Abstract: The present invention provides systems and methods for reconstructing continuous physiological signals from non-invasive input signals using a modular framework combining fractional calculus, time-frequency transformations, and deep learning. Input signals acquired from sensors such as ECG, PPG, or SCG undergo preprocessing that includes normalization and computation of fractional derivatives to capture fine-grained temporal dynamics. A first neural network applies an adaptive, learnable time-frequency transformation—such as a complex Morse wavelet transform—to extract meaningful representations. These are then processed by a second neural network to reconstruct continuous signals, such as arterial blood pressure, in real time. The networks are trained using loss functions like mean squared error and dynamic time warping against reference signals. The system operates without requiring calibration and generalizes across populations and sensor conditions.