Motif Discovery Framework for Psychiatric EEG Data Classification

TL;DR

This paper introduces a novel motif discovery framework for EEG data that can predict psychiatric treatment responses early, aiding clinical decisions and demonstrating high accuracy across multiple psychiatric conditions.

Contribution

The paper presents the first use of motif discovery in depression diagnostics and applies it to various psychiatric EEG datasets for early response prediction.

Findings

High classification accuracy in multiple psychiatric datasets

EEG dynamic properties can predict treatment response early

Motif features effectively distinguish responders from non-responders

Abstract

In current medical practice, patients undergoing depression treatment must wait four to six weeks before a clinician can assess medication response due to the delayed noticeable effects of antidepressants. Identification of a treatment response at any earlier stage is of great importance, since it can reduce the emotional and economic burden connected with the treatment. We approach the prediction of a patient response to a treatment as a classification problem, by utilizing the dynamic properties of EEG recordings on the 7th day of the treatment. We present a novel framework that applies motif discovery to extract meaningful features from EEG data distinguishing between depression treatment responders and non-responders. We applied our framework also to classification tasks in other psychiatric EEG datasets, namely to patients with symptoms of schizophrenia, pediatric patients with intractable seizures, and Alzheimer disease and dementia. We achieved high classification precision in all data sets. The results demonstrate that the dynamic properties of the EEGs may support clinicians in decision making both in diagnosis and in the prediction depression treatment response as early as on the 7th day of the treatment. To our best knowledge, our work is the first one using motifs in the depression diagnostics in general.