Detection of ADHD From EOG Signals Using Approximate Entropy and Petrosain's Fractal Dimension.

IF 1.1 Q4 ENGINEERING, BIOMEDICAL

Journal of Medical Signals & Sensors Pub Date : 2022-07-26 eCollection Date: 2022-07-01 DOI:10.4103/jmss.jmss_119_21

Nasrin Sho'ouri

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引用次数: 2

Abstract

Background: Previous research has shown that eye movements are different in patients with attention deficit hyperactivity disorder (ADHD) and healthy people. As a result, electrooculogram (EOG) signals may also differ between the two groups. Therefore, the aim of this study was to investigate the recorded EOG signals of 30 ADHD children and 30 healthy children (control group) while performing an attention-related task.

Methods: Two features of approximate entropy (ApEn) and Petrosian's fractal dimension (Pet's FD) of EOG signals were calculated for the two groups. Then, the two groups were classified using the vector derived from two features and two support vector machine (SVM) and neural gas (NG) classifiers.

Results: Statistical analysis showed that the values of both features were significantly lower in the ADHD group compared to the control group. Moreover, the SVM classifier (accuracy: 84.6% ± 4.4%, sensitivity: 85.2% ± 4.9%, specificity: 78.8% ± 6.5%) was more successful in separating the two groups than the NG (78.1% ± 1.1%, sensitivity: 80.1% ± 6.2%, specificity: 72.2% ± 9.2%).

Conclusion: The decrease in ApEn and Pet's FD values in the EOG signals of the ADHD group showed that their eye movements were slower than the control group and this difference was due to their attention deficit. The results of this study can be used to design an EOG biofeedback training course to reduce the symptoms of ADHD patients.

Abstract Image

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利用近似熵和岩相分形维数从eeg信号中检测ADHD。

背景:以往的研究表明，注意缺陷多动障碍(ADHD)患者和健康人的眼球运动不同。因此，眼电图(EOG)信号在两组之间也可能不同。因此，本研究的目的是研究30名ADHD儿童和30名健康儿童(对照组)在执行注意相关任务时记录的脑电图信号。方法:计算两组eeg信号的近似熵(ApEn)和Petrosian分形维数(Pet’s FD)两个特征。然后，使用两个特征和两个支持向量机(SVM)和神经气体(NG)分类器得到的向量对两组进行分类。结果:经统计分析，ADHD组两项特征值均显著低于对照组。此外，SVM分类器(准确率:84.6%±4.4%，灵敏度:85.2%±4.9%，特异性:78.8%±6.5%)的两组分离成功率高于NG分类器(78.1%±1.1%，灵敏度:80.1%±6.2%，特异性:72.2%±9.2%)。结论:ADHD组EOG信号中ApEn和Pet的FD值下降说明ADHD组的眼球运动比对照组慢，这种差异是由于ADHD组的注意力缺陷造成的。本研究结果可用于设计脑电图生物反馈训练课程，以减轻ADHD患者的症状。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Journal of Medical Signals & Sensors ENGINEERING, BIOMEDICAL-

CiteScore

2.30

自引率

0.00%

发文量

审稿时长

33 weeks

期刊介绍： JMSS is an interdisciplinary journal that incorporates all aspects of the biomedical engineering including bioelectrics, bioinformatics, medical physics, health technology assessment, etc. Subject areas covered by the journal include: - Bioelectric: Bioinstruments Biosensors Modeling Biomedical signal processing Medical image analysis and processing Medical imaging devices Control of biological systems Neuromuscular systems Cognitive sciences Telemedicine Robotic Medical ultrasonography Bioelectromagnetics Electrophysiology Cell tracking - Bioinformatics and medical informatics: Analysis of biological data Data mining Stochastic modeling Computational genomics Artificial intelligence & fuzzy Applications Medical softwares Bioalgorithms Electronic health - Biophysics and medical physics: Computed tomography Radiation therapy Laser therapy - Education in biomedical engineering - Health technology assessment - Standard in biomedical engineering.