AN INTELLIGENT DEPRESSION DETECTION MODEL BASED ON MULTIMODAL FUSION TECHNOLOGY

Abstract

Depression is a prevalent mental condition, and it is essential to diagnose and treat patients as soon as possible to maximize their chances of rehabilitation and recovery. An intelligent detection model based on multimodal fusion technology is proposed based on the findings of this study to address the difficulties associated with depression detection. Text data and electroencephalogram (EEG) data are used in the model as representatives of subjective and objective nature, respectively. These data are processed by the BERT–TextCNN model and the CNN–LSTM model, which are responsible for processing them. While the CNN–LSTM model is able to handle time-series data in an effective manner, the BERT–TextCNN model is able to adequately capture the semantic information that is included in text data. This enables the model to consider the various features that are associated with the various types of data. In this research, a weighted fusion technique is utilized to combine the information contained within the two modal datasets. This strategy involves assigning a weight to the outcomes of each modal data processing in accordance with the degree of contribution that each modal data will make to produce the ultimate depression detection results. In regard to the task of depression identification, the suggested model demonstrates great validity and robustness, as demonstrated by the results of the experimental validation that we carried out on a dataset that we manufactured ourselves. A viable and intelligent solution for the early identification of depression is provided by the proposed model. This solution will likely be widely utilized in clinical practice and will provide new ideas and approaches for the growth of the field of precision medicine.