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US 2025/0358021 A1Published application
Systems and methods for continuous signal generation using transformations
AssigneeEMx Systems LLC
InventorBenjamin Charles Shank
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AssigneeEMx Systems LLC
InventorBenjamin Charles Shank
Estimating the term…
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. This architecture enables accurate, calibration-free signal monitoring applicable to healthcare, industrial diagnostics, and beyond, offering a scalable solution for real-time signal reconstruction from multimodal, non-invasive biosensors.
A system for generating at least one continuous signal from at least one input signal, the system comprising:
A method for generating at least one continuous signal from at least one input signal, the method comprising:
A non-transitory computer-readable medium storing instructions that, when executed by at least one processor, causes the processor to:
Dependencies are inferred from the claim text.