A neural event process, including receiving a neural response signal, decomposing the signal using at least one wavelet, differentiating phase data of the wavelets and the response signal to determine maxima and minima of the phase data and the signal, and processing the maxima and minima to determine peaks representing neural events.

An initial set of parameters for operating one or more detection tools is automatically derived and subsequently adjusted so that each detection tool is more or less sensitive to signal characteristics in a region of interest. Detection tool(s) may be applied to physiological signals sensed from a patient (such as EEG signals) and may be configured to run in an implanted medical device that is programmable with the parameters to look for rhythmic activity, spike activity, and power changes in the sensed signals, etc. A detection tool may be selected, and parameter values derived in a logical sequence and/or in pairs based on a graphical representation of an activity type which may be selected by a user, for example, by clicking and dragging on the graphic via a GUI. Displayed simulations allow a user to assess what will be detected with a derived parameter set and then to adjust the sensitivity of the set or start over as desired.

A device determines values for one or more metrics that indicate the quality of a patient's sleep based on sensed physiological parameter values. Sleep efficiency, sleep latency, and time spent in deeper sleep states are example sleep quality metrics for which values may be determined. The sleep quality metric values may be used, for example, to evaluate the effectiveness of a therapy delivered to the patient by a medical device. In some embodiments, determined sleep quality metric values are automatically associated with the therapy parameter sets according to which the medical device delivered the therapy when the physiological parameter values were sensed, and used to evaluate the effectiveness of the various therapy parameter sets. The medical device may deliver the therapy to treat a non-respiratory neurological disorder, such as epilepsy, a movement disorder, or a psychological disorder. The therapy may be, for example, deep brain stimulation (DBS) therapy.

This invention relates generally to methods for localization and visualization of implanted electrodes and penetrating probes in the brain in 3D space with consideration of functional brain anatomy. Particularly, this invention relates to precise and sophisticated methods of localizing and visualizing implanted electrodes to the cortical surface and/or topological volumes of a patient's brain using 3D modeling, and more particularly to methods of accurately mapping implanted electrodes to the cortical topology and/or associated topological volumes of a patient's brain, such as, for example, by utilizing recursive grid partitioning on a manipulable virtual replicate of a patient's brain. This invention further relates to methods of surgical intervention utilizing accurate cortical surface modeling and/or topological volume modeling of a patient's brain for targeted placement of electrodes and/or utilization thereof for surgical intervention in the placement of catheters or other probes into it.

Novel enhanced endovascular tracing and pacing methods and systems using endovascular electrodes are described. Medial Temporal Lobe, SubCortical and Cortical sites within the posterior aspects of the brain are accessed through the Left subclavian and jugular veins into the sinus and venous systems of the brain.

Novel enhanced endovascular tracing and pacing methods and systems using endovascular electrodes are described. Medial Temporal Lobe, SubCortical and Cortical sites within the posterior aspects of the brain are accessed through the Left subclavian and jugular veins into the sinus and venous systems of the brain.

In some embodiments, there is provided an apparatus including a common bus and a plurality of oscillatrode circuits coupled to the common bus, the plurality of oscillatrode circuits including a first oscillatrode circuit outputting a first frequency tone when a first input voltage is detected by the first oscillatrode circuit and a second oscillatrode circuit outputting a second frequency tone when a second input voltage is detected by the second oscillatrode circuit, wherein common bus carries the first frequency tone and the second frequency tone at different frequencies in a frequency division multiplex signal. Related systems, methods, and articles of manufacture are also disclosed.

A device determines values for one or more metrics that indicate the quality of a patient's sleep based on sensed physiological parameter values. Sleep efficiency, sleep latency, and time spent in deeper sleep states are example sleep quality metrics for which values may be determined. The sleep quality metric values may be used, for example, to evaluate the effectiveness of a therapy delivered to the patient by a medical device. In some embodiments, determined sleep quality metric values are automatically associated with the therapy parameter sets according to which the medical device delivered the therapy when the physiological parameter values were sensed, and used to evaluate the effectiveness of the various therapy parameter sets. The medical device may deliver the therapy to treat a non-respiratory neurological disorder, such as epilepsy, a movement disorder, or a psychological disorder. The therapy may be, for example, deep brain stimulation (DBS) therapy.

An initial set of parameters for operating one or more detection tools is automatically derived and subsequently adjusted so that each detection tool is more or less sensitive to signal characteristics in a region of interest. Detection tool(s) may be applied to physiological signals sensed from a patient (such as EEG signals) and may be configured to run in an implanted medical device that is programmable with the parameters to look for rhythmic activity, spike activity, and power changes in the sensed signals, etc. A detection tool may be selected, and parameter values derived in a logical sequence and/or in pairs based on a graphical representation of an activity type which may be selected by a user, for example, by clicking and dragging on the graphic via a GUI. Displayed simulations allow a user to assess what will be detected with a derived parameter set and then to adjust the sensitivity of the set or start over as desired.

A computing device for use in a system for mapping brain activity of a subject includes a processor. The processor is programmed to select a plurality of measurements of brain activity that is representative of at least one parameter of a brain of the subject during a resting state. Moreover, the processor is programmed to compare at least one data point from each of the measurements with a corresponding data point from a previously acquired data set from at least one other subject. The processor is also programmed to produce at least one map for each of the measurements based on the comparison of the resting state data point and the corresponding previously acquired data point. The processor may also be programmed to categorize the brain activity in a plurality of networks in the brain based on the map.