Seizure sensing and detection using an implantable device

• US 7,813,793 B2
• Filed: 10/25/2004
• Issued: 10/12/2010
• Est. Priority Date: 06/28/2001
• Status: Active Grant

• Current Assignee: Neuropace Inc.
• Original Assignee: Neuropace Inc.
• Inventors: Kirkpatrick, Bruce, Archer, Stephen T., Baysinger, Craig M., Gurunathan, Suresh, Tcheng, Thomas K., Pless, Benjamin D., Gibb, Barbara
• Primary Examiner(s): Nasser; Robert L
• Application Number:
  US10/973,091
• Publication Number:
  US 20070016094A1
• Time in Patent Office: 2,178 Days
• Field of Search: None
• US Class Current: 600/544
• CPC Class Codes: A61B 5/293 Invasive
• CPC Class Codes: A61B 5/369 Electroencephalography [EEG] devices for psychotechnics A61B5/16
• CPC Class Codes: A61B 5/372 Analysis of electroencephalograms
• CPC Class Codes: A61B 5/4094 Diagnosing or monitoring seizure diseases, e.g. epilepsy
• CPC Class Codes: A61B 5/6868 Brain
• CPC Class Codes: A61B 5/7203 for noise prevention, reduction or removal
• CPC Class Codes: A61B 5/7242 using integration
• 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 5/7275 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: A61N 1/36064 Epilepsy

Abstract

A system and method for detecting and predicting neurological events with an implantable device uses a relatively low-power central processing unit in connection with signal processing circuitry to identify features (including half waves) and calculate window-based characteristics (including line lengths and areas under the curve of the waveform) in an electrographic signal received from a patient'"'"'s brain. The features and window-based characteristics are combinable in various ways according to the invention to detect and predict neurological events in real time, enabling responsive action by the implantable device.

Claims

1. An implantable device for detecting a neurological event by analyzing an electrical signal from a patient'"'"'s brain, the device comprising:

an implantable control module;

a central processing unit located within the control module; and

a detection subsystem located within the control module and comprising a waveform analyzer having a window-based analyzer configured to identify waveform features using sample-to-sample amplitude variations of the electrical signal during a window and a feature-based analyzer configured to identify half wave features of the electrical signal.

2. The implantable device of claim 1, wherein the detection subsystem performs a first portion of an event detection procedure, and the central processing unit performs a second portion of the event detection procedure.

3. The implantable device of claim 1, wherein the central processing unit and the detection subsystem are enclosed within a housing, and wherein the housing is adapted to be implanted in the patient.

4. The implantable device of claim 3, wherein the housing is adapted to be implanted intracranially in the patient.

5. The implantable device of claim 1, wherein the neurological event comprises an epileptic seizure, an onset of an epileptic seizure, a precursor to an epileptic seizure, an episode of a movement disorder, an onset of an episode of a movement disorder, a precursor to an episode of a movement disorder, a migraine headache, an onset of a migraine headache, a precursor to a migraine headache, an episode of pain, an onset of an episode of pain, a precursor to an episode of pain, an episode of depression, an onset of an episode of depression, or a precursor to an episode of depression.

6. The implantable device of claim 1, wherein the neurological event comprises an event predictive of an episode of clinical symptoms selected from the group consisting of an epileptic seizure, an episode of a movement disorder, a migraine headache, an episode of pain, and an episode of depression.

7. The implantable device of claim 1, wherein the detection subsystem comprises at least one electronics module.

8. The implantable device of claim 1, wherein the feature-based analyzer comprises a wave morphology analyzer.

10. The implantable device of claim 1, wherein the detection subsystem is configured to receive the electrical signal from an electrode pair, identify at least one feature in the electrical signal, calculate at least one window parameter in the electrical signal;

provide an indication to the implantable device to perform an action in accordance with an indication from the detection subsystem, and sample the electrical signal to create a sampled digital signal,

wherein the feature-based analyzer is configured to identify a half wave feature in the sampled digital signal includes the feature-based analyzer is configured to locate a half wave in the sampled digital signal, determine whether an amplitude of the half wave exceeds an amplitude threshold and whether a duration of the half wave exceeds a duration threshold, and if both thresholds are met, label the half wave as a qualified half wave, and if the half wave is a qualified half wave, store a record representative of the qualified half wave, and

wherein the window analyzer is configured to calculate a window parameter in the sampled digital signal.

11. The implantable device of claim 10, wherein the feature-based analyzer is configured to locate a half wave in the sampled digital signal includes:

the feature-based analyzer is configured to find a starting point for the half wave, wherein the starting point has a first sample value equal to an initial current EEG sample value;

the feature-based analyzer is configured to calculate an ending threshold by combining the current EEG sample value and a hysteresis value;

the feature-based analyzer is configured to measure a current EEG sample value;

the feature-based analyzer is configured to compare the current EEG sample value to the ending threshold;

the feature-based analyzer is configured to repeat the calculate, measure, and compares until the current EEG sample value exceeds the ending threshold;

the feature-based analyzer is configured to establish an ending point for the half wave, wherein the ending point has an ending sample value equal to the current EEG sample value;

the feature-based analyzer is configured to calculate a half wave amplitude equal to a difference between the first sample value and the current EEG sample value; and

the feature-based analyzer is configured to calculate a half wave duration equal to a difference between a time corresponding to the ending point and a time corresponding to the starting point.

