Detecting seizures based on heartbeat data

• US 9,918,670 B2
• Filed: 03/03/2017
• Issued: 03/20/2018
• Est. Priority Date: 04/25/2014
• Status: Active Grant

• Current Assignee: LivaNova USA, Inc. (LivaNova Plc)
• Original Assignee: Cyberonics, Inc
• Inventors: Sabesan, Shivkumar
• Primary Examiner(s): PATTON, AMANDA K
• Application Number:
  US15/449,405
• Publication Number:
  US 20170172483A1
• Time in Patent Office: 382 Days
• CPC Class Codes: A61B 5/0006 ECG or EEG signals
• CPC Class Codes: A61B 5/01 Measuring temperature of body parts ; Diagnostic temperature sensing, e.g. for malignant or inflamed tissue clinical contact thermometers G01K13/20
• 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 5/024 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 5/05 Detecting, measuring or recording for diagnosis by means of electric currents or magnetic fields; Measuring using microwaves or radio waves impedance plethysmography A61B5/0295; measuring movement of the entire body or parts thereof A61B5/11; detecting, measuring or recording bioelectric or biomagnetic signals of the body or parts thereof A61B5/24
• CPC Class Codes: A61B 5/053 Measuring electrical impedance or conductance of a portion of the body
• CPC Class Codes: A61B 5/1118 Determining activity level
• CPC Class Codes: A61B 5/352 Detecting R peaks, e.g. for synchronising diagnostic apparatus; Estimating R-R interval
• 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 5/4809 Sleep detection, i.e. determining whether a subject is asleep or not
• CPC Class Codes: A61B 5/4842 Monitoring progression or stage of a disease
• CPC Class Codes: A61B 5/4866 Evaluating metabolism A61B5/083 takes precedence
• 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: A61N 1/36064 Epilepsy
• CPC Class Codes: A61N 1/36139 with automatic adjustment

Abstract

A method includes receiving heartbeat data of a patient and receiving activity data of the patient. The activity data includes one or more activity values that are related to an activity level of the patient and that are measured independently of the heartbeat data. The method further includes determining a value of a weighting factor based on the activity data. The method also includes determining modified heartbeat data by applying the weighting factor to at least a portion of the heartbeat data. The method also includes detecting a seizure event based on the modified heartbeat data.

Claims

1. A method comprising:

receiving heartbeat data of a patient;

receiving accelerometer data of the patient;

determining, using the accelerometer data, that the patient is transitioning from a sleep state to an awake state;

decreasing a weighting factor based on the transition;

determining modified heartbeat data by applying the weighting factor to at least a portion of the heartbeat data; and

detecting a seizure event based on the modified heartbeat data.

2. The method of claim 1, wherein determining the modified heartbeat data includes determining a background (BG) heart rate based on a previously-determined BG heart rate, a most recent R-R interval, and the weighting factor.

3. The method of claim 2, wherein the BG heart rate is determined as a sum of a first value and a second value, wherein the first value is the weighting factor times the previously-determined BG heart rate and the second value is one minus the weighting factor times the most recent R-R interval.

4. The method of claim 2, wherein determining the modified heartbeat data further includes determining a foreground (FG) heart rate based on one or more R-R intervals of the heartbeat data.

5. The method of claim 4, wherein detecting the seizure event comprises comparing a ratio of the FG heart rate and the BG heart rate to a seizure detection threshold.

6. The method of claim 1, wherein the heartbeat data of the patient includes electrocardiogram data.

7. The method of claim 6, wherein the electrocardiogram data indicates a timing related to a plurality of R-waves.

8. The method of claim 1, further comprising causing, in response to the detection of the seizure event, initiation of at least one of a stimulation to tissue of the patient, a reporting of the occurrence of the seizure event, or a recording of the seizure event.

9. A medical device comprising:

a first interface to receive heartbeat data of a patient;

a second interface to receive acceleration data of the patient; and

a processor configured to;

determine, using the accelerometer data, that the patient is transitioning from a sleep state to an awake state;

decrease a weighting factor based on the transition;

determine modified heartbeat data by applying the weighting factor to at least a portion of the heartbeat data; and

detect a seizure event based on the modified heartbeat data.

10. The medical device of claim 9, wherein determining the modified heartbeat data includes determining a background (BG) heart rate based on a previously-determined BG heart rate, a most recent R-R interval, and the weighting factor.

11. The medical device of claim 10, wherein the BG heart rate is determined as a sum of a first value and a second value, wherein the first value is the weighting factor times the previously-determined BG heart rate and the second value is one minus the weighting factor times the most recent R-R interval.

12. The medical device of claim 10, wherein determining the modified heartbeat data further includes determining a foreground (FG) heart rate based on one or more R-R intervals of the heartbeat data, wherein the seizure event is detected by comparing a ratio of the FG heart rate and the BG heart rate to a seizure detection threshold.

13. The medical device of claim 9, wherein the processor, in response to the detection of the seizure event, provides a signal to at least one of stimulation device disposed to provide stimulation to tissue of the patient, a signal indicating the occurrence of the seizure event, and a signal initiating the recording of the seizure event.

14. A non-transitory computer-readable medium storing instructions that are executable by a processor to cause the processor to perform operations including:

receiving heartbeat data of a patient;

receiving accelerometer data of the patient;

determining, using the accelerometer data, that the patient is transitioning from a sleep state to an awake state;

decreasing a weighting factor based on the transition;

determining modified heartbeat data by applying the weighting factor to at least a portion of the heartbeat data; and

detecting a seizure event based on the modified heartbeat data.

15. The non-transitory computer-readable medium of claim 14, wherein determining the modified heartbeat data includes determining a background (BG) heart rate based on a previously-determined BG heart rate, a most recent R-R interval, and the weighting factor.

16. The non-transitory computer-readable medium of claim 15, wherein the BG heart rate is determined as a sum of a first value and a second value, wherein the first value is the weighting factor times the previously-determined BG heart rate and the second value is one minus the weighting factor times the most recent R-R interval.

17. The non-transitory computer-readable medium of claim 15, wherein determining the modified heartbeat data further includes determining a foreground (FG) heart rate based on one or more R-R intervals of the heartbeat data, wherein the seizure event is detected by comparing a ratio of the FG heart rate and the BG heart rate to a seizure detection threshold.

18. The non-transitory computer-readable medium of claim 14, wherein the processor, in response to the detection of the seizure event, provides a signal to at least one of stimulation device disposed to provide stimulation to tissue of the patient, a signal indicating the occurrence of the seizure event, and a signal initiating the recording of the seizure event.