Study of Machine Learning Techniques for Predicting Panic Attacks with EEG and Personalized Binaural Beat Frequencies

Abstract

Panic attack detection and intervention remain critical challenges in mental health care due to their unpredictable nature and individual variability. This study proposes a machine learning-based framework for early detection of panic attacks using EEG-derived physiological signals, coupled with real-time personalized auditory intervention through binaural beat frequencies. Data were collected under controlled conditions using wearable biosensors to capture features such as heart rate variability, electrodermal activity, and skin temperature. A Gradient Boosting Classifier achieved 96% accuracy in detecting panic states, while an Isolation Forest algorithm effectively identified anomalous patterns preceding attacks. Based on physiological profiles, the system dynamically recommends individualized binaural beat frequencies to promote relaxation and emotional stabilization. The results demonstrate the feasibility of combining predictive modeling and neuroadaptive sound therapy to deliver scalable, non-invasive, and personalized mental health interventions. This approach aligns with global preventive health strategies, particularly those promoting digital therapeutics and early intervention for anxiety-related conditions.