DocCode — SCORE
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`SCORE Placeholder Sheet for IFW Content
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`Application Number: 61794540
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`Document Date: 03/15/2013
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`received in electronic format on the date identified above. This content is stored in the SCORE
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`- Drawing
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`
`Form Revision Date: February 8, 2006
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`1
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`1
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`APPLE 1010
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`APPLE 1010
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`1
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`DYNAMIC ADJUSTMENT OF NON-PATHOLOGICAL RANGE OF BODY DATA
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`VARIABILITY (BDV) THRESHOLD FOR SEIZURE DETECTION
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`[001] This disclosure relates to medical device systems and methods capable of detecting
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`FIELD OF THE INVENTION
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`epileptic seizures.
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`SUMMARY OF THE INVENTION
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`[002]
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`In some embodiments,
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`the present disclosure relates to a method of detecting an
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`occurrence of a pathological state of a patient from body data of the patient, comprising:
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`receiving a body signal of the patient; determining a current body data variability (BDV) value
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`based upon the body signal; determining an activity level of the patient; determining a non-
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`pathological BDV range based at least in part on the activity level; comparing the current BDV
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`value to the non-pathological BDV range; detecting the occurrence of a pathological state in
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`response to the current BDV value being outside the non-pathological BDV range.
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`[003]
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`In some embodiments,
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`the present disclosure relates to a method of detecting an
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`occurrence of a pathological state of a patient from cardiac data of the patient, comprising:
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`receiving a cardiac signal of the patient; determining an current heart rate variability (HRV)
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`value based on the cardiac signal; determining an activity level of the patient; determining a non-
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`pathological HRV range based at least in part on the activity level; comparing the current HRV
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`value to the non-pathological HRV range; and detecting an occurrence of an epileptic seizure in
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`response to the current HRV value being outside the non-pathological HRV range.
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`[004]
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`In some embodiments,
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`the present disclosure relates to a method of detecting an
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`occurrence of a pathological state of a patient from body data of the patient, comprising:
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`receiving a body signal of the patient; determining a current body data variability (BDV) based
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`upon the body signal; comparing the current BDV to a non-pathological BDV range; and
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`detecting the occurrence of a pathological state in response to the current BDV being outside the
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`non-pathological BDV range.
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`In one embodiment, the body data comprises one or more of
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`autonomic data, neurologic data, metabolic data, endocrine data, or tissues stress factor data.
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`[005]
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`In other embodiments,
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`the present disclosure relates to a medical device system,
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`comprising at least one sensor configured to collect a body signal from a patient; and a medical
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`device, comprising: a body signal data module configured to determine a time series of body
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`signal values based upon the body signal; a body data variability module configured to determine
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`at least a current body data variability (BDV) value based at least upon the time series of body
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`signal values; a non-pathological body data range module configured to determine a non-
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`pathological body data variability (BDV) range, based at least in part on the current body signal
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`value; and a pathological state detection module configured to detect an occurrence of a
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`pathological state, in response to the current BDV value being outside the non-pathological BDV
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`range.
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`[006]
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`In some embodiments,
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`the present disclosure relates to a non-transitory computer
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`readable program storage unit encoded with instructions that, when executed by a computer,
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`perform a method as described above.
