A system comprises a neuromonitoring system configured to generate nerve data regarding a state of a nerve of a patient during a surgical procedure on the patient. The system includes a robotic system configured to receive or generate, for the surgical procedure, location data that identifies a location of the nerve of the patient. The robotic system may cause the neuromonitoring system to be in either an active state or an inactive state based on the location data, where the active state is a state in which the neuromonitoring system provides the nerve data to the robotic system, while the inactive state is a state in which the neuromonitoring system does not provide the nerve data to the robotic system. The robotic system may further generate at least one control signal that implements one or more safeguards for the surgical procedure.

An example of a system for delivering neurostimulation and sensing one or more signals may include a programming control circuit and a parameter control circuit. The programming control circuit may be configured to control the delivery of the neurostimulation according to stimulation parameters and the sensing of a target neural signal including target neural responses according to sensing parameters. The parameter control circuit may be configured to determine the stimulation parameters and the sensing parameters and may include a recording analyzer. The recording analyzer may be configured to evaluate a sequence of test recording configurations each including a set of recording configuration parameters selected from the stimulation parameters and the sensing parameters and to determine one or more recording configurations suitable for detection of the target neural responses using an outcome of the evaluation.

The present invention relates to an integrated circuit for processing biosignals, a biosignal processing apparatus, and a biosignal processing system, and the integrated circuit includes: a digital conversion unit for converting an analog biosignal input through a biosignal input terminal into a digital biodata; and an AI block for processing a plurality of biodata converted through the digital conversion unit according to an artificial intelligence processing flow, and outputting a result data according to processing of the plurality of biodata.

A system includes a first electrode, a second electrode, and a suture structure. The first electrode and the second electrode are both coupled to the suture structure. The system may deliver, via the first electrode, electrical stimulation signals to a nerve or nerve branch. The system may sense, via the second electrode, response signals based on delivering the electrical stimulation signals. The system may control parameters associated with delivering the electrical stimulation signals, based on sensing the response signals.

The disclosure relates to a computer-implemented method for monitoring diaphragmatic response to phrenic nerve stimulation. The method comprises receiving in real-time a diaphragmatic CMAP signal. The method comprises computing a baseline value of a characteristic of the CMAP signal. The characteristic represents a diaphragmatic response intensity to a phrenic nerve stimulation. The method comprises determining a threshold value of the characteristic, representing a boundary of values of the characteristic indicative of upcoming diaphragmatic palsy. The determining of the threshold value includes shifting the baseline value. The method comprises receiving in real-time a ECG signal. The method comprises repeating in real-time: detecting a QRS complex in the ECG signal, monitoring the CMAP signal, computing a real-time value of the characteristic, comparing the real-time value to the threshold value, and outputting an alert when the threshold is passed. The real-time value of the characteristic is asynchronous to the QRS complex.

The disclosure relates to a computer-implemented method for monitoring diaphragmatic response to phrenic nerve stimulation. The method comprises receiving in real-time a diaphragmatic CMAP signal. The method comprises computing a baseline value of a characteristic of the CMAP signal. The characteristic represents a diaphragmatic response intensity to a phrenic nerve stimulation. The method comprises determining a threshold value of the characteristic, representing a boundary of values of the characteristic indicative of upcoming diaphragmatic palsy. The determining of the threshold value includes shifting the baseline value. The method comprises receiving in real-time a ECG signal. The method comprises repeating in real-time: detecting a QRS complex in the ECG signal, monitoring the CMAP signal, computing a real-time value of the characteristic, comparing the real-time value to the threshold value, and outputting an alert when the threshold is passed. The real-time value of the characteristic is asynchronous to the QRS complex.

A system and method for generating a neuropathologic nourishment program comprises a computing device configured to obtain a neural element from a neural monitoring component, generate at least a neural profile as a function of the neural element, wherein generating comprises receiving at least a neural cluster as a function of a neural counsel, and generating the neural profile as a function of the neural cluster and neural element using a neural machine-learning model, identify at least an edible as a function of the neural profile, wherein identifying comprises obtaining a nourishment composition from an edible directory, determining a nourishment abnormality as a function of the neural profile and a normal range, and identifying an edible using the nourishment composition, nourishment abnormality, and an edible machine-learning model, and generate a nourishment program of a plurality of nourishment programs as a function of the edible.

A system and method for generating a neuropathologic nourishment program comprises a computing device configured to obtain a neural element from a neural monitoring component, generate at least a neural profile as a function of the neural element, wherein generating comprises receiving at least a neural cluster as a function of a neural counsel, and generating the neural profile as a function of the neural cluster and neural element using a neural machine-learning model, identify at least an edible as a function of the neural profile, wherein identifying comprises obtaining a nourishment composition from an edible directory, determining a nourishment abnormality as a function of the neural profile and a normal range, and identifying an edible using the nourishment composition, nourishment abnormality, and an edible machine-learning model, and generate a nourishment program of a plurality of nourishment programs as a function of the edible.

A computer-implemented method for pelvic floor feedback. The method includes capturing a strength of action potentials via wireless sensors, the wireless sensors positioned proximate to a pelvic floor of a user. The method also includes transmitting the strength of the action potentials to a mobile device. The method also includes recording the strength of the action potentials on the mobile device.

A computer-implemented method for pelvic floor feedback. The method includes capturing a strength of action potentials via wireless sensors, the wireless sensors positioned proximate to a pelvic floor of a user. The method also includes transmitting the strength of the action potentials to a mobile device. The method also includes recording the strength of the action potentials on the mobile device.