9. An implantable device for detecting a neurological event by analyzing an electrical signal from a patient'"'"'s brain, the device comprising:

an implantable control module;

a detection subsystem located within the control module and adapted to identify at least one half wave feature in the electrical signal and calculate at least one window parameter in the electrical signal using sample-to-sample amplitude variations of the electrical signal during a window; and

a central processing unit located within the control module and adapted to analyze the at least one feature and the at least one window parameter, and to initiate an action in accordance therewith.

12. A method for detecting a neurological event by analyzing an electrical signal from a patient'"'"'s brain with an implantable device, the method comprising:

receiving the electrical signal with an electrode pair;

processing the electrical signal with a detection subsystem, wherein the detection subsystem is adapted to identify at least one half wave feature in the electrical signal and calculate at least one window parameter in the electrical signal using sample-to-sample amplitude variations of the electrical signal during a window; and

causing the implantable device to perform an action in accordance with an indication from the detection subsystem.

13. The method for detecting a neurological event of claim 12, wherein the processing step comprises:

sampling the electrical signal to create a sampled digital signal;

identifying a feature in the sampled digital signal with a wave morphology analyzer; and

calculating a window parameter in the sampled digital signal with a window analyzer.

14. The method for detecting a neurological event of claim 13, wherein the causing step comprises:

processing the feature and the window parameter to determine whether to set a detection flag;

if the detection flag is set, performing an action in accordance with the detection flag.

15. A method for detecting a neurological event by analyzing an electrical signal from a patient'"'"'s brain with an implantable device, the method comprising:

receiving the electrical signal with an electrode pair;

processing the electrical signal with a detection subsystem, wherein the detection subsystem is adapted to identify at least one half wave feature in the electrical signal and calculate at least one window parameter in the electrical signal; and

causing the implantable device to perform an action in accordance with an indication from the detection subsystem,

wherein the processing the electrical signal comprises;

sampling the electrical signal to create a sampled digital signal;

identifying a feature in the sampled digital signal with a wave morphology analyzer; and

calculating a window parameter in the sampled digital signal with a window analyzer,

wherein calculating a window parameter comprises computing a line length function.

16. A method for detecting a neurological event by analyzing an electrical signal from a patient'"'"'s brain with an implantable device, the method comprising:

receiving the electrical signal with an electrode pair;

processing the electrical signal;

converting the electrical signal into a digital signal;

performing a half wave feature extraction operation on the digital signal;

performing a window calculation operation on the digital signal using sample-to-sample amplitude variations of the digital signal during a window;

analyzing a result from the half wave feature extraction operation;

analyzing a result from the window calculation operation; and

performing an action in accordance with the result from the half wave feature extraction operation and the result from the window calculation operation.

17. A method for detecting a neurological event by analyzing an electrical signal from a patient'"'"'s brain with an implantable device, the device having a processor and a detection subsystem, the method comprising:

causing the processor to enter a low-power mode;

measuring a half wave feature characteristic of the electrical signal with the detection subsystem;

measuring a window characteristic of the electrical signal with the detection subsystem;

causing the processor to exit the low-power mode;

analyzing the half wave feature characteristic and the window characteristic with the processor; and

performing an action in accordance with the half wave feature characteristic and the window characteristic.

18. The method for detecting a neurological event of claim 17, wherein the low-power mode is characterized by relative inactivity.

19. An implantable device for detecting a neurological event by analyzing an electrical signal from a patient'"'"'s brain, the device comprising:

an implantable control module;

a central processing unit located within the control module; and

a detection subsystem located within the control module and comprising a waveform analyzer having a window-based analyzer and a feature-based analyzer configured to identify half wave features of the electrical signal, wherein the detection subsystem is configured to receive the electrical signal from an electrode pair, identify at least one feature in the electrical signal, calculate at least one window parameter in the electrical signal, provide an indication to the implantable device to perform an action in accordance with an indication from the detection subsystem, and sample the electrical signal to create a sampled digital signal,

wherein the window analyzer is configured to calculate a window parameter in the sampled digital signal, wherein the window-based analyzer is adapted to analyze a line length function and an area function in the electrical signal,

wherein the feature-based analyzer is configured to identify a half wave feature in the sampled digital signal includes the feature-based analyzer is configured to locate a half wave in the sampled digital signal, determine whether an amplitude of the half wave exceeds an amplitude threshold and whether a duration of the half wave exceeds a duration threshold, and if both thresholds are met, label the half wave as a qualified half wave, and if the half wave is a qualified half wave, store a record representative of the qualified half wave, wherein the feature-based analyzer is adapted to analyze the half waves in the electrical signal.