Abstract: A method and system for predicting process information (e.g., phase data) using a given input (e.g., intensity) to a parameterized model are described. A latent space of a given input is determined based on dimensional data in a latent space of the parameterized model for a given input to the parameterized model. Further, an optimum latent space is determined by constraining the latent space with prior information (e.g., wavelength) that enables converging to a solution that causes more accurate predictions of the process information. The optimum latent space is used to predict the process information. The given input may be a measured amplitude (e.g., intensity) associated with the complex electric field image. The predicted process information can be complex electric field image having amplitude data and phase data. The parameterized model comprises variational encoder-decoder architecture.

Abstract: A neuromodulation therapy is delivered via at least one electrode implanted subcutaneously and superficially to a fascia layer superficial to a nerve of a patient. In one example, an implantable medical device is deployed along a superficial surface of a deep fascia tissue layer superficial to a nerve of a patient. Electrical stimulation energy is delivered to the nerve through the deep fascia tissue layer via implantable medical device electrodes.

Abstract: A neuromodulation therapy is delivered via at least one electrode implanted subcutaneously and superficially to a fascia layer superficial to a nerve of a patient. In one example, an implantable medical device is deployed along a superficial surface of a deep fascia tissue layer superficial to a nerve of a patient. Electrical stimulation energy is delivered to the nerve through the deep fascia tissue layer via implantable medical device electrodes.