Abstract: A method for identifying changes in an epilepsy patient's disease state, comprising: receiving at least one body data stream; determining at least one body index from the at least one body data stream; detecting a plurality of seizure events from the at least one body index; determining at least one seizure metric value for each seizure event; performing a first classification analysis of the plurality of seizure events from the at least one seizure metric value; detecting at least one additional seizure event from the at least one determined index; determining at least one seizure metric value for each additional seizure event, performing a second classification analysis of the plurality of seizure events and the at least one additional seizure event based upon the at least one seizure metric value; comparing the results of the first classification analysis and the second classification analysis; and performing a further action.

Abstract: Disclosed are methods and medical device systems for automated delivery of therapies for pain and determination of need for and safety of treatment. In one embodiment, such a medical device system may comprise a sensor configured to sense at least one body signal from a patient; and a medical device configured to receive a first sensed body signal from the sensor determine a patient pain index based at least in part on said first sensed body signal; determine whether said patient pain index is above at least a first pain index threshold; determine a safety index based at least in part on a second sensed body signal; select a pain treatment regimen based on at least one of said safety index and or a determination that said pain index is above said first pain index threshold; and deliver said pain treatment regimen.

Abstract: A method for determining and responding in real-time to an increased risk of death relating to a patient with epilepsy is provided. The method includes receiving cardiac data and determining a cardiac index based upon the cardiac data. The method includes determining an increased risk of death associated with epilepsy if the indices are extreme, issuing a warning of the increased risk of death and logging information related to the increased risk of death. Also presented is a second method for determining and responding in real-time to an increased risk of death relating to a patient with epilepsy comprising receiving at least one of arousal data, responsiveness data or awareness data and determining an arousal index, a responsiveness index or an awareness index, where the indices are based on arousal data, responsiveness data or awareness data respectively.

Abstract: Methods, including a method, comprising selecting a plurality of dependent variables relating to each of a plurality of seizures in a patient; selecting a plurality of independent variables, wherein each independent variable comprises a therapy parameter, a therapy delivery parameter, a temporal factor, an environmental factor, or a patient factor; quantifying at least one relationship between each of at least two dependent variables and each of at least two independent variables; and performing an action in response to said quantifying, selected from reporting said at least one relationship, assessing an efficacy of a therapy, assessing an adverse effect of said therapy, providing a therapy modification recommendation, or adjusting said therapy. A medical device system capable of implementing a method. A non-transitory computer readable program storage medium containing instructions that, when executed by a computer, perform a method.

Abstract: A method, comprising receiving a time series of patient body signal, determining first and second sliding time windows for the time series; applying an autoregression algorithm, comprising: applying an autoregression analysis to each of the first and second windows, yielding autoregression coefficients and a residual variance for each window; estimating a parameter vector for each window based on the autoregression coefficients and residual variances; and determining a difference between the parameter vectors; and determining seizure onset and seizure termination based on the difference between the parameter vectors. A non-transitory computer readable program storage unit encoded with instructions that, when executed by a computer, perform the method.

Abstract: We disclose methods and medical device systems for selectively recruiting a nerve fiber type within a cranial nerve, a peripheral nerve or a spinal root. Such a method may comprise applying a first pressure, a heating, and/or a cooling to a second location of the nerve, the pressure, heating, or cooling sufficient to substantially block at least one of activation or conduction in at least one fiber population through the second location of the nerve for a blocking time period; and applying an electrical signal to a first location during the blocking time period.

Abstract: Disclosed are methods and medical device systems for automated delivery of therapies for pain and determination of need for and safety of treatment. In one embodiment, such a medical device system may comprise a sensor configured to sense at least one body signal from a patient; and a medical device configured to receive a first sensed body signal from the sensor; determine a patient pain index based at least in part on said first sensed body signal; determine whether said patient pain index is above at least a first pain index threshold; determine a safety index based at least in part on a second sensed body signal; select a pain treatment regimen based on at least one of said safety index and or a determination that said pain index is above said first pain index threshold; and deliver said pain treatment regimen.

Abstract: Disclosed are methods and medical device systems for automated delivery of therapies for pain and determination of need for and safety of treatment. In one embodiment, such a medical device system may comprise a sensor configured to sense at least one body signal from a patient; and a medical device configured to receive a first sensed body signal from the sensor; determine a patient pain index based at least in part on said first sensed body signal; determine whether said patient pain index is above at least a first pain index threshold; determine a safety index based at least in part on a second sensed body signal; select a pain treatment regimen based on at least one of said safety index and or a determination that said pain index is above said first pain index threshold; and deliver said pain treatment regimen.

Abstract: Disclosed are methods and medical device systems for automated delivery of therapies for pain and determination of need for and safety of treatment. In one embodiment, such a medical device system may comprise a sensor configured to sense at least one body signal from a patient; and a medical device configured to receive a first sensed body signal from the sensor; determine a patient pain index based at least in part on said first sensed body signal; determine whether said patient pain index is above at least a first pain index threshold; determine a safety index based at least in part on a second sensed body signal; select a pain treatment regimen based on at least one of said safety index and or a determination that said pain index is above said first pain index threshold; and deliver said pain treatment regimen.

Abstract: Methods, systems, and apparatus for detecting and/or validating a detection of a state change by matching the shape of one or more of an cardiac data series, a heart rate variability data series, or at least a portion of a heart beat complex, derived from cardiac data, to an appropriate template.

