Graph Theory Analysis of Microstates in Attention-Deficit Hyperactivity Disorder

Lorraine  M. Alves; Klaus  F. Côco; Mariane  L. de Souza; Patrick  M. Ciarelli

doi:10.48011/asba.v2i1.1481

Lorraine M. Alves Universidade Federal do Espírito Santo
Klaus F. Côco Universidade Federal do Espírito Santo
Mariane L. de Souza Universidade Federal do Espírito Santo
Patrick M. Ciarelli Universidade Federal do Espírito Santo

DOI: https://doi.org/10.48011/asba.v2i1.1481

Keywords: EEG-Microstates, ADHD, Signal Processing, Graph Theory, Classification

Abstract

Attention-Deficit Hyperactivity Disorder (ADHD) is one of the most common disorders of childhood and youth. The diagnosis of ADHD remains essentially clinical, based on history and questionnaires for symptom assessment, therefore, a biomarker can be of great value to reduce the inherent uncertainty of clinical diagnosis. In recent years, several studies have been carried out to assess the usefulness of neurophysiological (electroencephalography - EEG)
and functional image data to assist in the process of diagnosing ADHD. Previous researches have revealed evidences that microstates are selectively affected by ADHD, indicating that their analysis may be a useful tool in methods of automatic disease identication. In this paper is proposed a new methodology for the detection of ADHD using EEG microstate analysis and graph theory. The proposed method allows modeling and interpreting each microstate as a complex network, which permits to identify the effect of ADHD on some characteristics of the built networks. In addition, it provides useful information to identify ADHD and subtypes patients with an accuracy around 99%, indicating that the proposed method is promising.