Nombre: LORRAINE MARQUES ALVES
Fecha de publicación: 23/03/2023
Junta de examinadores:
Nombre |
Papel |
|---|---|
| ELIAS SILVA DE OLIVEIRA | Examinador Interno |
| JUGURTA ROSA MONTAVÃO FILHO | Examinador Externo |
| KARIN SATIE KOMATI | Examinador Externo |
| PATRICK MARQUES CIARELLI | Presidente |
| RODRIGO VAREJÃO ANDREÃO | Examinador Externo |
Sumario: The electroencephalogram (EEG) is an important source of signals for assisting in the
diagnosis of mental disorders, and new research is constantly exploring ways to use these
signals to improve the quality of medical diagnosis. Diseases such as schizophrenia, epilepsy,
attention-deficit/hyperactivity disorder (ADHD), depression, dementia, and alcoholism,
among others, are examples of how the application of EEG reading and decoding techniques
can be of great value in supporting medical diagnosis. Studies of brain dynamics using
EEG have revealed that global neural activity can be described by a limited number of
scalp topographic electric potential maps, called microstates. This work proposes new
methodologies for decoding EEG signals and their application to public databases for the
detection of schizophrenia, depression, and ADHD by exploring approaches applied to EEG
microstates addressing the problem of binary classification (disorder vs. healthy control).
In addition, a third approach was proposed to solve a multiclass classification problem
for the simultaneous detection of schizophrenia, depression, and dementia. The proposed
methodologies are based on complex network theory and natural language processing.
Both proposals allow an understanding of how the brain signals of an individual with one
of the mentioned mental disorders differ from those of a healthy person. The complex
network theory allowed the determination of important topological characteristics of
the constructed microstate networks, resulting in an average accuracy of 100.0% for
schizophrenia and depression, and for ADHD, the average accuracies were 99.44% (ADHD
vs. healthy) and 98.61% (ADHD-I vs. ADHD-C vs. healthy). The application of natural
language processing on symbolic sequences of microstates revealed the importance of the
information contained in a window of neighborhoods of the microstate symbolic sequence in
characterizing patients with mental disorders, resulting in an average accuracy of 100.0% for
schizophrenia, 98.47% for depression, and for ADHD, the average accuracies were 99.38%
(ADHD vs. healthy) and 98.19% (ADHD-I vs. ADHD-C vs. healthy). A third approach
derived from natural language processing allowed the solution of a classification problem
involving multiple disorders, resulting in an average accuracy of 99.19% (schizophrenia
vs. depression vs. dementia vs. healthy). These proposals contribute to the assistance in
the process of diagnosing mental disorders, being a promising tool in the development of
AI-based psychiatry.
