DETECTING, ASSESSING AND MANAGING EPILEPSY USING A MULTI-VARIATE, METRIC-BASED CLASSIFICATION ANALYSIS

• US 20150196246A1
• Filed: 12/25/2014
• Published: 07/16/2015
• Est. Priority Date: 10/01/2010
• Status: Active Grant

• Current Assignee: Flint Hills Scientific, LLC
• Original Assignee: Flint Hills Scientific, LLC
• Inventors: Osorio, Ivan
• Granted Patent: US 9,204,838 B2
• Time in Patent Office: Days
• US Class Current: 1/1
• CPC Class Codes: A61B 2560/0242 adapted to measure environmental factors, e.g. temperature, pollution
• CPC Class Codes: A61B 2560/0475 Special features of memory means, e.g. removable memory cards
• CPC Class Codes: A61B 5/0205 Simultaneously evaluating both cardiovascular conditions and different types of body conditions, e.g. heart and respiratory condition
• CPC Class Codes: A61B 5/02055 Simultaneously evaluating both cardiovascular condition and temperature
• CPC Class Codes: A61B 5/021 Measuring pressure in heart or blood vessels A61B5/0205 takes precedence
• CPC Class Codes: A61B 5/024 Detecting, measuring or recording pulse rate or heart rate A61B5/0205, A61B5/021 take precedence
• CPC Class Codes: A61B 5/02405 Determining heart rate variability
• CPC Class Codes: A61B 5/026 Measuring blood flow A61B3/1233, A61B3/1241 take precedence
• CPC Class Codes: A61B 5/08 Detecting, measuring or recording devices for evaluating the respiratory organs A61B5/0205 takes precedence
• CPC Class Codes: A61B 5/11 Measuring movement of the entire body or parts thereof, e.g. head or hand tremor, mobility of a limb for measuring pulse A61B5/02 ;A61B5/1038 takes precedence; motion detection to correct for motion artifacts in physiological signals A61B5/721
• CPC Class Codes: A61B 5/1117 Fall detection
• CPC Class Codes: A61B 5/1118 Determining activity level
• CPC Class Codes: A61B 5/14503 invasive, e.g. introduced into the body by a catheter or needle or using implanted sensors A61B5/1459, A61B5/1464, A61B5/1473, A61B5/1482, A61B5/14865 take precedence
• CPC Class Codes: A61B 5/14539 for measuring pH
• CPC Class Codes: A61B 5/14542 for measuring blood gases A61B5/14551 takes precedence
• CPC Class Codes: A61B 5/283 Invasive
• CPC Class Codes: A61B 5/316 Modalities, i.e. specific diagnostic methods
• CPC Class Codes: A61B 5/349 Detecting specific parameters of the electrocardiograph cycle
• CPC Class Codes: A61B 5/369 Electroencephalography [EEG] devices for psychotechnics A61B5/16
• CPC Class Codes: A61B 5/389 Electromyography [EMG]
• CPC Class Codes: A61B 5/4094 : Diagnosing or monitoring seizure diseases, e.g. epilepsy
• CPC Class Codes: A61B 5/4836 : Diagnosis combined with treatment in closed-loop systems or methods A61B5/0036 takes precedence
• CPC Class Codes: A61B 5/4848 : Monitoring or testing the effects of treatment, e.g. of medication
• CPC Class Codes: A61B 5/486 : Bio-feedback A61B5/375 takes precedence
• CPC Class Codes: A61B 5/686 : Permanently implanted devices, e.g. pacemakers, other stimulators, biochips A61B5/6861 takes precedence
• CPC Class Codes: A61B 5/7246 : using correlation, e.g. template matching or determination of similarity
• CPC Class Codes: A61B 5/7264 : Classification of physiological signals or data, e.g. using neural networks, statistical classifiers, expert systems or fuzzy systems neural networks per se G06N3/00; expert systems per se G06N5/00
• CPC Class Codes: A61B 5/7267 : involving training the classification device
• CPC Class Codes: A61B 5/7275 : Determining trends in physiological measurement data; Predicting development of a medical condition based on physiological measurements, e.g. determining a risk factor
• CPC Class Codes: A61B 5/7282 : Event detection, e.g. detecting unique waveforms indicative of a medical condition A61B5/4094, A61B5/4818, A61B5/349, A61B5/0823 take precedence
• CPC Class Codes: A61B 5/742 : using visual displays A61B5/339 takes precedence
• CPC Class Codes: A61B 5/743 : Displaying an image simultaneously with additional graphical information, e.g. symbols, charts, function plots
• CPC Class Codes: A61B 5/746 : Alarms related to a physiological condition, e.g. details of setting alarm thresholds or avoiding false alarms alarms per se G08B
• CPC Class Codes: A61M 16/1005 : with O2 features or with parameter measurement
• CPC Class Codes: A61M 2202/0208 : Oxygen
• CPC Class Codes: A61M 2205/3303 : Using a biosensor
• CPC Class Codes: A61N 1/36064 : Epilepsy
• CPC Class Codes: A61N 1/36135 : using physiological parameters
• CPC Class Codes: G16H 20/10 : relating to drugs or medications, e.g. for ensuring correct administration to patients
• CPC Class Codes: G16H 20/40 : relating to mechanical, radiation or invasive therapies, e.g. surgery, laser therapy, dialysis or acupuncture
• CPC Class Codes: G16H 50/20 : for computer-aided diagnosis, e.g. based on medical expert systems
• CPC Class Codes: G16H 50/30 : for calculating health indices; for individual health risk assessment

