Multimodal Data-Driven Classification of Mental Disorders: A Comprehensive Approach to Diagnosing Depression, Anxiety, and Schizophrenia

TL;DR

This paper presents a comprehensive multimodal data-driven approach combining EEG and sociodemographic data, utilizing deep learning and big data tools to improve the diagnosis of mental disorders like depression, anxiety, and schizophrenia.

Contribution

It introduces a novel classification pipeline integrating EEG features with sociodemographic data using CNNs and Apache Spark for big data analysis, enhancing diagnostic accuracy.

Findings

Coherence features significantly improve classification accuracy.

Multimodal data integration enhances robustness of mental disorder diagnosis.

Deep learning methods effectively analyze large-scale psychiatric datasets.

Abstract

This study investigates the potential of multimodal data integration, which combines electroencephalogram (EEG) data with sociodemographic characteristics like age, sex, education, and intelligence quotient (IQ), to diagnose mental diseases like schizophrenia, depression, and anxiety. Using Apache Spark and convolutional neural networks (CNNs), a data-driven classification pipeline has been developed for big data environment to effectively analyze massive datasets. In order to evaluate brain activity and connection patterns associated with mental disorders, EEG parameters such as power spectral density (PSD) and coherence are examined. The importance of coherence features is highlighted by comparative analysis, which shows significant improvement in classification accuracy and robustness. This study emphasizes the significance of holistic approaches for efficient diagnostic tools by integrating a variety of data sources. The findings open the door for creative, data-driven approaches to treating psychiatric diseases by demonstrating the potential of utilizing big data, sophisticated deep learning methods, and multimodal datasets to enhance the precision, usability, and comprehension of mental health diagnostics.