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

Kuo Hsuan Chung, Yue Shan Chang, Wei Ting Yen, Linen Lin, Satheesh Abimannan

Research output: Contribution to journalArticlepeer-review

Abstract

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.

Original languageEnglish
Pages (from-to)1450-1468
Number of pages19
JournalComputational and Structural Biotechnology Journal
Volume23
DOIs
Publication statusPublished - Dec 2024

Keywords

  • Deep neural network
  • EEG signal
  • Ensemble learning
  • Mental status assessment
  • Multi featured deep learning

ASJC Scopus subject areas

  • Biotechnology
  • Biophysics
  • Structural Biology
  • Biochemistry
  • Genetics
  • Computer Science Applications

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