics Classification (of information) Electroencephalography Electrophysiology Entropy Feature extraction Power spectrum Regression analysisTesting |
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Bioinformatics Classification (of information) Electroencephalography Electrophysiology Entropy Feature extraction Power spectrum Regression analysisTesting LEMB |
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LEMB |
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Attention deficit and hyperactivity disorder (ADHD) is a medical condition that affects approximately 7% of children worldwide. The diagnosis of ADHD can be done using psychological tests and electroencephalography (EEG). However, the variability and complexity of EEG signals affects its diagnostic utility. The purpose of this work is to identify relevant features of EEG signals from children diagnosed with ADHD and control cases for their classification. A total of 47 children were included in the study (22 with ADHD and 25 in the control group). EEG of cognitive evoked potentials were preprocessed using wavelet filtering and synchronized averaging. Then, fourteen features were calculated in signals from four channels (F3, AF3, F4 and AF4), including evoked potentials, power spectrum, entropy, chaos, bicoherence measures, and prominent peaks. For feature selection, the algorithms PCA, hybrid stepwise regression, ridge regression, and correlation values were evaluated. It was evidenced that evoked potentials have a relative high level of importance, as well as the prominent peaks. On the other hand, the values of chaos and bicoherence measures, along with the gender, are the least representative features. These results are consistent among the four feature selection algorithms. In conclusion, 9 of the 14 features are representative of the data set and were used for the classification stage of this work. |
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2020 |
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2020-11-03 |
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2021-02-09T22:05:51Z |
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2021-02-09T22:05:51Z |
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2021-02-09 |
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http://purl.org/coar/version/c_970fb48d4fbd8a85 |
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Isabela M. Mercado-Aguirre, Karol P. Gutierrez-Ruiz, and Sonia H. Contreras-Ortiz "EEG feature selection for ADHD detection in children", Proc. SPIE 11583, 16th International Symposium on Medical Information Processing and Analysis, 115830S (3 November 2020); https://doi.org/10.1117/12.2579625 |
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https://hdl.handle.net/20.500.12585/9970 |
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https://www.spiedigitallibrary.org/conference-proceedings-of-spie/11583/115830S/EEG-feature-selection-for-ADHD-detection-in-children/10.1117/12.2579625.short?SSO=1 |
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10.1117/12.2579625 |
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Universidad Tecnológica de Bolívar |
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Repositorio Universidad Tecnológica de Bolívar |
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Isabela M. Mercado-Aguirre, Karol P. Gutierrez-Ruiz, and Sonia H. Contreras-Ortiz "EEG feature selection for ADHD detection in children", Proc. SPIE 11583, 16th International Symposium on Medical Information Processing and Analysis, 115830S (3 November 2020); https://doi.org/10.1117/12.2579625 10.1117/12.2579625 Universidad Tecnológica de Bolívar Repositorio Universidad Tecnológica de Bolívar |
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Mercado-Aguirre, Isabela M.6ec184eb-e974-4ed2-98f2-a72699f371f7Gutierrez-Ruiz, Karol P.9431cc3d-7ebb-481c-8916-3186c0de5980Contreras Ortiz, Sonia Helenad8ce7bb4-34ec-40b2-9bbc-76db7004d4212021-02-09T22:05:51Z2021-02-09T22:05:51Z2020-11-032021-02-09Isabela M. Mercado-Aguirre, Karol P. Gutierrez-Ruiz, and Sonia H. Contreras-Ortiz "EEG feature selection for ADHD detection in children", Proc. SPIE 11583, 16th International Symposium on Medical Information Processing and Analysis, 115830S (3 November 2020); https://doi.org/10.1117/12.2579625https://hdl.handle.net/20.500.12585/9970https://www.spiedigitallibrary.org/conference-proceedings-of-spie/11583/115830S/EEG-feature-selection-for-ADHD-detection-in-children/10.1117/12.2579625.short?SSO=110.1117/12.2579625Universidad Tecnológica de BolívarRepositorio Universidad Tecnológica de BolívarAttention deficit and hyperactivity disorder (ADHD) is a medical condition that affects approximately 7% of children worldwide. The diagnosis of ADHD can be done using psychological tests and electroencephalography (EEG). However, the variability