Detection of ADHD From EOG Signals Using Approximate Entropy and Petrosain’s Fractal Dimension
Publish place: Journal of medical signals and sensors، Vol: 12، Issue: 3
Publish Year: 1401
نوع سند: مقاله ژورنالی
زبان: English
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شناسه ملی سند علمی:
JR_JMSI-12-3_007
تاریخ نمایه سازی: 28 تیر 1402
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 ۳۰ ADHD children and ۳۰ 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: ۸۴.۶% ± ۴.۴%, sensitivity: ۸۵.۲% ± ۴.۹%, specificity:
۷۸.۸% ± ۶.۵%) was more successful in separating the two groups than the NG (۷۸.۱% ± ۱.۱%,
sensitivity: ۸۰.۱% ± ۶.۲%, specificity: ۷۲.۲% ± ۹.۲%). 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 , attention deficit hyperactivity disorder , electrooculogram , neural gas , Petrosian’s fractal dimension , support vector machine
Authors
Nasrin Sho'ouri
Faculty of Technology and Engineering, Central Tehran Branch, Islamic Azad University, Tehran, Iran