Abstract
This paper presents our preliminary study EEG brain signals of children with attention deficit hyperactivity disorder (ADHD) in order to support a computer assisted diagnostic system. The EEG signals were recorded from 13 children including normal and children diagnosed with ADHD. We analyzed the signals with multilevel discrete wavelet decompositions in order to extract brain signal power spectrum features. A wavelet thresholding technique was employed to further improve the data quality by denoising the artifacts. In order to discriminate the attention level in electrical brain activity of ADHD children, we used a standard Self-Organizing Map clustering technique with wavelet coefficient input features. Clustering results varied depending on the wavelet feature extraction stage, particularly it was noticed that accuracy was dependent on the type of the used wavelet function. The clustering results demonstrate that 'sym7' wavelet function provides better input feature localization to provide the accurate separation of normal and disordered children's brain activity.
| Original language | English |
|---|---|
| Title of host publication | ICCAS 2010 - International Conference on Control, Automation and Systems |
| Pages | 2439-2442 |
| Number of pages | 4 |
| Publication status | Published - 2010 |
| Externally published | Yes |
| Event | International Conference on Control, Automation and Systems, ICCAS 2010 - Gyeonggi-do, Korea, Republic of Duration: Oct 27 2010 → Oct 30 2010 |
Other
| Other | International Conference on Control, Automation and Systems, ICCAS 2010 |
|---|---|
| Country | Korea, Republic of |
| City | Gyeonggi-do |
| Period | 10/27/10 → 10/30/10 |
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Keywords
- ADHD
- EEG
- SOM
- Wavelet
ASJC Scopus subject areas
- Artificial Intelligence
- Control and Systems Engineering
Cite this
Analysis of attention deficit hyperactivity disorder in EEG using wavelet transform and self organizing maps. / Lee, Seung Hyun; Abibullaev, Berdakh; Kang, Won Seok; Shin, Yunhee; An, Jinung.
ICCAS 2010 - International Conference on Control, Automation and Systems. 2010. p. 2439-2442 5670255.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - Analysis of attention deficit hyperactivity disorder in EEG using wavelet transform and self organizing maps
AU - Lee, Seung Hyun
AU - Abibullaev, Berdakh
AU - Kang, Won Seok
AU - Shin, Yunhee
AU - An, Jinung
PY - 2010
Y1 - 2010
N2 - This paper presents our preliminary study EEG brain signals of children with attention deficit hyperactivity disorder (ADHD) in order to support a computer assisted diagnostic system. The EEG signals were recorded from 13 children including normal and children diagnosed with ADHD. We analyzed the signals with multilevel discrete wavelet decompositions in order to extract brain signal power spectrum features. A wavelet thresholding technique was employed to further improve the data quality by denoising the artifacts. In order to discriminate the attention level in electrical brain activity of ADHD children, we used a standard Self-Organizing Map clustering technique with wavelet coefficient input features. Clustering results varied depending on the wavelet feature extraction stage, particularly it was noticed that accuracy was dependent on the type of the used wavelet function. The clustering results demonstrate that 'sym7' wavelet function provides better input feature localization to provide the accurate separation of normal and disordered children's brain activity.
AB - This paper presents our preliminary study EEG brain signals of children with attention deficit hyperactivity disorder (ADHD) in order to support a computer assisted diagnostic system. The EEG signals were recorded from 13 children including normal and children diagnosed with ADHD. We analyzed the signals with multilevel discrete wavelet decompositions in order to extract brain signal power spectrum features. A wavelet thresholding technique was employed to further improve the data quality by denoising the artifacts. In order to discriminate the attention level in electrical brain activity of ADHD children, we used a standard Self-Organizing Map clustering technique with wavelet coefficient input features. Clustering results varied depending on the wavelet feature extraction stage, particularly it was noticed that accuracy was dependent on the type of the used wavelet function. The clustering results demonstrate that 'sym7' wavelet function provides better input feature localization to provide the accurate separation of normal and disordered children's brain activity.
KW - ADHD
KW - EEG
KW - SOM
KW - Wavelet
UR - http://www.scopus.com/inward/record.url?scp=78751493183&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=78751493183&partnerID=8YFLogxK
M3 - Conference contribution
SN - 9781424474530
SP - 2439
EP - 2442
BT - ICCAS 2010 - International Conference on Control, Automation and Systems
ER -