Machine-based learning system: classification of ADHD and non-ADHD participants
Alp, Y. K.
Proceedings of the IEEE 25th Signal Processing and Communications Applications Conference, SIU 2017
Item Usage Stats
MetadataShow full item record
Attention-deficit/hyperactivity disorder (ADHD) is the most frequent diagnosis among children who are referred to psychiatry departments. Although ADHD was discovered at the beginning of the 20th century, its diagnosis is confronted with many problems. In this paper, a novel classification approach that discriminates ADHD and non-ADHD groups over the time-frequency domain features of ERP recordings is presented. Support Vector Machine-Recursive Feature Elimination (SVM-RFE) was used to obtain best discriminating features. When only three of these features were used the accuracy of classification reached to 98%, and use of six features further improved classification accuracy to 99.5%. The proposed scheme was tested with a new experimental setup and 100% accuracy is obtained. The results were obtained using RCV. The classification performance of this study suggests that TFHA can be employed as a core component of the diagnostic and prognostic procedures of various psychiatric illnesses.
KeywordsAttention-deficit/hyperactivity disorder (ADHD)
Support vector machine-recursive feature elimination (SVM-RFE)
Time-frequency Hermite atomizer
Accuracy of classifications
Published Version (Please cite this version)http://dx.doi.org/10.1109/SIU.2017.7960704
Showing items related by title, author, creator and subject.
İkizler, Nazlı; Güvenir, H. Altay (Springer, Berlin, Heidelberg, 2003)There is a great need for classification methods that can properly handle asymmetric cost and benefit constraints of classifications. In this study, we aim to emphasize the importance of classification benefits by means ...
Yurtman, A.; Barshan, B. (Taylor and Francis Inc., 2016)This article provides a comparative study on the different techniques of classifying human activities using tag-based radio-frequency (RF) localization. A publicly available dataset is used where the position data of ...
Can, Gökmen; Akbaş, Cem Emre; Çetin, Ahmet Enis (IEEE, 2017-08-09)We introduce a time-frequency scattering method using hyperbolic tangent function for vessel sound classification. The sound data is wavelet transformed using a two channel filter-bank and filter-bank outputs are scattered ...