Depression assessment using integrated multi-featured EEG bands deep neural network models: Leveraging ensemble learning techniques

• Published in: Computational and structural biotechnology journal Vol. 23; pp. 1450 - 1468
• Main Authors: Chung, Kuo-Hsuan, Chang, Yue-Shan, Yen, Wei-Ting, Lin, Linen, Abimannan, Satheesh
• Format: Journal Article
• Language: English
• Published: Netherlands Elsevier B.V 01.12.2024; Research Network of Computational and Structural Biotechnology; Elsevier
• Subjects: biotechnology; commercialization; Deep neural network; EEG signal; electroencephalography; Ensemble learning; Mental status assessment; Multi featured deep learning; neural networks; regression analysis; Mental status assessment; Deep neural network; Ensemble learning; Multi featured deep learning; EEG signal
• ISSN: 2001-0370; 2001-0370
• DOI: 10.1016/j.csbj.2024.03.022

Mental Status Assessment (MSA) holds significant importance in psychiatry. In recent years, several studies have leveraged Electroencephalogram (EEG) technology to gauge an individual's mental state or level of depression. This study introduces a novel multi-tier ensemble learning approach to integrate multiple EEG bands for conducting mental state or depression assessments. Initially, the EEG signal is divided into eight sub-bands, and then a Long Short-Term Memory (LSTM)-based Deep Neural Network (DNN) model is trained for each band. Subsequently, the integration of multi-band EEG frequency models and the evaluation of mental state or depression level are facilitated through a two-tier ensemble learning approach based on Multiple Linear Regression (MLR). The authors conducted numerous experiments to validate the performance of the proposed method under different evaluation metrics. For clarity and conciseness, the research employs the simplest commercialized one-channel EEG sensor, positioned at FP1, to collect data from 57 subjects (49 depressed and 18 healthy subjects). The obtained results, including an accuracy of 0.897, F1-score of 0.921, precision of 0.935, negative predictive value of 0.829, recall of 0.908, specificity of 0.875, and AUC of 0.8917, provide evidence of the superior performance of the proposed method compared to other ensemble learning techniques. This method not only proves effective but also holds the potential to significantly enhance the accuracy of depression assessment. [Display omitted]