Panic Attack Prediction for Patients With Panic Disorder via Machine Learning and Wearable Electrocardiography Monitoring: Model Development and Validation Study.

Background: Panic attack prediction remains a critical challenge in mental health care due to the high interindividual variability of physiological responses and the limitations of subjective psychological assessments. Objective: This study aims to develop a multimodal deep learning framework that integrates real-time physiological signals from wearable electrocardiogram (ECG) monitors and psychological assessments to improve the accuracy of panic attack prediction. Methods: We adapted the ConvNetQuake architecture, originally designed for seismic detection, to extract temporal patterns from ECG signals. The model was pretrained on the PTB-XL ECG dataset and fine-tuned using wearable ECG data collected from adult participants. In parallel, psychological profiles based on the Diagnostic and Statistical Manual of Mental Disorders, 4th Edition criteria and Panic Disorder Severity Scale assessments were encoded as auxiliary inputs. The multimodal framework was evaluated using standard performance metrics. Results: The proposed model achieved an accuracy of 71.43%, precision of 83.72%, recall of 70.59%, and F1 score of 76.60% in detecting heart rate variability anomalies associated with panic episodes. Experimental comparisons demonstrated that the integration of physiological and psychological modalities significantly outperformed unimodal baselines in prediction reliability. Conclusions: This study provides empirical support for wearable-based early warning systems for panic attacks. The proposed approach demonstrates the feasibility of just-in-time digital interventions and underscores the potential of wearable artificial intelligence in advancing affective computing and digital psychiatry.