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`BRIEF DESCRIPTION OF THE DRAWINGS
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`[007] The disclosure may be understood by reference to the following description taken in
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`conjunction with the accompanying drawings, in which like reference numerals identify like
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`elements, and in which:
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`[008] Figure 1 shows a schematic diagram of a medical device system, in accordance with
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`some embodiments of the present disclosure;
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`[009] Figure 2 shows a schematic diagram of portions of a medical device system,
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`in
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`accordance with some embodiments of the present disclosure;
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`Figure 3 shows a schematic diagram of a body data variability (BDV) module, according to some
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`embodiments of the present disclosure;
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`[0010] Figure 4 shows a dependence of a non-pathological BDV range (in this figure, heart rate
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`variability (HRV)) on the value of the body data (in this figure, heart rate), according to some
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`embodiments of the present disclosure;
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`[0011] Figure 5 shows a dependence of a non-pathological BDV range (in this figure, respiratory
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`rate variability (RRV)) on the value of the body data (in this figure, respiratory rate), according
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`to some embodiments of the present disclosure;
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`[0012] Figure 6 shows a work level lookup chart, indicating a work level range expected at
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`various times of day, according to some embodiments of the present disclosure;
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`[0013] Figure 7 shows a flowchart depiction of a method, according to some embodiments of the
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`present disclosure;
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`[0014] Figure 8 shows a flowchart depiction of a method, according to some embodiments of the
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`present disclosure; and
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`[0015] Figure 9 shows a flowchart depiction of a method, according to some embodiments of the
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`present disclosure.
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`[0016] While the disclosure is susceptible to various modifications and alternative forms,
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`specific embodiments thereof have been shown by way of example in the drawings and are
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`herein described in detail.
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`It should be understood, however, that the description herein of
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`specific embodiments is not intended to limit the disclosure to the particular forms disclosed, but
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`on the contrary, the intention is to cover all modifications, equivalents, and alternatives falling
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`within the spirit and scope of the disclosure as defined by the appended claims.
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`DETAILED DESCRIPTION OF SPECIFIC EMBODIMENTS
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`[0017] Illustrative embodiments of the disclosure are described herein. For clarity, not all
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`features of an actual
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`implementation are described.
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`In the development of any actual
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`embodiment, numerous implementation-specific decisions must be made to achieve design-
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`specific goals, which will vary from one implementation to another. Such a development effort,
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`while possibly complex and time-consuming, would nevertheless be a routine undertaking for
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`persons of ordinary skill in the art having the benefit of this disclosure.
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`[0018] Embodiments disclosed herein provide for determining a body data variability (e. g, heart
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`rate variability, respiratory rate variability, 02 saturation variability, blood pressure variability,
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`etc.), as well as an activity level of a patient in order to determine a non-pathological range for
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`the body data variability (BDV). A current BDV may then be compared to the non-pathological
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`BDV range to perform a pathological state of a patient.
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`[0019] Figure 1 shows a schematic representation of a medical device system, according to some
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`embodiments of the present disclosure. The medical device system 100 may comprise a medical
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`device 200, body data sensor(s) 112, and lead(s) 111 coupling the sensor(s) 112 to the medical
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`device 200. In one embodiment, body data sensor(s) 112 may each be configured to collect data
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`from a patient relating to a time series of body data values. The body data may be selected from
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`heart
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`rate, heart
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`rate pattern, blood pressure,
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`respiratory rate,
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`respiratory pattern, EKG
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`morphology, dermal activity, pupillary activity, oxygen saturation, or kinetic activity, among
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`others.
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`[0020] Various components of the medical device 200, such as controller 110, processor 115,
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`memory 117, power supply 130, communication unit 140, warning unit 192, therapy unit 194,
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`logging unit 196, and severity unit 198 have been described in other patent applications assigned
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`to Flint Hills Scientific, LLC or Cyberonics, Inc., such as, USSN 12/896,525, filed October 1,
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`2010; USSN 13/288,886, filed November 3, 2011; USSN 13/449,166, filed April 17, 2012; and
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`USSN 13/678,339, filed November 15, 2012. Each of the patent applications identified in this
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`paragraph is hereby incorporated herein by reference.
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`[0021] The medical device 200 may comprise a body data module 150 configured to obtain a
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`time series of body data from the collected data. The body data module 150 may also be
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`configured to determine one or a time series of body data values or body index values based
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`upon the time series. Such a time series of body index values may comprise at least one of an
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`instantaneous heart rate (HR), an instantaneous respiratory rate (R), an instantaneous blood
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`pressure (BP), or an instantaneous blood oxygen saturation (02S) value, among others.