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.

Claims

1-20. -20. (canceled)

21. A medical device system, comprising:

a seizure determination unit configured to detect a plurality of seizure events based upon body data of the patient;

a blood gases acquisition unit configured to acquire oxygen saturation data of a patient;

a seizure metric determination unit configured to determine, for each seizure event, at least one seizure metric value characterizing the seizure event, wherein each of said at least one seizure metric values is based at least in part on an oxygen saturation index;

a classification analysis unit configured to perform a first classification of a first portion of the plurality of seizure events, the first classification analysis comprising assigning each seizure event in the first portion to at least one seizure class based upon the proximity of the seizure metric values to each other; and

further configured to perform a second classification analysis of a second portion of the plurality of seizure events, the second classification analysis comprising assigning each seizure event in the second portion to at least one seizure class based upon the proximity of the seizure metric values, wherein said second portion comprises at least one seizure event not present in the first portion;

a classification analysis comparator unit configured to compare the results of the first classification analysis and the second classification analysis;

and

a responsive action unit configured to perform at least one of;

a. reporting a change from the first classification to the second classification;

b. reporting the absence of a change from the first classification to the second classification;

c. displaying a result of at least one of the first classification analysis, the second classification analysis, and the comparing;

d. identifying the emergence of a new class based on the comparing;

e. identifying the disappearance of a prior class based on the comparing;

f. identifying one or more outlier seizure events not part of any class;

g. identifying an effect of a therapy;

h. providing a therapy to the patient in response to the comparing;

i. identifying a proposed change in therapy;

j. identifying a proposed additional therapy;

k. identifying an extreme seizure event;

l. identifying a worsening trend in the patient'"'"'s seizures;

m. identifying an improvement trend in the patient'"'"'s seizures;

n. downgrading the patient'"'"'s condition in response to a worsening in the patient'"'"'s seizures; and

o. upgrading the patient'"'"'s condition in response to an improvement in the patient'"'"'s seizures.

22. The medical device system of claim 21, further comprising at least one of a cardiac data acquisition unit configured to acquire cardiac data from the patient, a respiration acquisition unit configured to acquire respiratory data from the patient, or a kinetic unit configured to acquire kinetic data from the patient.

23. The medical device system of claim 22, wherein the seizure determination unit is configured to detect a plurality of seizure events based at least in part upon one or more of cardiac data acquired by the cardiac data acquisition unit, respiratory data acquired by the respiration acquisition unit, or kinetic activity data acquired by the kinetic unit.

