Method for automated detection of A-waves

• US 20060020222A1
• Filed: 03/09/2005
• Published: 01/26/2006
• Est. Priority Date: 03/09/2004
• Status: Active Grant

• Current Assignee: Neurometrix, Inc.
• Original Assignee: Neurometrix, Inc.
• Inventors: Gozani, Shai N., Kong, Xuan
• Granted Patent: US 7,749,171 B2
• Time in Patent Office: Days
• US Class Current: 600/546
• CPC Class Codes: A61B 5/24 Detecting, measuring or recording bioelectric or biomagnetic signals of the body or parts thereof
• CPC Class Codes: A61B 5/388 Nerve conduction study, e.g. detecting action potential of peripheral nerves

Abstract

In one form of the present invention, there is provided a method for detecting an A-wave, the method comprising: applying a series of stimuli to a nerve; recording a series of late responses; creation of a feature space map from an ensemble of late responses; identification of clusters within the feature space that represent A-wave components; consolidation of A-wave components into a discrete collection of A-waves; removal of false positive A-waves; and extraction of A-wave characteristics. In another form of the present invention, there is provided a system for detecting an A-wave comprising: a stimulation electrode; a stimulation circuit connected to the stimulation electrode for applying a series of stimuli to a nerve; a detection electrode; a detection circuit connected to the detection electrode; and an analyzer connected to the detection electrode and adapted to detect an A-wave by: recording a series of late responses detected by the detection circuit; creation of a feature space map from an ensemble of late responses; identification of clusters within the feature space map that represent A-wave components; consolidation of A-wave components into a discrete collection of A-waves; removal of false positive A-waves; and extraction of A-wave characteristics.

Claims

1. A method for detecting an A-wave, the method comprising:

applying a series of stimuli to a nerve;

recording a series of late responses;

creation of a feature space map from an ensemble of late responses;

identification of clusters within the feature space that represent A-wave components;

consolidation of A-wave components into a discrete collection of A-waves;

removal of false positive A-waves; and

extraction of A-wave characteristics.

2. A method according to claim 1 wherein the feature space map is created from a translation of the late responses.

3. A method according to claim 2 wherein the translation comprises the first derivative of the late responses.

4. A method according to claim 2 wherein the translation comprises the second derivative of the late responses.

5. A method according to claim 2 wherein the translation comprises a linear translation of the late responses.

6. A method according to claim 2 wherein the translation comprises a non-linear translation of the late responses.

7. A method for detecting an A-wave, the method comprising:

applying a series of stimuli to a nerve;

recording a series of evoked bioelectrical responses;

identifying one or more attributes in each of the responses;

utilizing each of the one or more attributes of each of the responses to create a new data set;

creating a search window that is smaller than the new data set;

searching the new data set for trends by;

sequentially applying the search window across the new data set; and

analyzing the search window by counting the number of data points contained within the search window at each separate, sequential search window position;

analyzing the new data set by;

registering a positive for an A-wave component if the number of data points contained within the search window at each separate, sequential search window position exceeds a predetermined threshold; and

registering a negative for an A-wave component if the number of data points contained within the search window at each separate, sequential search window position does not exceed a predetermined threshold; and

consolidating the A-wave components into a single A-wave.

8. A method according to claim 7 wherein the search window has an area such that the height of the search window is proportional to the amplitude location within the new data set.

9. A method for detecting an A-wave, the method comprising:

applying a series of stimuli to a nerve;

recording a series of evoked bioelectrical responses;

identifying one or more attributes in each of the responses;

utilizing each of the one or more attributes of each of the responses to create a new data set;

identifying trends in the new data set; and

analyzing the trends to identify the A-wave.

10. A method according to claim 9 wherein the one or more attributes comprise the amplitude of local maxima and/or local minima.

11. A method according to claim 9 wherein the one or more attributes comprise the absolute value amplitude.

12. A method according to claim 9 wherein the one or more attributes comprise the second derivate of amplitude.

13. A method for diagnosing a disorder in a patient comprising:

detecting an A-wave in a patient by;

applying a series of stimuli to a nerve;

recording a series of late responses;

creation of a feature space map from an ensemble of late responses;

identification of clusters within the feature space map that represent A-wave components;

consolidation of A-wave components into a discrete collection of A-waves;

removal of false positive A-waves; and

extraction of A-wave characteristics; and

comparing the A-wave of the patient with the A-wave of known disorder.

14. A system for detecting an A-wave comprising:

a stimulation electrode;

a stimulation circuit connected to the stimulation electrode for applying a series of stimuli to a nerve;

a detection electrode;

a detection circuit connected to the detection electrode; and

an analyzer connected to the detection electrode and adapted to detect an A-wave by;

recording a series of late responses detected by the detection circuit;

creation of a feature space map from an ensemble of late responses;

identification of clusters within the feature space map that represent A-wave components;

consolidation of A-wave components into a discrete collection of A-waves;

removal of false positive A-waves; and

extraction of A-wave characteristics.