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`[0022] The medical device 200 may comprise a body data variability module 165 configured to
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`determine at least a body data variability (BDV) of a body signal. The body data variability
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`module 165 may be configured to determine at least one body data variability selected from a
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`heart rate variability (HRV), a respiratory rate variability (RRV), a blood pressure (BPV), a
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`respiratory pattern variability, an EKG (electrocardiogram) morphology variability, a heart rate
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`pattern variability, an electrodermal variability, a pupillary variability (hippus), a blood oxygen
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`saturation variability, or a kinetic (rate, amplitude, direction or force of movement) rate
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`variability.
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`[0023] The medical device 200 may comprise a non-pathological body data range module 160
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`configured to determine a non-pathological body data variability (BDV) range, based at least in
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`part on a body data value.
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`In some embodiments, the non-pathological body data variability
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`(BDV) range may comprise at least one of a non-pathological HRV range, a non-pathological
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`RRV range, a non-pathological BPV range, a non-pathological respiratory pattern variability
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`range, a non-pathological EKG morphology variability range, a non-pathological heart rate
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`pattern variability range, a non-pathological electrodermal variability range, a non-pathological
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`pupillary variability range, a non-pathological blood oxygen saturation variability range, or a
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`non-pathological kinetic rate variability range.
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`[0024] The medical device 200 may comprise a pathological state detection module 170
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`configured to detect an occurrence of a pathological state, based at least in part on the BDV
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`being outside the non-pathological BDV range.
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`In some embodiments, the pathological state
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`may be an epileptic event.
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`[0025] The medical device 200 may comprise at least one activity level sensor(s) 114, each
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`configured to collect at least one body signal from a patient relating to an activity level of the
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`patient. For example, each activity level sensor(s) 114 may be selected from an accelerometer,
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`an inclinometer, an electromyography (EMG) sensor, a muscle temperature sensor, an oxygen
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`consumption sensor, a lactic acid concentration sensor, a sweat sensor, or a neurogram sensor.
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`[0026] The medical device 200 may comprise an activity level module 180, configured to
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`determine an activity level from a body signal collected by activity level sensor(s) 114. By
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`“activity level” is meant the level of one or more of the patient’s energy consumption (which
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`may be termed “work level” and may conveniently be measured by proxies such as body
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`movement, EMG activity, 02 consumption or heart rate, among others, and from which the
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`classical definition of work is not excluded), emotional activity (e.g., mild versus intense
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`emotion), or cognitive activity (e.g., mild versus intense thought).
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`Information relating to work
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`level may be collected by an accelerometer, etc., described above.
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`In some embodiments, the
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`non-pathological HRV range module is configured to determine the non-pathological HRV
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`range, based at least in part on the determined work level.
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`[0027] The activity level module 180 may determine an activity level of the patient at any
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`sampling frequency.
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`In one embodiment,
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`the activity level module 180 is configured to
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`determine the activity level with a sampling frequency from about one hundred times per second
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`to about once every four hours. The activity level module 180 may determine an activity level
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`for at least one time window or may determine an instantaneous measure of activity. The at least
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`one time window may be on a micro- (less than 10 min), a meso- (lO min-24 hr), or a macroscale
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`(greater than 24 hr).
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`[0028] In some embodiments, activity level module 180 may be configured to determine an
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`activity level by measuring the patient’s energy consumption at a given time ; determine whether
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`the activity level is below a predetermined activity level threshold, wherein the activity level
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`threshold is a function of one of an age, gender, fitness level, time of day or an activity level of
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`the patient; and indicate a confirmation of the pathological state in response to a determination
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`that that the workload is not below the predetermined work level threshold. Activity level may
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`be based upon one or more factors such as kinetic (body movement), emotional, and/or cognitive
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`factors. Work level may be also determined using kinetic signals such as amplitude, force and
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`rate of movement or autonomic signals such as heart rate, among others.
