The Art Therapy for Preschool Children's Mental Health Based on Artificial Intelligence and Deep Learning

This study addressed the limited language expression abilities of rural left-behind preschool children and the limitations of conventional anxiety assessment methods, which primarily rely on parent reports and expert observation. A One-Dimensional Convolutional Neural Network (CNN)-Long Short-Term Memory (LSTM) classification model integrating Electrocardiogram (ECG) and Electrodermal Activity (EDA) signals was developed. A total of 160 rural left-behind preschool children aged 3-6 years were enrolled. At the participant level, 112 children were assigned to the training set and 48 to the test set, ensuring that all signal segments obtained from the same child were included exclusively in a single dataset subset. Raw ECG and EDA signals were acquired at a sampling rate of 1,000 Hz. Following filtering, normalization, and resampling, the processed data were converted into input tensors with dimensions of 300 × 2. Anxiety levels were determined jointly using the Preschool Anxiety Scale (PAS), the Child Behavior Checklist for Ages 1.5-5 (CBCL/1.5-5), and blinded behavioral assessments conducted by three child psychologists. Physiological signals were not involved in the labeling process. In the participant-independent test, the CNN-LSTM model achieved an accuracy of 87.3%, outperforming the standalone CNN model, which achieved an accuracy of 83.8%. Furthermore, five-fold participant-level cross-validation yielded a mean accuracy of 87.4% ± 0.9%, with a 95% confidence interval ranging from 86.34% to 88.46%. These findings indicate that jointly modeling local waveform features and temporal dependencies facilitates the discrimination of anxiety levels within the current sample. The proposed model is intended as an auxiliary screening tool, and its generalizability across different geographical regions, acquisition devices, and real-world settings requires further external validation.