Detecting, assessing and managing epilepsy using a multi-variate, metric-based classification analysis

• US 9,538,951 B2
• Filed: 11/09/2015
• Issued: 01/10/2017
• Est. Priority Date: 10/01/2010
• Status: Active Grant

• Current Assignee: Flint Hills Scientific, LLC
• Original Assignee: Flint Hills Scientific, LLC
• Inventors: Osorio, Ivan
• Primary Examiner(s): Gedeon, Brian T
• Application Number:
  US14/936,492
• Publication Number:
  US 20160058359A1
• Time in Patent Office: 428 Days
• Field of Search: None
• 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. A medical device system, 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 seizure metric determination unit configured to determine, for the seizure event, at least two seizure metric values characterizing the seizure event, wherein each of said at least two seizure metric values is based at least in part on a cardiac index;

a classification analysis unit configured to classify the seizure event as resulting in cardiac dysfunction based at least in part upon the 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 a plurality of seizure events, wherein the one or more relationships are based on the at least two seizure metric values for each seizure event;

a classification analysis comparator unit configured to identify a change in at least one class of said one or more seizure classes from a first classification analysis to a second classification analysis; and

an event/warning unit configured to issue a warning of the seizure event resulting in cardiac dysfunction.

2. The medical device system of claim 1, 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.

3. The medical device system of claim 1, 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 a plurality of seizure events, wherein the one or more relationships are based on the at least one seizure metric value for each seizure event.

4. The medical device system of claim 1, 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.

5. The medical device system of claim 1, further comprising:

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

wherein the classification analysis unit is configured to perform a first classification of a first portion of a 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.

6. The medical device system of claim 5, wherein the patient seizure impact unit is configured to determine the patient impact value dynamically.

7. The medical device system of claim 5, wherein the patient seizure impact unit is configured to determine the patient impact value specifically for the patient based upon observable factors of the patient.

8. The medical device system of claim 1, 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.

9. The medical device system of claim 1, wherein at least one of the seizure determination unit, the cardiac data acquisition unit, the seizure metric determination unit, the classification analysis unit, or the event/warning unit is configured to be implantable in the patient'"'"'s body.

10. The medical device system of claim 1, wherein at least one of the seizure determination unit, the cardiac data acquisition unit, the seizure metric determination unit, the classification analysis unit, or the event/warning unit is configured to be sited externally to the patient'"'"'s body.

11. A method, comprising:

receiving body data of a patient;

detecting at least one seizure event based upon the received body data;

receiving cardiac data of the patient;

determining, for the at least one seizure event, at least two seizure metric values characterizing the seizure event, wherein each of said at least two seizure metric values is based at least in part on a cardiac index;

classifying the seizure event as resulting in cardiac dysfunction based at least in part upon the at least two seizure metric values, wherein the classifying comprises identifying one or more seizure classes by determining one or more relationships among at least a portion of a 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

issuing a warning of the seizure event resulting in cardiac dysfunction.

12. The method of claim 11, 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.

13. The method of claim 11, wherein the classifying comprises identifying one or more seizure classes by determining one or more relationships among at least a portion of a plurality of seizure events, wherein the one or more relationships are based on the at least one seizure metric value for each seizure event.

14. The method of claim 11, 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.

15. The method of claim 11, further comprising:

determining a patient impact value of at least one seizure event on the patient;

wherein the classifying comprises performing a first classification of a first portion of a 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

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.

16. The method of claim 15, wherein the determining the patient impact value is performed dynamically.

17. The method of claim 15, wherein the determining the patient impact value is performed specifically for the patient based upon observable factors of the patient.

18. The method of claim 11, wherein the classifying further comprises at least one of:

identifying 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,

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

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

19. A non-transitory computer readable program storage unit encoded with instructions that, when executed by a computer, perform a method, comprising:

receiving body data of a patient;

detecting at least one seizure event based upon the received body data;

receiving cardiac data of the patient;

determining, for the at least one seizure event, at least two seizure metric values characterizing the seizure event, wherein each of said at least two seizure metric values is based at least in part on a cardiac index;

classifying the seizure event as resulting in cardiac dysfunction based at least in part upon the at least two seizure metric values, wherein the classifying comprises identifying one or more seizure classes by determining one or more relationships among at least a portion of a 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

issuing a warning of the seizure event resulting in cardiac dysfunction.