Identification of Attention Deficit Hyperactivity Disorder Patients Using Wavelet-Based Features of EEG Signals

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Karimi Shahraki Sahar; KHEZRI MAHDI

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Journal: Journal of Intelligent Procedures in Electrical Technology Year:2021 | Volume:12 | Issue:47 Page(s): 1-11

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Title

Identification of Attention Deficit Hyperactivity Disorder Patients Using Wavelet-Based Features of EEG Signals

Author(s)

Karimi Shahraki Sahar | KHEZRI MAHDI | Issue Writer Certificate

Keywords

ADHDQ1

time domain featuresQ1

wavelet transformQ2

kNN classifierQ1

SVM classifierQ1

Abstract

Attention Deficit Hyperactivity Disorder (ADHD) is a neurological and psychiatric disorder which causes to attention deficit, anxiety, hyperactivity and impulsive behaviors. ADHD is more common in children and directly leads to their learning disability. The aim of this study was to accurately identify ADHD patients by using wavelet-based features of EEG signals. Recorded EEG signals from 61 children with ADHD (diagnosed according to the DSM-IV criteria) and 60 healthy controls in the age range of 7-12 years were used to design the system. In the proposed method by applying wavelet transform, EEG signals were decomposed into sub-bands. For the time version of the signals in each sub-band, the temporal and statistical features were calculated. The reduced feature set by principal component analysis (PCA) method was then used to train the classification unit to identify ADHD patients from healthy individuals. To obtain the desired results, different types of wavelet functions and decomposition levels were investigated. The bior3. 1 wavelet function with the support vector machine (SVM) classifier and the rbio1. 1 wavelet function with the k-nearest neighbor (kNN) classifier presented the best performance with the recognition accuracy of 98. 33% and 99. 17%, respectively. The SVM classification method with radial basis kernel function (RBF) and the kNN method with three nearest neighbors, k = 3 obtained the best results. The results obtained in this study compared to the results reported in previous studies showed at least a 2% improvement in the recognition accuracy of ADHD patients.

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APA: Copy

Karimi Shahraki, Sahar, & KHEZRI, MAHDI. (2021). Identification of Attention Deficit Hyperactivity Disorder Patients Using Wavelet-Based Features of EEG Signals. JOURNAL OF INTELLIGENT PROCEDURES IN ELECTRICAL TECHNOLOGY, 12(47 ), 1-11. SID. https://sid.ir/paper/391415/en

Vancouver: Copy

Karimi Shahraki Sahar, KHEZRI MAHDI. Identification of Attention Deficit Hyperactivity Disorder Patients Using Wavelet-Based Features of EEG Signals. JOURNAL OF INTELLIGENT PROCEDURES IN ELECTRICAL TECHNOLOGY[Internet]. 2021;12(47 ):1-11. Available from: https://sid.ir/paper/391415/en

IEEE: Copy

Sahar Karimi Shahraki, and MAHDI KHEZRI, “Identification of Attention Deficit Hyperactivity Disorder Patients Using Wavelet-Based Features of EEG Signals,” JOURNAL OF INTELLIGENT PROCEDURES IN ELECTRICAL TECHNOLOGY, vol. 12, no. 47 , pp. 1–11, 2021, [Online]. Available: https://sid.ir/paper/391415/en

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