Detection of ADHD from EOG signals using approximate entropy and petrosain's fractal dimension
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 re...
| Main Author: | |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Wolters Kluwer Medknow Publications
2022-01-01
|
| Series: | Journal of Medical Signals and Sensors |
| Subjects: | |
| Online Access: | http://www.jmssjournal.net/article.asp?issn=2228-7477;year=2022;volume=12;issue=3;spage=254;epage=262;aulast=Shoouri |
| _version_ | 1811342232440012800 |
|---|---|
| author | Nasrin Shoouri |
| author_facet | Nasrin Shoouri |
| author_sort | Nasrin Shoouri |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-04-13T19:08:00Z |
| format | Article |
| id | doaj.art-650e7c9cf342449fbc2a6abf8dd97afd |
| institution | Directory Open Access Journal |
| issn | 2228-7477 |
| language | English |
| last_indexed | 2024-04-13T19:08:00Z |
| publishDate | 2022-01-01 |
| publisher | Wolters Kluwer Medknow Publications |
| record_format | Article |
| series | Journal of Medical Signals and Sensors |
| spelling | doaj.art-650e7c9cf342449fbc2a6abf8dd97afd2022-12-22T02:33:55ZengWolters Kluwer Medknow PublicationsJournal of Medical Signals and Sensors2228-74772022-01-0112325426210.4103/jmss.jmss_119_21Detection of ADHD from EOG signals using approximate entropy and petrosain's fractal dimensionNasrin ShoouriBackground: 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.http://www.jmssjournal.net/article.asp?issn=2228-7477;year=2022;volume=12;issue=3;spage=254;epage=262;aulast=Shoouriapproximate entropyattention deficit hyperactivity disorderelectrooculogramneural gaspetrosian's fractal dimensionsupport vector machine |
| spellingShingle | Nasrin Shoouri Detection of ADHD from EOG signals using approximate entropy and petrosain's fractal dimension Journal of Medical Signals and Sensors approximate entropy attention deficit hyperactivity disorder electrooculogram neural gas petrosian's fractal dimension support vector machine |
| title | Detection of ADHD from EOG signals using approximate entropy and petrosain's fractal dimension |
| title_full | Detection of ADHD from EOG signals using approximate entropy and petrosain's fractal dimension |
| title_fullStr | Detection of ADHD from EOG signals using approximate entropy and petrosain's fractal dimension |
| title_full_unstemmed | Detection of ADHD from EOG signals using approximate entropy and petrosain's fractal dimension |
| title_short | Detection of ADHD from EOG signals using approximate entropy and petrosain's fractal dimension |
| title_sort | detection of adhd from eog signals using approximate entropy and petrosain s fractal dimension |
| topic | approximate entropy attention deficit hyperactivity disorder electrooculogram neural gas petrosian's fractal dimension support vector machine |
| url | http://www.jmssjournal.net/article.asp?issn=2228-7477;year=2022;volume=12;issue=3;spage=254;epage=262;aulast=Shoouri |
| work_keys_str_mv | AT nasrinshoouri detectionofadhdfromeogsignalsusingapproximateentropyandpetrosainsfractaldimension |