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`[0029] Figure 2 shows data acquisition elements of the medical device system 100 in more
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`detail. Figure 2 depicts an exemplary implementation of the body data module 150 described
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`above with respect to Figure l. The body data module 150 may include a body data memory 250
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`for storing and/or buffering data in the body data module 150. The body data memory 250 may,
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`in some embodiments, be adapted to store body data for logging or reporting purposes and/or for
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`future body data processing. The body data module 150 may also include one or more body data
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`interfaces 210. The body data interface 210 may provide an interface for input/output (I/O)
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`communications between the body data module 150 and body data units/modules (e.g., [260-
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`270], [273-275]) via connection 280. Connection 280 may a wired or wireless connection, or a
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`combination of the two. The connection 280 may be a bus-like implementation or may include
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`an individual connection (not shown) for each, or some number, of the body data unit (e. g., [260-
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`270], [273-275]). The connection 280 may also include connection elements as would be known
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`to one of skill in the art having the benefit of this disclosure. The specific implementation of the
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`connection 280 does not serve to limit other aspects of various embodiments described herein
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`unless specifically described.
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`[0030] In various embodiments, the body data units may include, but are not limited to, an
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`autonomic data acquisition unit 260, a neurologic data acquisition unit 270, an endocrine data
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`acquisition unit 273, a metabolic data acquisition unit 274, and/or a tissue stress marker data
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`acquisition unit 275.
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`[0031] In one embodiment, the autonomic data acquisition unit 260 may include a heart beat
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`data acquisition unit 261 adapted to acquire heart sounds, EKG data, PKG data, heart echo,
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`apexcardiography and/or the like, a blood pressure acquisition unit 263, a respiration acquisition
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`unit 264, a blood gases acquisition unit 265, a dermal acquisition unit 266, and/or the like.
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`[0032] In one embodiment, the neurologic data acquisition unit 270 may contain a kinetic unit
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`that may comprise an accelerometer, an inclinometer, and/or the like;
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`the neurologic data
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`acquisition unit 270 may also contain a responsiveness/awareness unit that may be used to
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`determine a patient’s responsiveness to testing/stimuli and/or a patient’s awareness of their
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`surroundings. These lists are not inclusive, and the body data module 150 may collect additional
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`data not listed herein, that would become apparent to one of skill in the art having the benefit of
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`this disclosure.
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`[0033] The body data units ([260-270], [273-275]) may be adapted to collect, acquire, receive
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`and/or
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`transmit heart beat data, EKG data, PKG (phonocardiogram) data, heart echo,
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`apexcardiography, heart sound data, blood pressure data, respiration data, blood gases data, body
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`acceleration data, body incline data and/or the like.
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`[0034] The body data interface(s) 210 may include various amplifier(s) 220, one or more A/D
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`converters 230 and/or one or more buffers 240 or other memory (not shown).
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`In one
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`embodiment, the amplifier(s) 220 may be adapted to boost incoming and/or outgoing signal
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`strengths for signals such as those to/from any body data units/modules (e.g., ([260-270], [273-
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`275]) or signals to/from other units/modules of the medical device 200. The A/D converter(s)
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`230 may be adapted to convert analog input signals from body data unit(s)/module(s) (e.g.,
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`([260-270], [273-275]) into a digital signal format for processing by controller 210 (and/or
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`processor 215). Such analog signals may include, but is not limited to, heart beat data, EKG
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`data, PKG data, heart echo, apexcardiography, heart sound data, blood pressure data, respiration
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`data, blood gases data, body acceleration data, body incline data and/or the like. A converted
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`signal may also be stored in a buffer(s) 240, a body data memory 250, or some other memory
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`internal to the medical device 200 (e.g., memory 217) or external to the medical device 200. The
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`buffer(s) 240 may be adapted to buffer and/or store signals received by the body data module
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`150 as well as signals to be transmitted by the body data module 150.
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`In various embodiments,
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`the buffer(s) 240 may also be adapted to buffer and/or store signals in the body data module 150
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`as these signals are transmitted between components of the body data module 150.
