Arousal and Valence Classification Model Based on Long Short-Term Memory and DEAP Data for Mental Healthcare Management

• Published in: Healthcare informatics research Vol. 24; no. 4; pp. 309 - 316
• Main Authors: Choi, Eun Jeong, Kim, Dong Keun
• Format: Journal Article
• Language: English
• Published: Korea (South) Korean Society of Medical Informatics 01.10.2018; The Korean Society of Medical Informatics; 대한의료정보학회
• Subjects: arousal and valence analysis; classification; deap dataset; machine learning; Original; supervised machine learning; 예방의학; DEAP Dataset; Arousal and Valence Analysis; Supervised Machine Learning; Machine Learning; Classification
• ISSN: 2093-3681; 2093-369X
• DOI: 10.4258/hir.2018.24.4.309

Both the valence and arousal components of affect are important considerations when managing mental healthcare because they are associated with affective and physiological responses. Research on arousal and valence analysis, which uses images, texts, and physiological signals that employ deep learning, is actively underway; research investigating how to improve the recognition rate is needed. The goal of this research was to design a deep learning framework and model to classify arousal and valence, indicating positive and negative degrees of emotion as high or low. The proposed arousal and valence classification model to analyze the affective state was tested using data from 40 channels provided by a dataset for emotion analysis using electrocardiography (EEG), physiological, and video signals (the DEAP dataset). Experiments were based on 10 selected featured central and peripheral nervous system data points, using long short-term memory (LSTM) as a deep learning method. The arousal and valence were classified and visualized on a two-dimensional coordinate plane. Profiles were designed depending on the number of hidden layers, nodes, and hyperparameters according to the error rate. The experimental results show an arousal and valence classification model accuracy of 74.65 and 78%, respectively. The proposed model performed better than previous other models. The proposed model appears to be effective in analyzing arousal and valence; specifically, it is expected that affective analysis using physiological signals based on LSTM will be possible without manual feature extraction. In a future study, the classification model will be adopted in mental healthcare management systems.