A sensing electrode selection algorithm is disclosed for use with an implantable pulse generator having an electrode array. The algorithm automatically selects optimal sensing electrodes in the array to be used with a pre-determined stimulation therapy appropriate for the patient. The algorithm preferably senses stimulation artifacts using different sensing electrodes, and more specifically different sensing electrode pairs as is appropriate when differential sensing is used. The algorithm further preferably senses these stimulation artifacts with the patient placed in two or more postures. The algorithm processes the stimulation artifact features measured at the different sensing electrodes and at the different postures to automatically determine one or more sensing electrode pairs that best distinguishes the two or more postures given the prescribed stimulation therapy.

Methods and systems for providing closed loop control of stimulation provided by an implantable stimulator device are disclosed herein. The disclosed methods and systems use a neural feature prediction model to predict a neural feature, which is used as a feedback control variable for adjusting stimulation. The predicted neural feature is determined based on one or more signals from an accelerometer configured in contact with the patient. The disclosed methods and systems can be used to provide closed loop feedback in situations, such as sub-perception therapy, when neural features cannot be readily directly measured.

Disclosed is an instrument assembly for a selected procedure. The procedure may include a dissection and neural monitoring. The instrument may be insulated to allow for a selected and precise electrical conductive path.

An implantable medical device (IMD) includes multiple stimulation engines (SEs) for independently stimulating respective electrode sets of a lead system. A voltage multiplier (VM) is configured to generate an adjustable target voltage at an output node. Each stimulation engine includes first switching circuitry to switchably connect an anodic node of the SE to the VM output node and second switching circuitry to switchably connect a cathodic node of the SE to a current sink circuit. Discharge switching circuitry may be disposed between the anodic and cathodic nodes of each SE. A selector and associated digital control logic block are operative to generate control signals for independently controlling respective SEs such that each SE may be activated to stimulate or discharge a corresponding select set of electrodes independently from or in concert with remaining SEs.

Provided is a method of analyzing electrical characteristics of nerves, the method including generating an input electrical signal to be applied to a nerve, obtaining an output electrical signal based on measuring a nerve signal generated from the nerve in response to the input electrical signal, obtaining output frequency components, which are frequency components of the output electrical signal, based on converting the output electrical signal into a frequency domain, and obtaining conductance of the nerves and capacitance of the nerves based on the output frequency components.

Provided is a method of analyzing electrical characteristics of nerves, the method including generating an input electrical signal to be applied to a nerve, obtaining an output electrical signal based on measuring a nerve signal generated from the nerve in response to the input electrical signal, obtaining output frequency components, which are frequency components of the output electrical signal, based on converting the output electrical signal into a frequency domain, and obtaining conductance of the nerves and capacitance of the nerves based on the output frequency components.

A system for detecting strokes includes a sensor device configured to obtain physiological data from a patient, for example brain activity data. The sensor device can include electrodes configured to be disposed at the back of the patient's neck or base of the skull. The electrodes can detect electrical signals corresponding to brain activity in the P3, Pz, and/or P4 brain regions or other brain regions. A computing device communicatively coupled to the sensor device is configured to receive the physiological data and analyze it to indicate whether the patient has suffered a stroke.

A method for assessing peripheral nerve stimulation (PNS) for a coil geometry includes retrieving a PNS Huygens' P-matrix for a body model. The PNS Huygens' P-matrix is defined on a Huygens' surface enclosing the body model. The method further includes generating a coil specific PNS P-matrix for the coil geometry based on at least the PNS Huygens' P-matrix for the body model, determining at least one PNS threshold for the coil geometry based on the coil specific PNS P-matrix, and storing the at least one PNS threshold in a storage device.

A method for assessing peripheral nerve stimulation (PNS) for a coil geometry includes retrieving a PNS Huygens' P-matrix for a body model. The PNS Huygens' P-matrix is defined on a Huygens' surface enclosing the body model. The method further includes generating a coil specific PNS P-matrix for the coil geometry based on at least the PNS Huygens' P-matrix for the body model, determining at least one PNS threshold for the coil geometry based on the coil specific PNS P-matrix, and storing the at least one PNS threshold in a storage device.

Methods and systems for providing closed loop control of stimulation provided by an implantable stimulator device are disclosed herein. The disclosed methods and systems use a neural feature prediction model to predict a neural feature, which is used as a feedback control variable for adjusting stimulation. The predicted neural feature is determined based on one or more signals from an accelerometer configured in contact with the patient. The disclosed methods and systems can be used to provide closed loop feedback in situations, such as sub-perception therapy, when neural features cannot be readily directly measured.