Disclosed examples include those directed to detecting and remediating detachment of electrodes from a patient. In an example, a system calculates a Pearson correlation coefficient between: (1) power spectral density of the noise and (2) power spectral density of a recorded signal (e.g., from an electrode being operated in free-run EMG mode). If the recorded signal correlates with the noise, then the system notifies the user of presence of noise (e.g., the fallen electrode). Otherwise, the recorded signal is considered as the signal of interest (e.g., a valid EMG signal).

An example method includes delivering one or more electrical stimulation signals to a patient, sensing a composite stimulation-evoked signal comprising a composite of signals generated by one or more signal sources in response to the one or more electrical stimulation signals, and controlling delivery of electrical stimulation therapy to the patient based on the composite stimulation-evoked signal.

A spinal cord stimulator (SCS) system and method for placing SCS electrodes in a patient for spinal cord stimulation therapy. The SCS system includes a stimulator and a base unit. In conjunction with a machine learning (ML) block, the base unit includes an algorithm module to store and process algorithms for processing data received from recording electrodes placed in a patient's body. The recording electrodes send electromyography (EMG) data to the algorithm module. The algorithm module processes and sends the EMG data to a display device. The displayed data is used, by a surgeon, for lateralization of the SCS electrode. The SCS system further includes algorithms to adjust stimulation parameters related to SCS electrodes based upon the surgeon's workflow. Further, the SCS system allows manual modification of stimulation parameters based upon muscle responses and the EMG data from the recording electrodes.

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.

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.

In a method of performing spinal reflex conditioning for an anatomical limb of a person, a spinal reflex is evoked by electrically stimulating a peripheral nerve of the anatomical limb, for example using stimulation electrodes disposed on an armband or leg band. The resulting spinal reflex is measured using electromyography (EMG) signals acquired from the anatomical limb. Vagus nerve stimulation (VNS) is performed in response to the measured spinal reflex satisfying a positive reinforcement criterion. The EMG may be high density EMG (HD-EMG) measured using a sleeve with a high density array of electrodes (e.g., at least 100 electrodes in an arm sleeve).

In a method of performing spinal reflex conditioning for an anatomical limb of a person, a spinal reflex is evoked by electrically stimulating a peripheral nerve of the anatomical limb, for example using stimulation electrodes disposed on an armband or leg band. The resulting spinal reflex is measured using electromyography (EMG) signals acquired from the anatomical limb. Vagus nerve stimulation (VNS) is performed in response to the measured spinal reflex satisfying a positive reinforcement criterion. The EMG may be high density EMG (HD-EMG) measured using a sleeve with a high density array of electrodes (e.g., at least 100 electrodes in an arm sleeve).

A flexible sheet for neurostimulation is described having a flexible non-conductive substrate matrix in which electrodes are embedded along a lower surface. Electrically conductive wires extend from the electrodes through the flexible substrate to another exterior surface of the substrate. Methods of making the flexible sheet and making a device using the flexible sheet are also disclosed.

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.