24. The medical device system of claim 22, wherein the seizure metric determination unit is configured to determine, for each seizure event, at least one seizure metric value characterizing the seizure event, wherein each of said at least one seizure metric values is further based at least in part on at least one of a cardiac data index, a respiratory index, or a kinetic activity index.

25. The medical device system of claim 24, wherein the seizure metric determination unit is configured to determine at least two seizure metric values;

wherein the classification analysis unit is configured to identify one or more seizure classes by determining one or more relationships among at least a portion of the plurality of seizure events, wherein the one or more relationships are based on the at least two seizure metric values for each seizure event; and

wherein the classification analysis comparator unit is configured to identify a change in at least one class of said one or more seizure classes from said first classification analysis to said second classification analysis.

26. The medical device system of claim 21, wherein the classification analysis unit is configured to identify one or more seizure classes by determining one or more relationships among at least a portion of the plurality of seizure events, wherein the one or more relationships are based on the at least one seizure metric value for each seizure event;

and

wherein the classification analysis comparator unit is configured to identify a change in at least one class of said one or more seizure classes from said first classification analysis to said second classification analysis.

27. The medical device system of claim 21, wherein the responsive action unit is further configured to identify a change in a relationship between a first class and a second class of seizures in moving from the first classification analysis to the second classification analysis.

28. The medical device system of claim 21, wherein the at least one seizure metric value comprises at least one of:

a seizure severity index,

an ictal energy level,

a post-ictal energy level,

a patient seizure impact, or

a rate of change of one of the foregoing over at least one of a microscopic, mesoscopic or macroscopic time scale.

29. The medical device system of claim 21, wherein the classification analysis unit is further configured to at least one of:

identify said classes by at least one mathematical analysis operation selected from a statistical analysis, a graphical analysis, an unsupervised machine learning analysis, a supervised machine learning analysis, and a semisupervised machine learning analysis, wherein the statistical analysis comprises one or more of identifying a measure of central tendency of the class based on the at least one seizure metric,

determine one or more percentile values based on the at least one seizure metric; and

determine one or more distributions based on the at least one seizure metric.

30. The medical device system of claim 21, wherein the first seizure class comprises non-extreme seizures and the second class comprises extreme seizures.

31. The medical device system of claim 30, wherein the extreme seizures comprise at least one of seizures having a risk of SUDEP or seizures having an increased risk of SUDEP.

32. The medical device system of claim 21, wherein at least one of the seizure determination unit, the blood gases acquisition unit, the seizure metric determination unit, the classification analysis unit, the classification analysis comparator unit, or the responsive action unit is configured to be implantable in the patient'"'"'s body.

33. The medical device system of claim 21, wherein at least one of the seizure determination unit, the blood gases acquisition unit, the seizure metric determination unit, the classification analysis unit, the classification analysis comparator unit, or the responsive action unit is configured to be sited externally to the patient'"'"'s body.

34. The medical device system of claim 21, wherein the blood gases acquisition unit is further configured to only acquire oxygen saturation data of the patient in response to a detection of a seizure event by the seizure determination unit.

35. The medical device system of claim 21, further comprising:

a cardiac data acquisition unit configured to acquire cardiac data from the patient; and

a cardiac arrhythmia detection unit configured to identify a cardiac arrhythmia or at least one risk factor for a cardiac arrhythmia from the cardiac data;

wherein the blood gases acquisition unit is further configured to only acquire oxygen saturation data of the patient in response to an identification of the cardiac arrhythmia or the at least one risk factor for the cardiac arrhythmia.

36. The medical device system of claim 21, wherein the responsive action unit is configured to deliver an oxygen treatment to the patient.

37. The medical device system of claim 21, further comprising:

a patient seizure impact unit configured to determine a patient impact value of each of the plurality of seizure events on the patient;

wherein the classification analysis unit is configured to perform a first classification of a first portion of the plurality of seizure events, the first classification analysis comprising assigning each seizure event in the first portion to at least one seizure class based upon the proximity of the patient impact values to each other; and

further configured to perform a second classification analysis of a second portion of the plurality of seizure events, the second classification analysis comprising assigning each seizure event in the second portion to at least one seizure class based upon the proximity of the patient impact values, wherein said second portion comprises at least one seizure event not present in the first portion.

