Motif Discovery Framework for Psychiatric EEG Data Classification

2025-01-08

AI Summary

Current clinical assessment of treatment efficacy for psychiatric disorders—such as major depressive disorder—suffers from significant delays (4–6 weeks), exacerbating patient morbidity and healthcare burden. To address this, we propose a novel early response prediction framework leveraging dynamic electroencephalography (EEG) recorded at day 7 of treatment. This work introduces, for the first time, time-series motif mining into psychiatric treatment response evaluation. We develop a generalizable EEG dynamic feature extraction paradigm via Symbolic Aggregate approXimation (SAX) and frequent motif discovery, applicable across diagnostic categories and heterogeneous populations. The extracted features are integrated with lightweight classifiers (SVM and Random Forest). Our framework achieves high classification accuracy on four distinct clinical datasets: major depressive disorder, schizophrenia, pediatric drug-resistant epilepsy, and Alzheimer’s disease. Results demonstrate that EEG motifs possess strong discriminative power for both cross-diagnostic stratification and early treatment response prediction—establishing a new paradigm for precision, early intervention in neuropsychiatric disorders.

Technology Category

• Machine Learning: Time-Series/Data Streams
• Cognitive Modeling & Cognitive Systems: Neural Spike Coding
• Data Mining & Knowledge Management: Data Stream Mining

Application Category

• Graph Algorithms and Modeling for the Web: Representation, reconstruction, and subgraph or motif discovery in Web-related graphs
• User Modeling, Personalization and Recommendation: Psychology-informed user models and recommender systems
• Web Mining and Content Analysis: Large pretrained models with web data

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.

Problem

• Antidepressant Efficacy
• Early Prediction
• Treatment Response

Innovation

• EEG Analysis
• Early Prediction
• Treatment Response