Clinical Psychopharmacology and Neuroscience Papers in Press available online.

 
Automatic Diagnosis of Attention Deficit Hyperactivity Disorders with Continuous Wavelet Transform and Convolutional Neural Network
Sinan ALTUN , Ahmet ALKAN , Hatice ALTUN
1DEPARTMENT OF ELECTRICAL AND ELECTRONICS ENGINEERING /KAHRAMANMARAS SUTCU IMAM UNIVERSITY, 2DEPARTMENT OF ELECTRICAL AND ELECTRONICS ENGINEERING /KAHRAMANMARAS SUTCU IMAM UNIVERSITY, 3DEPARTMENT OF CHILD AND ADOLESCENT PSYCHIATRY AND DIEASES, FACULTY OF MEDICINE, KAHRAMANMARAS SUTCU IMAM UNIVERSITY
Abstract
The attention deficit and hyperactivity disorder has a negative impact on the child's educational life and relationships with the social environment during childhood and adolescence. The connection between temperament traits and The attention deficit and hyperactivity disorder has been proven by various studies. As far as we know, there is no machine learning study to diagnose The attention deficit and hyperactivity disorder in a dataset created using temperament characteristics. In this respect, this study includes original qualities and innovations. Many different deep learning methods were used in the research. Deep learning methods are models that achieve high success by using a large number of images in various image processing competitions. The images of the signals in the data set were first obtained by Continuous Wavelet Transform. The highest classification success in our data set was obtained with the Squeeze Net model with 88.33%.
Accepted Manuscript [Submitted on 2021-09-08, Accepted on 2021-10-30]