38. A medical device, comprising:

a seizure determination unit configured to detect at least one seizure event based upon body data of a patient;

a cardiac data acquisition unit configured to receive cardiac data of the patient;

a kinetic unit configured to acquire kinetic data of the patient;

a blood gases acquisition unit configured to acquire oxygen saturation data of the patient;

a seizure metric determination unit configured to determine, for the seizure event, at least one seizure metric value characterizing the seizure event, wherein said at least one seizure metric value is based at least in part on at least one of a cardiac index, a kinetic index, or an oxygen saturation index;

a classification analysis unit configured to classify the seizure event based at least in part upon the at least one seizure metric value;

and

a responsive action unit configured to perform at least one of;

a. reporting a classification of the seizure event;

b. identifying the emergence of a new class;

c. identifying the disappearance of a prior class;

d. identifying one or more outlier seizure events not part of any class;

e. identifying an effect of a therapy;

f. providing a therapy to the patient;

g. identifying a proposed change in therapy;

h. identifying a proposed additional therapy;

i. identifying an extreme seizure event;

j. identifying a worsening trend in the patient'"'"'s seizures;

k. identifying an improvement trend in the patient'"'"'s seizures;

l. downgrading the patient'"'"'s condition in response to a worsening in the patient'"'"'s seizures; and

m. upgrading the patient'"'"'s condition in response to an improvement in the patient'"'"'s seizures.

39. The medical device of claim 38, wherein the at least one seizure metric value is indicative of at least one of an arrhythmia, an electrocardiogram (EKG) morphology change, or a heart rate variability (HRV) change.

40. The medical device system of claim 38, further comprising:

a patient seizure impact unit configured to determine a patient impact value of the at least one seizure event on the patient;

wherein the classification analysis unit is configured to classify the at least one seizure event, based at least in part upon the patient impact value.

41. The medical device system of claim 38, further comprising:

an event/warning unit configured to issue a warning of at least one of a risk of SUDEP or an increased risk of SUDEP, in response to the at least one action performed by the responsive action unit.

42. A medical device, comprising:

a seizure determination unit configured to detect at least one seizure event based upon body data of a patient;

a cardiac data acquisition unit configured to receive cardiac data of the patient;

a kinetic unit configured to acquire kinetic data of the patient;

a seizure metric determination unit configured to determine, for the seizure event, at least one seizure metric value characterizing the seizure event, wherein said at least one seizure metric value is based at least in part on at least one of a cardiac index or a kinetic index;

a classification analysis unit configured to classify the seizure event based at least in part upon the at least one seizure metric value;

and

a responsive action unit configured to perform at least one of;

a. reporting a classification of the seizure event;

b. identifying the emergence of a new class;

c. identifying the disappearance of a prior class;

d. identifying one or more outlier seizure events not part of any class;

e. identifying an effect of a therapy;

f. providing a therapy to the patient;

g. identifying a proposed change in therapy;

h. identifying a proposed additional therapy;

i. identifying an extreme seizure event;

j. identifying a worsening trend in the patient'"'"'s seizures;

k. identifying an improvement trend in the patient'"'"'s seizures;

l. downgrading the patient'"'"'s condition in response to a worsening in the patient'"'"'s seizures; and

m. upgrading the patient'"'"'s condition in response to an improvement in the patient'"'"'s seizures.

43. The medical device of claim 42, further comprising a blood gases acquisition unit configured to acquire oxygen saturation data of the patient;

wherein the seizure metric determination unit is configured to determine said at least one seizure metric value based at least in part on an oxygen saturation index.

44. The medical device system of claim 42, further comprising:

a patient seizure impact unit configured to determine a patient impact value of the at least one seizure event on the patient;

wherein the classification analysis unit is configured to classify the at least one seizure event, based at least in part upon the patient impact value.

45. The medical device system of claim 42, further comprising:

an event/warning unit configured to issue a warning of at least one of a risk of SUDEP or an increased risk of SUDEP, in response to the at least one action performed by the responsive action unit.