The Effect of Acute Stress on the Interpretability and Generalization of Schizophrenia Predictive Machine Learning Models

TL;DR

This study investigates how acute stress during EEG recording impacts the accuracy and generalization of machine learning models for schizophrenia diagnosis, proposing a method to mitigate stress artifacts and improve model performance.

Contribution

The paper introduces a novel approach to identify and compensate for stress artifacts in EEG data, enhancing the robustness of schizophrenia predictive models.

Findings

Stress levels vary across EEG sessions and affect model accuracy.

Compensating for stress artifacts improves model generalization.

Managing patient stress during EEG enhances diagnostic performance.

Abstract

Introduction Schizophrenia is a severe mental disorder, and early diagnosis is key to improving outcomes. Its complexity makes predicting onset and progression challenging. EEG has emerged as a valuable tool for studying schizophrenia, with machine learning increasingly applied for diagnosis. This paper assesses the accuracy of ML models for predicting schizophrenia and examines the impact of stress during EEG recording on model performance. We integrate acute stress prediction into the analysis, showing that overlapping conditions like stress during recording can negatively affect model accuracy. Methods Four XGBoost models were built: one for stress prediction, two to classify schizophrenia (at rest and task), and a model to predict schizophrenia for both conditions. XAI techniques were applied to analyze results. Experiments tested the generalization of schizophrenia models using their datasets' healthy controls and independent health-screened controls. The stress model identified high-stress subjects, who were excluded from further analysis. A novel method was used to adjust EEG frequency band power to remove stress artifacts, improving predictive model performance. Results Our results show that acute stress vary across EEG sessions, affecting model performance and accuracy. Generalization improved once these varying stress levels were considered and compensated for during model training. Our findings highlight the importance of thorough health screening and management of the patient's condition during the process. Stress induced during or by the EEG recording can adversely affect model generalization. This may require further preprocessing of data by treating stress as an additional physiological artifact. Our proposed approach to compensate for stress artifacts in EEG data used for training models showed a significant improvement in predictive performance.