Several studies have made use of EEG features to detect specific mental health illnesses such as epilepsy or schizophrenia, as supplementary diagnosis to the usual symptom-based diagnoses. At the same time general mental health diagnostic tools (biomarker or symptom-based) to identify individuals who are manifesting early signs of mental health disorders are not commonly available. This thesis seeks to explore the potential use of EEG features as a biomarker-based tool for general mental health diagnosis.
Specifically, the predictive ability using machine learning of a general biomarker derived from EEG readings elicited from an oddball auditory experiment to predict someone’s mental health status (mentally ill or healthy) is investigated in this study. Given that mindfulness exercises are regularly provided as treatment for a wide range of mental illnesses, the features of interest seek to quantify it as a measure of mental health. The 2 feature sets developed and tested in this study were collected from a traumatic brain injury (TBI) and healthy controls dataset. Further testing of these feature sets was done on the Bipolar and Schizophrenia Network on Intermediate Phenotypes (BSNIP) dataset containing multiple mental illnesses and healthy controls to test the features for generalizability. Feature Set 1 consisted of the average and variance of P300 and N200 ERP component peak amplitudes and latencies across the centroparietal and fronto-central EEG channels respectively. Feature Set 2 contains the average and variance of P300 and N200 ERP component mean amplitudes across the centro-parietal andfronto-central EEG channels respectively.
The predictive ability of these 2 feature sets was tested. Logistic regression, support vector machines, decision trees, random forests, KNN classification algorithms were used, and random forest and KNN were used in combination with oversampling to predict the mental health status of the subjects (whether they were cases or healthy controls). The model performance was tested using accuracy, precision, sensitivity, specificity, f1 score, confusion matrices, and AUC of the ROC.
The results of this thesis show promise on the use of EEG features as biomarkers to diagnose mental illnesses or to get a better understanding of mental wellness. The use of this technology opens doors for more accurate, biomarker-based diagnosis of mental health conditions, lowering the cost of mental health care, and making mental health care accessible for more people.
| Identifer | oai:union.ndltd.org:uottawa.ca/oai:ruor.uottawa.ca:10393/44816 |
| Date | 17 April 2023 |
| Creators | Talekar, Akshay |
| Contributors | Fraser, Maia |
| Publisher | Université d'Ottawa / University of Ottawa |
| Source Sets | Université d’Ottawa |
| Language | English |
| Detected Language | English |
| Type | Thesis |
| Format | application/pdf, application/octet-stream, application/octet-stream, application/octet-stream |
| Rights | Attribution 4.0 International, http://creativecommons.org/licenses/by/4.0/ |
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