Seizure sensing and detection using an implantable device

• US 6,810,285 B2
• Filed: 06/28/2001
• Issued: 10/26/2004
• Est. Priority Date: 06/28/2001
• Status: Active Grant

• Current Assignee: Neuropace Inc.
• Original Assignee: Neuropace Inc.
• Inventors: Tcheng, Thomas K., Gibb, Barbara, Kirkpatrick, Bruce, Baysinger, Craig M., Gurunathan, Suresh K., Pless, Benjamin D., Archer, Stephen T.
• Primary Examiner(s): Schaetzle, Kennedy
• Assistant Examiner(s): DROESCH, KRISTEN L
• Application Number:
  US09/896,092
• Publication Number:
  US 20030004428A1
• Time in Patent Office: 1,216 Days
• Field of Search: 600/378, 600/544-545, 607/45
• 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. 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;

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.

2. 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;

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; and

wherein the calculating step comprises the step of computing a line length function.

3. The method for detecting a neurological event of claim 2, further comprising the step of transforming the sampled digital signal prior to computing the line length function.

4. 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;

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; and

wherein the identifying step comprises the step 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, and if the half wave is a qualified half wave, storing a record representative of the qualified half wave.

5. The method for detecting a neurological event of claim 4, 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 EEC sample value and a hysteresis value;

measuring a current EEC sample value;

comparing the current EEC sample value to the ending threshold;

repeating the calculating, measuring, and comparing steps until the current EEC 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 EEC sample value;

calculating a half wave amplitude equal to a difference between the first sample value and the current EEC 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.

6. 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.

7. 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.

8. An implantable device for detecting a neurologcal 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;

wherein the window-based analysis unit comprises a line length function analysis tool.

9. The implantable device of claim 8, wherein the window-based analysis unit further comprises an area function analysis tool.