This research explores advanced entropy-based methods and multiscale complexity analysis to decode the intricate patterns of brain signals. By quantifying neural dynamics, we aim to identify objective biomarkers for psychiatric disorders, bridging the gap between computational neuroscience and precision clinical diagnosis.

This article examines the transformative role of machine learning in modern psychiatry, focusing on its capacity to analyze complex multimodal data. By leveraging predictive modeling and pattern recognition, we investigate how AI-driven insights can enhance diagnostic accuracy and personalize treatment strategies for mental health disorders, advancing the field of digital medicine.