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Phase Space Reconstruction of EEG Signals for Classification of ADHD and Control Adults.
Clin EEG Neurosci 2019; :1550059419876525CE

Abstract

Attention deficit hyperactivity disorder (ADHD) is a childhood behavioral disorder that can persist into adulthood. Electroencephalography (EEG) plays a significant role in assessing the neurophysiology of ADHD because of its ability to reveal complex brain activity. The present study proposes an EEG-based diagnosis system using the phase space reconstruction technique to classify ADHD and control adults. Electric activity is recorded for 47 ADHD and 50 control adults during the eyes-open, eyes-closed, and Continuous Performance Test (CPT) condition. Various statistical features are extracted from Euclidean distances based on phase space reconstruction of signals. The proposed system is evaluated with 2 feature selection methods (correlation-based feature selection and particle swarm optimization) and 5 machine learning methods (neural dynamic classifier, support vector machine, enhanced probabilistic neural network, k-nearest neighbor, and naive-Bayes classifier). Experimental results showed the highest testing accuracy of 93.3% under the eyes-open, 90% under the eyes-closed, and 100% under the CPT condition. This study focused on the utility of phase space reconstruction of brain signals to discriminate between ADHD and control adults.

Authors+Show Affiliations

Department of Computer Science and Engineering, University Institute of Engineering and Technology, Panjab University, Chandigarh, India.Department of Computer Science and Engineering, University Institute of Engineering and Technology, Panjab University, Chandigarh, India.Department of Psychiatry, Government Medical College and Hospital, Chandigarh, India.Department of Electrical and Electronics Engineering, University Institute of Engineering and Technology, Panjab University, Chandigarh, India.Department of Psychiatry, Government Medical College and Hospital, Chandigarh, India.

Pub Type(s)

Journal Article

Language

eng

PubMed ID

31533446

Citation

Kaur, Simranjit, et al. "Phase Space Reconstruction of EEG Signals for Classification of ADHD and Control Adults." Clinical EEG and Neuroscience, 2019, p. 1550059419876525.
Kaur S, Singh S, Arun P, et al. Phase Space Reconstruction of EEG Signals for Classification of ADHD and Control Adults. Clin EEG Neurosci. 2019.
Kaur, S., Singh, S., Arun, P., Kaur, D., & Bajaj, M. (2019). Phase Space Reconstruction of EEG Signals for Classification of ADHD and Control Adults. Clinical EEG and Neuroscience, p. 1550059419876525. doi:10.1177/1550059419876525.
Kaur S, et al. Phase Space Reconstruction of EEG Signals for Classification of ADHD and Control Adults. Clin EEG Neurosci. 2019 Sep 19;1550059419876525. PubMed PMID: 31533446.
* Article titles in AMA citation format should be in sentence-case
TY - JOUR T1 - Phase Space Reconstruction of EEG Signals for Classification of ADHD and Control Adults. AU - Kaur,Simranjit, AU - Singh,Sukhwinder, AU - Arun,Priti, AU - Kaur,Damanjeet, AU - Bajaj,Manoj, Y1 - 2019/09/19/ PY - 2019/9/20/entrez KW - EEG KW - adults KW - attention deficit hyperactivity disorder KW - continuous performance test KW - phase space reconstruction SP - 1550059419876525 EP - 1550059419876525 JF - Clinical EEG and neuroscience JO - Clin EEG Neurosci N2 - Attention deficit hyperactivity disorder (ADHD) is a childhood behavioral disorder that can persist into adulthood. Electroencephalography (EEG) plays a significant role in assessing the neurophysiology of ADHD because of its ability to reveal complex brain activity. The present study proposes an EEG-based diagnosis system using the phase space reconstruction technique to classify ADHD and control adults. Electric activity is recorded for 47 ADHD and 50 control adults during the eyes-open, eyes-closed, and Continuous Performance Test (CPT) condition. Various statistical features are extracted from Euclidean distances based on phase space reconstruction of signals. The proposed system is evaluated with 2 feature selection methods (correlation-based feature selection and particle swarm optimization) and 5 machine learning methods (neural dynamic classifier, support vector machine, enhanced probabilistic neural network, k-nearest neighbor, and naive-Bayes classifier). Experimental results showed the highest testing accuracy of 93.3% under the eyes-open, 90% under the eyes-closed, and 100% under the CPT condition. This study focused on the utility of phase space reconstruction of brain signals to discriminate between ADHD and control adults. SN - 2169-5202 UR - https://www.unboundmedicine.com/medline/citation/31533446/Phase_Space_Reconstruction_of_EEG_Signals_for_Classification_of_ADHD_and_Control_Adults L2 - http://journals.sagepub.com/doi/full/10.1177/1550059419876525?url_ver=Z39.88-2003&rfr_id=ori:rid:crossref.org&rfr_dat=cr_pub=pubmed DB - PRIME DP - Unbound Medicine ER -