Identifying Seizures Using Heart Data from Two of More Windows

• US 20120271182A1
• Filed: 04/25/2011
• Published: 10/25/2012
• Est. Priority Date: 04/25/2011
• Status: Active Grant

• Current Assignee: LivaNova USA, Inc. (LivaNova Plc)
• Original Assignee: Cyberonics, Inc
• Inventors: LIAO, Wangcai, ZHANG, Jicong
• Granted Patent: US 9,498,162 B2
• Time in Patent Office: Days
• US Class Current: 600/508
• 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 5/02438 with portable devices, e.g. worn by the patient
• CPC Class Codes: A61B 5/0245 by using sensing means generating electric signals, i.e. ECG signals
• CPC Class Codes: A61B 5/4094 Diagnosing or monitoring seizure diseases, e.g. epilepsy
• 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

Abstract

Methods and systems for characterizing a seizure event in a patient, including determining a time of beat sequence of the patient'"'"'s heart, determining a first HR measure for a first window, determining a second HR measure for a second window, wherein at least a portion of the first window occurs after the second window, determining at least one HR parameter based upon said first HR measure and said second HR measure, identifying an onset of the seizure event in response to determining that at least one HR parameter crosses an onset threshold, identifying an end of the seizure event in response to determining that at least one HR parameter crosses an offset threshold.

Claims

1. A method for characterizing a seizure event in a patient, the method comprising:

determining a time of beat sequence of the patient'"'"'s heart;

determining a first HR measure for a first window;

determining a second HR measure for a second window, wherein at least a portion of the first window occurs after the second window;

determining at least one HR parameter based upon said first HR measure and said second HR measure;

identifying an onset of the seizure event in response to determining that at least one HR parameter crosses an onset threshold; and

identifying an end of the seizure event in response to determining that at least one HR parameter crosses a seizure end threshold.

2. The method of claim 1, further comprising an intermediate window, where at least a portion of the intermediate window occurs after the second window and before the first window.

3. The method of claim 2, where the first window, the intermediate window, and the second window are one of time windows and number-of-beats windows.

4. The method of claim 1,

where the first HR measure is a statistical measure of central tendency of HR in the first window;

where the second HR measure is a statistical measure of central tendency of HR in the second window;

where at least one HR parameter is a ratio of the first HR measure to the second HR measure;

where the at least one HR parameter crossing the onset threshold comprises the HR parameter being greater than the onset threshold.

5. The method of claim 4, where the at least one HR parameter crossing the seizure end threshold comprises the HR parameter being less than the seizure end threshold.

6. The method of claim 5, where the statistical measure of central tendency of HR in the first window is a median HR in a foreground time window, and the statistical measure of central tendency of HR in the second window is a median HR in a background window prior to the first window.

7. A system for identifying seizures, the system comprising:

one or more processors;

one or more memory units coupled to the one or more processors;

the system being configured to;

determine a time of beat sequence of the patient'"'"'s heart;

determine a first HR measure for a first window;

determine a second HR measure for a second window, wherein at least a portion of the first window occurs after the second window;

determine at least one HR parameter based upon said first HR measure and said second HR measure;

identify an onset of the seizure event in response to determining that at least one HR parameter crosses an onset threshold;

identify an end of the seizure event in response to determining that at least one HR parameter crosses a seizure end threshold.

8. The system of claim 7, further comprising an intermediate window, where at least a portion of the intermediate window occurs after the second window and before the first window.

9. The system of claim 8, where the first window, the intermediate window, and the second window are one of time windows and number-of-beats windows.

10. The system of claim 7,

where the first HR measure is a statistical measure of central tendency of HR in the first window;

where the second HR measure is a statistical measure of central tendency of HR in the second window;

where at least one HR parameter is a ratio of the first HR measure to the second HR measure;

where the at least one HR parameter crossing the onset threshold comprises the HR parameter being greater than the onset threshold.

11. The system of claim 7, where the at least one HR parameter crossing the seizure end threshold comprises the HR parameter being less than the seizure end threshold.

12. The system of claim 11, where the statistical measure of central tendency of HR in the first window is a median HR in a foreground time window, and the statistical measure of central tendency of HR in the second window is a median HR in a background window prior to the first window.

13. A computer program product embodied in a computer-operable medium, the computer program product comprising logic instructions, the logic instructions being effective to:

determine a first HR measure for a first window in a time series of heart beat data for a patient;

determine a second HR measure for a second window, wherein at least a portion of the first window occurs after the second window;

determine at least one HR parameter based upon said first HR measure and said second HR measure;

identify an onset of the seizure event in response to determining that at least one HR parameter crosses an onset threshold;

identify an end of the seizure event in response to determining that at least one HR parameter crosses a seizure end threshold.

14. The product of claim 13, further comprising an intermediate window, where at least a portion of the intermediate window occurs after the second window and before the first window.

15. The product of claim 14, where the first window, the intermediate window, and the second window are one of time windows and number-of-beat windows.

16. The product of claim 13,

where the first HR measure is a statistical measure of central tendency of HR in the first window;

where the second HR measure is a statistical measure of central tendency of HR in the second window;

where at least one HR parameter is a ratio of the first HR measure to the second HR measure;

where the at least one HR parameter crossing the onset threshold comprises the HR parameter being greater than the onset threshold.

17. The product of claim 16, where the at least one HR parameter crossing the seizure end threshold comprises the HR parameter being less than the seizure end threshold.

18. The product of claim 17, where statistical measure of central tendency of HR in the first window is a median HR in a foreground time window, and the statistical measure of central tendency of HR in the second window is a median HR in a background window prior to the first window.