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`[0035] Figure 3 shows a body data variability module 165. The body data variability module
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`165 may be configured to determine a body data variability of one or more body data types. For
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`example, the body data variability module 165 may comprise an HRV module 310 configured to
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`determine HRV from heart rate data. For another example, the body data variability module 165
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`may comprise a respiratory rate variability (RRV) module 320 configured to determine RRV
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`from respiratory rate data. For another example, the body data variability module 165 may
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`comprise an 02 saturation variability (OZSV) module 330 configured to determine OZSV from
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`02 saturation data. For another example, the body data variability module 165 may comprise a
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`blood pressure variability (BPV) module 350 configured to determine BPV from blood pressure
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`data. Alternatively or in addition, the body data variability module 165 may comprise other
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`modules configured to determine a variability of a particular body data type from that body data.
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`[0036] The body data variability module 165 may also comprise a body data variability
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`calculation unit 340 configured to perform one or more calculations on one or more outputs of
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`variability modules 310-330 and/or 350.
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`[0037] The body data variability module 165 may output one or more BDV values, such as an
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`HRV value, an RV value, a BPV value, or an OZSV value, among others.
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`[0038] Figure 4 shows a dependence of a non-pathological BDV range (in this figure, heart rate
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`variability (HRV)) on the value of the body data (in this figure, heart rate). Generally, as heart
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`rate increases, moving left to right from zone a to zone b to zone c in the figure, the non-
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`pathological HRV range decreases. This indicates that under non-pathological conditions, as a
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`patient’s HR increases (e. g, as a result of exercise), the heart rate variability decreases. Various
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`measures of HRV have been established, and include l)statistical HRV measures such as
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`standard deviation of NN intervals (SDNN), the square root of the mean squared differences of
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`successive NN intervals (RMSSD), the percentage of successive NN intervals for a selected time
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`period greater than 50 ms (pNNSO; 2) spectral analysis measures such as the ratio of high
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`frequency to low frequency components (HF/LF); 3) nonlinear methods such as Lyopunov
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`exponents, approximate entropy, and other methods. Details on methods of determining HRV
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`are available in Malik et al., “Heart Rate Variability: Standards of Measurement, Physiological
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`Interpretation, and Clinical Use,” Task Force of the European Society of Cardiology and the
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`North American Society of Pacing Electrophysiology (1996). Regardless of the particular HRV
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`measure used, as shown in Figure 4, an excursion of HRV outside the non-pathological HRV
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`range may be taken as an indication of an occurrence of a pathological state, e.g., an epileptic
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`seizure. This figure is for illustrative purposes, and is not necessarily reflective of body
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`physiology.
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`[0039] Figure 5 shows an example of the dependence of a non-pathological BDV range (in this
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`figure, respiratory rate variability (RRV)) on the value of the body data (in this figure,
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`respiratory rate).
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`In the example of Figure 5, RRV forms a U-shaped curve, with highest values
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`at low or high respiratory rates, and low values at medium respiratory rates. An excursion of
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`RV outside the non-pathological RRV range may be taken as an indication of an occurrence of
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`a pathological state, e.g., an epileptic seizure. This figure is for illustrative purposes, and is not
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`necessarily reflective of body physiology.
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`In particular, other shapes may describe non-
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`pathological RRV ranges for patients under particular conditions.
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`[0040] Although Figures 4-5 are directed to heart rate and HRV or respiratory rate and RV,
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`other body data, such as blood pressure, oxygen saturation, or dermal activity, among others,
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`would also be expected to have different non-pathological variability ranges at different values
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`of the body data.
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`[0041] Figure 6 shows an example of an activity level lookup chart, indicating activity levels at
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`various times of day for a patient. For example, the expected work level while the patient is
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`asleep (in this example, from about 12 am to about 6 am) is low. The activity level rises during
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`typical morning activities of preparing for work, and spikes at around 6 pm (in this example, the
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`patient engages in aerobic exercise after work). At the low work level, HR may be relatively low
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`(e.g., about 70 bpm) and HRV may be relatively high. At the high work level, HR may be
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`relatively high (e.g., about 145 bpm) and HRV may be relatively low.
