A Hybrid CNN-HMM Framework for EEG-Based Classification of Anxiety, Chronic Stress, and Depression

Document Type : Original Article

Authors

1 Department of Electrical and Computer Engineering, Hamedan University of Technology, Hamedan, Iran

2 Department of Electrical and computer Engineering, Hamedan University of Technology, Hamedan, Iran.

10.22091/jdaid.2026.16603.1082

Abstract

One of the biggest challenges in clinical medicine is early and accurate diagnosis of brain disorders, especially functional and psychological disorders like stress, anxiety and depression. While conventional machine learning relies on hand engineered time-consuming features, deep learning models, despite showing superior accuracy, can be limited in clinic application by interpretability issues because of their “black-box” design. In this paper we present a novel hybrid approach for brain disorder detection and classification through the analysis of EEG signals. Our model combines the strength of automatic feature extraction of Convolutional Neural Network (CNN) and time sequence modelling & interpretation of Hidden Markov Model (HMM). A real clinical data from 200 individuals (healthy group, anxiety group, chronic stress group, depression group) is collected and used to train and evaluate our system. This local collected data is for the first time used to validate models. Our results show that the hybrid approach achieved 92.30 percent in 4-class classification, and showed superiority to other standard approaches. The interpretable nature by HMM transition matrices also make it more relevant to aid clinical decision-making.

Keywords

Main Subjects


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