Brain music as a potential tool for diagnosing attention-deficit/hyperactivity disorder (ADHD)

Abstract

The first goal of this thesis was to prove the possibility that those with ADHD could be diagnosed using sounds derived from their brainwaves. For this, I made the following specific goals. The first was to develop ADHD diagnosing algorithms utilizing EEG brainwaves through several mathematical methods, specially using eyes-open, resting-state brainwaves. The second was to develop a sonifcation algorithm to convert brainwaves to musical sounds, which is needed for a future brainwave stethoscope for diagnosis of ADHD patients. For this, after recording the brainwaves of 20 people (10: controls, 10: ADHD) during eyes-open resting-state, Fast Fourier Transform was applied to their brainwaves to get energy per frequency band, and per channel, of the brain. Finally, 11 among 248 features, which would be used for training classification function, were selected after applying an ANOVA test and Fishier discriminant analysis, and comparing the level of overlap. By training SVM with the selected 11 features based on the brainwaves of 20 people, SVM was given the ability to classify. Then, the distances derived from the SVM result were transformed to musical sounds while making the difference between the brainwaves of ADHD and control subjects bigger than before and arranging that they be expressed as a brainwave music form which enabled people to classify two classes with a high precision level. To validate this brain music, I played the musical sounds to 29 participants and let them distinguish the experimental class of the sounds and measure their certainty based on a 7th LIKERT scale. The experimental results were as follows: for SVM classification, 97. 667% precision was gained, and for the music classification test, a sensitivity of 97.9%, a specificity of 96.55%, a positive predictive rate of 96.6%, and a negative predictive rate of 97.9% were gained. These results imply that the brainwaves of those with and without ADHD can be distinguished using musical sounds derived from their brainwaves, and that a brainwave stethoscope can be utilized for ADHD patients in the future. Furthermore, the high SVM classification result proved that brainwaves during eyes-open resting-state between healthy people and ADHD patients also have such significantly meaningful differences that they can be utilized for making a future stethoscope. Moreover, after applying power spectrum analysis to the brainwaves during eyes-open resting-state

differences in the gamma, beta, and theta activities according to the location on the brain were found. These findings were not discovered in previous relevant studies in detail and the reason for those brainwave-activity distribution-differences, according to the location of the brain. has to be identified in future research. In conclusion, future applications of this research could result in doctors being able to diagnose ADHD by just listening to sounds converted from brainwaves without consuming much time, and SVM could be utilized to make brainwave music for this application and to make a high classification result.