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`[0042] Figure 7 shows a flowchart representation of a method 700 of detecting an occurrence of
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`a pathological state, e.g., an epileptic seizure, of a patient from body data of the patient. The
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`method 700 may comprise receiving or acquiring at 710 the body data of the patient. For
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`example, the received or acquired body data may be at least one of an autonomic signal, a
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`neurologic signal, a metabolic signal, an endocrine signal or a tissue stress signal.
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`In some
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`embodiments, the autonomic signal may comprise cardiac data, respiratory data, blood pressure
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`data, or oxygen saturation data.
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`[0043] The method 700 may comprise determining at 720 a value of the body data.
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`For
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`example, from cardiac data, heart rate may be determined at 720. For another example, from
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`respiratory data, respiratory rate may be determined at 720. In one embodiment, a time series of
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`body data values (e.g., a time series of heart rate) may be determined.
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`[0044] The method 700 may comprise determining at 730 a body data variability (BDV) based at
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`least in part upon the body data values at 720. The BDV value may be determined based on a
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`given number of body data value points, a time window of given length of body data value
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`points, etc.
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`[0045] In some embodiments, the method 700 may further comprise determining an activity
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`level of the patient (not shown).
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`In some embodiments, determining the activity level may
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`comprise determining a kinetic index from an output of at least one of an accelerometer, an
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`inclinometer, an electromyogram (EMG), a muscle temperature sensor, an oxygen consumption
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`sensor, a lactic acid concentration sensor, a sweat sensor, or a neurogram sensor, in at least a first
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`time window. Determining the kinetic index may comprise determining an activity level in at
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`least one time window from an output of one of an accelerometer, an inclinometer, an
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`electromyogram (EMG), or a neurogram sensor.
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`[0046] The method 700 may comprise determining at 740 a non-pathological BDV range, based
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`at least in part on the body data values.
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`In some embodiments, the determining at 740 may be
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`based at least in part on an activity level. The non-pathological BDV range may be determined
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`once, or repeatedly determined, or continuously determined. The range may be determined on a
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`fixed or variable automatic schedule, in response to changes in the body data values and/or
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`activity levels, in response to a manual signal, or two or more thereof. Exemplary considerations
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`for determining the non-pathological BDV range may be at least one of an historic BDV of the
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`patient, a trend relating to the body data value, work being performed by the patient, or the
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`perception of a work level as experienced by the patient.
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`In one embodiment, the activity level
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`of the patient is objectively determined (e.g., rate of movement on a 15° incline on a treadmill for
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`a certain time, using, for example, an accelerometer signal). However, the work level a patient
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`perceives may or may not match the objective indicators of activity level. For example, a 2 mile
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`jog (same path) may seem one day easy and the next day grueling. This may be due to
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`psychological, environmental and other factors (lack of sleep, illness, etc.). Also, activity level
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`may take into account not only the actual load but the length of time the patient performs it. The
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`same load after 10 min may be perceived differently from that at 5 min. How the patient
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`experiences the work level may also impact the non-pathological BDV range determination at
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`740.
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`[0047] In some embodiments, determining at 740 the non-pathological BDV range may be based
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`at least in part on least one of the patient’s age, gender, health status, fitness level, work level,
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`environmental conditions, or current body data in a first time period.
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`[0048] One embodiment of the non-pathological BDV range determination performed at 740 is
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`shown in Figure 8. The determining performed at 740 may comprise performing at 810 a lookup
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`of historical BDV for the patient (which may be based upon, e.g., time of day, activity level,
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`physiological parameters, etc.). A trend of movement of body data (e.g., whether heart rate is
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`increasing) may be determined at 820. The value of the body data (e.g., heart rate) may be
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`determined at 830. The activity level may be determined at 840.
