Abstract: Methods for optimization of surgical placement of an implantable electrode for spinal cord epidural stimulation of a subject include creating a computational model of the subject spinal cord based on medical imagery, determining the position of the lumbosacral enlargement, and determining an optimal placement to maximize volumetric coverage of the lumbosacral enlargement by the implantable electrode.

Abstract: An automated segmentation system for medical imaging data segments data into muscle and fat volumes, and separates muscle volumes into discrete muscle group volumes using a plurality of models of the medical imaging data, and wherein the medical imaging data includes data from a plurality of imaging modalities.

Abstract: Computer-implemented systems and methods for determining epidural spinal stimulation parameters that promote muscle activation use spectral analysis and machine learning techniques to characterize electromyography data.

Abstract: An automated segmentation system for medical imaging data segments data into muscle and fat volumes, and separates muscle volumes into discrete muscle group volumes using a plurality of models of the medical imaging data, and wherein the medical imaging data includes data from a plurality of imaging modalities.

Abstract: Computer-implemented systems and methods for determining epidural spinal stimulation parameters that promote muscle activation use spectral analysis and machine learning techniques to characterize electromyography data.

Abstract: An automated segmentation system for medical imaging data segments data into muscle and fat volumes, and separates muscle volumes into discrete muscle group volumes using a plurality of models of the medical imaging data, and wherein the medical imaging data includes data from a plurality of imaging modalities.

Abstract: Computer-implemented systems and methods for determining epidural spinal stimulation parameters that promote muscle activation use spectral analysis and machine learning techniques to characterize electromyography data.

Abstract: Embodiments of the present invention relate to a novel approach to automatically detect the occurrence of evoked potentials, quantify the attributes of the signal and visualize the effect across a high number of spinal cord epidural stimulation parameters. This new method is designed to automate the current process for performing this task that has been accomplished manually by data analysts through observation of the raw EMG signals, which is laborious and time-consuming as well as being prone to human errors. The proposed method provides fast and accurate framework for activation detection and visualization of the results within five main algorithms.