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

Nasrin Sho'ouri

doi:10.4103/jmss.jmss_119_21

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

J Med Signals Sens. 2022 Jul 26;12(3):254-262. doi: 10.4103/jmss.jmss_119_21. eCollection 2022 Jul-Sep.

Author

Nasrin Sho'ouri¹

Affiliation

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

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.

Keywords: Approximate entropy; Petrosian's fractal dimension; attention deficit hyperactivity disorder; electrooculogram; neural gas; support vector machine.