Analysis of attention deficit hyperactivity disorder in EEG using wavelet transform and self organizing maps

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.

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