A Comparative Investigation of Wavelet Families for Classification of EOG Signals Related to Healthy and ADHD Children

Author:

Ayoubipour Shahrzad1,Sho'ouri Nasrin1

Affiliation:

1. Department of Technology and Engineering, Central Tehran Branch, Islamic Azad University, Tehran, Iran.

Abstract

Based on previous research, there are differences between eye movements of people with attention-deficit hyperactivity disorder (ADHD) and of healthy people, as a result, the existence of differences regarding the electrooculogram (EOG) signals of the 2 groups exists. Thus, this study aimed to examine the recorded EOG signals of 30 ADHD children and 30 healthy children while performing an attention-related task. For this purpose, the EOG signals of these 2 groups were decomposed utilizing various wavelet functions. Afterward, features, including mean, energy, and standard deviation (SD) of approximation and detail wavelet coefficients were calculated. The Davies–Bouldin (DB) index was used for the evaluation of the feature space quality. Finally, the 2 groups were classified using one-dimensional feature vector and support vector machine (SVM). The SD of detail coefficients (db4) was selected as the most effective feature for separating the 2 groups. Statistical analysis revealed that the values of energy and SD of EOG signals’ detail coefficients were significantly lower in the ADHD group in comparison with the healthy group ( P<.001). These results showed that the speed of the ADHD group's eye movements was slower due to the fact that the high-frequency band activity of EOG signals in the healthy group was higher. In addition, the EOG signals were classified with a detection accuracy of 83.42 ± 3.8%. The results of this study can be applied in designing an EOG biofeedback protocol to treat or mitigate the symptoms of ADHD patients.

Publisher

SAGE Publications

Subject

Neurology (clinical),Neurology,General Medicine

Cited by 1 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. A novel approach for the processing and classification of electrooculographic signals;Proceedings of the 5th International Conference on Computer Information and Big Data Applications;2024-04-26

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