This thesis presents the development and application of a physiological fitness landscape methodology for the analysis of real human biomarker datasets, with the aim of providing a dynamic, interpretable and integrative representation of physiological stability and adaptability. Traditonal analyses of electrocardiogram (ECG) and electroencephalogram (EEG) signals typically rely on isolated features and static metrics, such as spectral power measures, which may fail to capture variations in physiological equilibrium under external perturbations. The proposed framework is based on concepts derived from statistical physics and phase transition theory, where physiological states are represented as landscapes characterized by valleys and peaks, which number correspondes to stable, metastable, and unstable configurations.