and complexity of EEG signals affects its diagnostic utility. The purpose of this work is to identify relevant features of EEG signals from children diagnosed with ADHD and control cases for their classification. A total of 47 children were included in the study (22 with ADHD and 25 in the control group). EEG of cognitive evoked potentials were preprocessed using wavelet filtering and synchronized averaging. Then, fourteen features were calculated in signals from four channels (F3, AF3, F4 and AF4), including evoked potentials, power spectrum, entropy, chaos, bicoherence measures, and prominent peaks. For feature selection, the algorithms PCA, hybrid stepwise regression, ridge regression, and correlation values were evaluated. It was evidenced that evoked potentials have a relative high level of importance, as well as the prominent peaks. On the other hand, the values of chaos and bicoherence measures, along with the gender, are the least representative features. These results are consistent among the four feature selection algorithms. In conclusion, 9 of the 14 features are representative of the data set and were used for the classification stage of this work.application/pdfengProceedings Volume 11583, 16th International Symposium on Medical Information Processing and Analysis; 115830S (2020)EEG feature selection for ADHD detection in childreninfo:eu-repo/semantics/lectureinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_8544http://purl.org/coar/version/c_970fb48d4fbd8a85BioinformaticsClassification (of information)ElectroencephalographyElectrophysiologyEntropyFeature extractionPower spectrumRegression analysisTestingLEMBinfo:eu-repo/semantics/closedAccesshttp://purl.org/coar/access_right/c_14cbCartagena de IndiasInvestigadoresVásquez, J., Cárdenas, E.M., Feria, M., Benjet, C., Palacios, L., de la Peña, F. Guía clínica para el trastorno por déficit de atención e hiperactividad (2010) DF Guías Clínicas Para La Atención De Trastornos Mentales. Cited 5 times. Berenzon S, Del Bosque J, Alfaro J, Medina-Mora ME. México, Instituto Nacional de Psiquiatría Ramón de la FuMental Health, N.I. (2009) Trastorno De Déficit De Atención E Hiperactividad of publicación STR 09-3572 Accessed 23-July-2018Chen, H., Chen, W., Song, Y., Sun, L., Li, X. EEG characteristics of children with attention-deficit/hyperactivity disorder (2019) Neuroscience, 406, pp. 444-456. Cited 7 times. www.elsevier.com/locate/neuroscience doi: 10.1016/j.neuroscience.2019.03.048Mohammadi, M.R., Khaleghi, A., Nasrabadi, A.M., Rafieivand, S., Begol, M., Zarafshan, H. EEG classification of ADHD and normal children using non-linear features and neural network (2016) Biomedical Engineering Letters, 6 (2), pp. 66-73. Cited 43 times. http://www.springer.com/engineering/biomedical+eng/journal/13534 doi: 10.1007/s13534-016-0218-2Ghassemi, F., Hassan, M., Tehrani-Doost, M., Abootalebi, V. Using non-linear features of EEG for ADHD/normal participants' classification (Open Access) (2012) Procedia - Social and Behavioral Sciences, 32, pp. 148-152. Cited 20 times. http://sciencedirect.utb.elogim.com/science/journal/18770428/1 doi: 10.1016/j.sbspro.2012.01.024Nazhvani, A.D., Boostani, R., Afrasiabi, S., Sadatnezhad, K. Classification of ADHD and BMD patients using visual evoked potential (2013) Clinical Neurology and Neurosurgery, 115 (11), pp. 2329-2335. Cited 22 times. doi: 10.1016/j.clineuro.2013.08.009Öztoprak, H., Toycan, M., Alp, Y.K., Arıkan, O., Doğutepe, E., Karakaş, S. Machine-based classification of ADHD and nonADHD participants using time/frequency features of event-related neuroelectric activity (Open Access) (2017) Clinical Neurophysiology, 128 (12), pp. 2400-2410. Cited 10 times. www.elsevier.com/inca/publications/store/6/0/1/5/2/8 doi: 10.1016/j.clinph.2017.09.105Badcock, N.A., Mousikou, P., Mahajan, Y., De Lissa, P., Thie, J., McArthur, G. Validation of the Emotiv EPOC® EEG gaming systemfor measuring research quality auditory ERPs (Open Access) (2013) PeerJ, 2013 (1), art. no. e38. Cited 193 times. https://peerj.com/articles/38.pdf doi: 10.7717/peerj.38Adeli, H., Zhou, Z., Dadmehr, N. Analysis of EEG records in an epileptic patient using wavelet transform (2003) Journal of Neuroscience Methods, 123 (1), pp. 69-87. Cited 806 times. doi: 10.1016/S0165-0270(02)00340-0Markazi, S.A., Qazi, S., Stergioulas, L.S., Ramchurn, A., Bunce, D. Wavelet filtering of the P300 component in event-related potentials (2006) Annual International Conference of the IEEE Engineering in Medicine and Biology - Proceedings, art. no. 4029661, pp. 1719-1722. Cited 16 times. ISBN: 1424400325; 978-142440032-4 doi: 10.1109/IEMBS.2006.260691Subasi, A. EEG signal classification using wavelet feature extraction and a mixture of expert model (2007) Expert Systems with Applications, 32 (4), pp. 1084-1093. 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