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`In light of one or more values
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`looked up and/or determined at 810-840, the non-pathological BDV range may be determined at
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`850.
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`[0049] Returning to Figure 7, the method 700 may comprise determining at 750 whether the
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`BDV is outside the non-pathological BDV range. If it is not, z'.e., if the BDV value is in the non-
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`pathological range, then flow may return to an earlier element of the method, e.g., receiving or
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`acquiring at 710. If it is, z'.e., if the BDV value indicates a pathological state, then the occurrence
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`of the pathological state may be determined at 760.
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`In another embodiment, an activity level-
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`based confirmation of the pathological state occurrence may be performed at 770 in response to a
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`determination that the BDV is outside of the non-pathological BDV range.
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`[0050] An embodiment of the activity level-based confirmation performed at 770 is shown in
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`Figure 9. The activity level-based confirmation of the pathological event at 770 may comprise
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`determining at 910 the patient’s activity level. If the patient’s activity level is determined at 920
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`not to be commensurate with one or more of the time of day, the patient’s body data, or with one
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`or more body indices appropriate for determining a non-pathological state, the pathological
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`statement be confirmed (930). On the other hand, if the patient’s activity level is commensurate
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`with one or more of the time of day, patient body data or other indices for indicating a non-
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`pathological state, then it may be determined at 940 if a change in the body data value is
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`monotonic (950). If so, then the pathological state may be not confirmed at 960. On the other
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`hand, if the work level is determined at 950 to be not monotonic, then the pathological state may
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`be confirmed at 930.
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`[0051] Returning to Figure 7, upon determining at 760 the occurrence of the pathological state,
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`e.g., an epileptic seizure,
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`the method may filrther comprise performing a further action in
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`response to the detecting, wherein the further action is selected from confirming the detection of
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`the occurrence of the pathological state (e. g, the activity level-based confirmation at 770);
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`issuing a warning; delivering a therapy; determining a severity of the pathological state; or
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`logging to memory one of the date and time of occurrence of the pathological state, a severity of
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`the pathological state, and an effect of a therapy delivered to treat the pathological state.
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`[0052] If the confirmation performed at 770 is determined at 780 to confirm the pathological
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`state, then the occurrence of the pathological state may be determined at 760. If the activity level
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`analysis does not confirm the pathological state, then flow may return to an earlier element of the
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`method, e. g., receiving or acquiring at 710.
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`[0053] In an exemplary embodiment, the body signal is a cardiac signal, and the body data
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`variability is heart rate variability. In some embodiments, detecting an occurrence of an epileptic
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`seizure may comprises detecting an epileptic seizure in response to the current HRV value being
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`greater than an expected heart rate variability based on an activity level in at least one time
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`window.
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`[0054] The methods depicted in Figures 7-9 and/or described above may be governed by
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`instructions that are stored in a non-transitory computer readable storage medium and that are
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`executed by, e.g., a processor 217 of the medical device 200. Each of the operations shown in
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`Figures 7-9 and/or described above may correspond to instructions stored in a non-transitory
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`computer memory or computer readable storage medium.
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`In various embodiments, the non-
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`transitory computer readable storage medium includes a magnetic or optical disk storage device,
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`solid state storage devices such as flash memory, or other non-volatile memory device or
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`devices. The computer readable instructions stored on the non-transitory computer readable
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`storage medium may be in source code, assembly language code, object code, or other
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`instruction format that is interpreted and/or executable by one or more processors.
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`What is claimed:
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`CLAIMS
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`1. A non-transitory computer readable program storage unit encoded with instructions
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`that, when executed by a computer, perform a method of detecting an occurrence of a
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`pathological state of a patient from body data of said patient, comprising:
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`receiving a body signal of said patient;
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`determining a current body data variability (BDV) value based upon said body signal;
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`determining an activity level of said patient;
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`determining a non-pathological BDV range based at least in part on said activity level;
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`comparing said current BDV value to said non-