Seizure sensing and detection using an implantable device

• US 20030004428A1
• Filed: 06/28/2001
• Published: 01/02/2003
• Est. Priority Date: 06/28/2001
• Status: Active Grant

• Current Assignee: Neuropace Inc.
• Original Assignee: Neuropace Inc.
• Inventors: Gibb, Barbara, Kirkpatrick, Bruce, Gurunathan, Suresh K., Baysinger, Craig, Tcheng, Thomas K., Pless, Benjamin D., Archer, Stephen T.
• Granted Patent: US 6,810,285 B2
• Time in Patent Office: Days
• US Class Current: 600/544
• CPC Class Codes: A61B 5/291 for electroencephalography [EEG]
• CPC Class Codes: A61B 5/316 Modalities, i.e. specific diagnostic methods
• CPC Class Codes: A61B 5/369 Electroencephalography [EEG] devices for psychotechnics A61B5/16
• 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 analysis unit and a feature-based analysis unit.

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 implanted in the patient.

4. The implantable device of claim 3, wherein the housing is 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 analysis unit comprises a wave morphology analysis unit.

9. The implantable device of claim 1, wherein the window-based analysis unit comprises a line length function analysis tool.

10. The implantable device of claim 9, wherein the window-based analysis unit further comprises an area function analysis tool.

11. 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 feature in the electrical signal and calculate at least one window parameter in the electrical signal; 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.

16. The method for detecting a neurological event of claim 15, wherein the processing step comprises the steps of:

sampling the electrical signal to create a sampled digital signal;

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

calculating a window parameter in the sampled digital signal with a window analysis unit.

17. The method for detecting a neurological event of claim 16, wherein the calculating step comprises the step of computing a line length function.

18. The method for detecting a neurological event of claim 17, further comprising the step of transforming the sampled digital signal prior to computing the line length function.

19. The method for detecting a neurological event of claim 16, wherein the calculating step comprises the step of computing a line length function.

20. The method for detecting a neurological event of claim 19, further comprising the step of transforming the sampled digital signal prior to computing the area function.

21. The method for detecting a neurological event of claim 16, wherein the identifying step comprises the steps of:

locating a half wave in the sampled digital signal;

determining 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, labeling the half wave as a qualified half wave.

if the half wave is a qualified half wave, storing a record representative of the qualified half wave.

22. The method for detecting a neurological event of claim 21, wherein the locating step comprises the steps of:

finding a starting point for the half wave, wherein the starting point has a first sample value equal to an initial current EEG sample value;

calculating an ending threshold by combining the current EEG sample value and a hysteresis value;

measuring a current EEG sample value;

comparing the current EEG sample value to the ending threshold;

repeating the calculating, measuring, and comparing steps until the current EEG sample value exceeds the ending threshold;

establishing an ending point for the half wave, wherein the ending point has an ending sample value equal to the current EEG sample value;

calculating a half wave amplitude equal to a difference between the first sample value and the current EEG sample value; and

calculating a half wave duration equal to a difference between a time corresponding to the ending point and a time corresponding to the starting point.

23. The method for detecting a neurological event of claim 16, wherein the causing step comprises the steps of:

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.

25. A method for detecting a neurological event of claim 24, wherein the processing step comprises the steps of:

employing the detection subsystem to identify at least a first feature in the first electrical signal and a second feature in the second electrical signal;

employing the detection subsystem to calculate at least a first window parameter in the first electrical signal and a second window parameter in the second electrical signal;

analyzing the first feature and the first window parameter to obtain a first detection channel output; and

analyzing the second feature and the second window parameter to obtain a second detection channel output.

28. The method for detecting a neurological event of claim 27, wherein the low-power mode is characterized by relative inactivity.

12. 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;

wherein the detection subsystem performs at least a portion of a detection task without the use of the central processing unit, thereby allowing the central processing unit to remain in a relatively inactive mode at least part of the time.

13. 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 plurality of electrodes including at least one brain electrode; and

a plurality of event detectors each adapted to analyze a signal from an electrode pair, wherein the electrode pair comprises two electrodes selected from the plurality of electrodes and including at least one brain electrode;

wherein each event detector of the plurality of event detectors is programmable with respect to at least one detection parameter; and

wherein each event detector of the plurality of event detectors is adapted to be selectively coupled to receive a signal from a corresponding electrode pair.

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

a central processing unit;

a detection subsystem comprising at least one electronics module and including a waveform analyzer having a window-based analysis unit adapted to analyze a line length function and an area function in the electrical signal, and a feature-based analysis unit adapted to analyze half waves in the electrical signal; and

a power supply;

wherein the detection subsystem includes a plurality of inputs and a plurality of detection channels;

wherein the window-based analysis unit includes a plurality of window analysis units and the feature-based analysis unit includes a plurality of wave morphology analysis units;

wherein the detection subsystem performs at least a portion of a detection task without the use of the central processing unit, thereby allowing the central processing unit to remain in a relatively inactive mode at least part of the time;

wherein the central processing unit, the detection subsystem, and the power supply are enclosed within a housing and implanted in the patient; and

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.

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 the steps of:

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 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.

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

receiving a first electrical signal and a second electrical signal from a plurality of electrodes;

processing the electrical signals with a detection subsystem to obtain a first detection channel output and a second detection channel output;

combining the first detection channel output and the second detection channel output to obtain an event detector output; and

causing the implantable device to perform an action in accordance with the event detector output.

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

receiving the electrical signal with an electrode pair;

processing the electrical signal;

converting the electrical signal into a digital signal;

performing a feature extraction operation on the digital signal;

performing a window calculation operation on the digital signal;

analyzing a result from the feature extraction operation;

analyzing a result from the window calculation operation; and

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

27. 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 the steps of:

causing the processor to enter a low-power mode;

measuring a 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 feature characteristic and the window characteristic with the processor; and

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