Abstract: A method for determining and responding in real-time to an increased risk of death relating to a patient with epilepsy is provided. The method includes receiving cardiac data and determining a cardiac index based upon the cardiac data. The method includes determining an increased risk of death associated with epilepsy if the indices are extreme, issuing a warning of the increased risk of death and logging information related to the increased risk of death. Also presented is a second method for determining and responding in real-time to an increased risk of death relating to a patient with epilepsy comprising receiving at least one of arousal data, responsiveness data or awareness data and determining an arousal index, a responsiveness index or an awareness index, where the indices are based on arousal data, responsiveness data or awareness data respectively.

Abstract: A method, comprising receiving a time series of patient body signal, determining first and second sliding time windows for the time series; applying an autoregression algorithm, comprising: applying an autoregression analysis to each of the first and second windows, yielding autoregression coefficients and a residual variance for each window; estimating a parameter vector for each window based on the autoregression coefficients and residual variances; and determining a difference between the parameter vectors; and determining seizure onset and seizure termination based on the difference between the parameter vectors. A non-transitory computer readable program storage unit encoded with instructions that, when executed by a computer, perform the method.

Abstract: Methods, systems, and apparatus for assessing a state of an epilepsy disease or a comorbidity thereof are provided. The methods comprise receiving at least one autonomic index, neurologic index, stress marker index, psychiatric index, endocrine index, adverse effect of therapy index, physical fitness index, or quality of life index of a patient; comparing the at least one index to at least one reference value; and assessing a state of an epilepsy disease or a body system of the patient based on the comparison. A computer readable program storage device encoded with instructions that, when executed by a computer, perform the method described above is also provided. A medical device system capable of implementing the method described above is also provided.

Abstract: Methods, systems, and apparatus for assessing a state of an epilepsy disease or a comorbidity thereof are provided. The methods comprise receiving at least one autonomic index, neurologic index, stress marker index, psychiatric index, endocrine index, adverse effect of therapy index, physical fitness index, or quality of life index of a patient; comparing the at least one index to at least one reference value; and assessing a state of an epilepsy disease or a body system of the patient based on the comparison. A computer readable program storage device encoded with instructions that, when executed by a computer, perform the method described above is also provided. A medical device system capable of implementing the method described above is also provided.

Abstract: A method, apparatus, and system are provided for mucous membrane therapy. The method includes receiving at least one body signal from a patient; detecting a condition of the patient based on the body signal; and administering the therapy to at least one of a mucous membrane or a serous membrane of the patient. A medical device system configured to implement the method is provided. A computer-readable storage device for storing instructions that, when executed by a processor, perform the method is also provided.

Abstract: Methods, systems, and apparatus for detecting and/or validating a detection of a state change by matching the shape of one or more of an cardiac data series, a heart rate variability data series, or at least a portion of a heart beat complex, derived from cardiac data, to an appropriate template.

Abstract: We report a method for detecting epileptic or non-epileptic seizures, comprising: receiving a heart rate of a patient; calculating a percentage change of the heart rate in reference to at least one of the patient's maximal heart rate, reserve heart rate, or a resting heart rate; detecting an epileptic seizure if the percentage increase in the heart rate reaches a threshold value; and taking at least one responsive action selected from issuing a warning, delivering a therapy, determining a seizure severity, or logging at least one of a date and time of occurrence of the seizure, its severity, the type of therapy, or its effect, in response to the detection. We also report a medical device system configured to implement the method. We also report a non-transitory computer readable program storage unit encoded with instructions that, when executed by a computer, perform the method.

Abstract: A method, comprising detecting, in at least a first brain region of a patient, an electrical activity relating to an epileptic activity; determining a first regularity index of said electrical activity; and applying at least one first electrical stimulation to at least one neural target of said patient for treating said epileptic event, in response to said first regularity index being within a first range. A non-transitive, computer-readable storage device for storing instructions that, when executed by a processor, perform the method. A medical device system suitable for use in the method.

Abstract: We report a method for detecting epileptic or non-epileptic seizures, comprising: receiving a heart rate of a patient; calculating a percentage change of the heart rate in reference to at least one of the patient's maximal heart rate, reserve heart rate, or a resting heart rate; detecting an epileptic seizure if the percentage increase in the heart rate reaches a threshold value; and taking at least one responsive action selected from issuing a warning, delivering a therapy, determining a seizure severity, or logging at least one of a date and time of occurrence of the seizure, its severity, the type of therapy, or its effect, in response to the detection. We also report a medical device system configured to implement the method. We also report a non-transitory computer readable program storage unit encoded with instructions that, when executed by a computer, perform the method.

Abstract: Methods, systems, and apparatuses are provided for detecting fall events of a person. Fall events are falls that are likely to occur, are occurring, or have occurred. Fall detectors and fall detector systems detect fall events of the person. Data relating to the person are received from sensors and analyzed to perform fall detection. Data relating to the person includes accelerations and forces experience by the person, changes in body position of the person, movements of the person, and body signals and sounds of the person. Neurological tests are administered to determine levels of responsiveness and awareness of the person in response to detections. Warnings are issued, and safety measures are deployed, in response to detections. Data relating to fall events are recorded and logged. Fall event histories based upon the logged data and fall detection algorithm performance are used to improve future fall detection and prediction.