Machine Learning Applied to Physiological Complexity

Physiological signals—from retinal responses to heart rate variability patterns—contain intricate temporal architectures. We integrate advanced signal processing theories with rigorous Artificial Intelligence algorithms.

Our team builds predictive frameworks (leveraging architectures like CNNs, SVMs, and hybrid combined models) trained on both hand-crafted complex features and raw time-series data. By emphasizing methodological rigor, proper cross-validation, and demographic normalization (addressing age and sex effects), we translate conceptual biological data patterns into robust, deployable classification models for assessing neurological and cardiovascular health.