Processing for Multi-Channel Signals

• US 20100302270A1
• Filed: 06/02/2010
• Published: 12/02/2010
• Est. Priority Date: 06/02/2009
• Status: Active Grant

• Current Assignee: Cyberonics, Inc
• Original Assignee: Cyberonics, Inc
• Inventors: Echauz, Javier Ramón, Snyder, David E., Leyde, Kent W.
• Granted Patent: US 8,786,624 B2
• Time in Patent Office: Days
• US Class Current: 345/589
• CPC Class Codes: A61B 2560/0271 using a remote monitoring unit
• CPC Class Codes: A61B 5/076 Permanent implantations telemetry using implanted circuitry A61B5/0031; implanted stimulators for therapy A61N1/3605, A61N1/362, A61N1/372
• 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: G06F 2218/12 Classification; Matching
• CPC Class Codes: G06T 11/206 Drawing of charts or graphs

Abstract

Method and apparatus for improved processing for multi-channel signals. In an exemplary embodiment, an anomaly metric is computed for a multi-channel signal over a time window. The magnitude of the anomaly metric may be used to determine whether an anomaly is present in the multi-channel signal over the time window. In an exemplary embodiment, the anomaly metric may be a condition number associated with the singular values of the multi-channel signal over the time window, as further adjusted by the number of channels to produce a data condition number. Applications of the anomaly metric computation include the scrubbing of signal archives for epileptic seizure detection/prediction/counter-prediction algorithm training, pre-processing of multi-channel signals for real-time monitoring of bio-systems, and boot-up and/or adaptive self-checking of such systems during normal operation.

Claims

1. A method for displaying information associated with a multi-channel signal to a user, the method comprising:

accepting input from the user selecting a metric to be displayed

displaying at least one time-series plot of the selected metric associated with the multi-channel signal;

using a backdrop pattern, indicating on the at least one time-series plot portions of the plot having at least one identified characteristic;

for each of the at least one time-series plot displayed, displaying a time event index wherein the corresponding metric meets a predetermined condition;

accepting input from the user as to whether to display further information associated with the time event index; and

displaying the further information associated with the time event index if the user so specifies.

2. The method of claim 1, the metric comprising a data condition number calculated for the multi-channel signal.

3. The method of claim 1, the at least one time-series plot comprising a plurality of time-series plots, each time-series plot associated with a subject ID.

4. The method of claim 1, the at least one identified characteristic including a portion of the time-series corresponding to a seizure segment.

5. The method of claim 1, the backdrop pattern comprising a color scheme, the color scheme representing “

interictal”

segments, “

preseizure”

segments, “

other”

segments, “

dropouts,” and

“

seizure”

events.

6. The method of claim 1, the predetermined condition comprising the selected metric being greater than a predetermined threshold.

7. The method of claim 1, the input from the user as to whether to display further information associated with the time event index comprising positioning of a cursor over the time event index.

8. The method of claim 1, further comprising:

accepting input from the user indicating a color scheme for the backdrop pattern to be used.

9. The method of claim 1, the displaying the further information associated with the time event index comprising:

displaying a pop-up plot of a portion of the multi-channel signal corresponding to the time event index.

10. The method of claim 9, the multi-channel signal comprising a multi-channel EEG signal.

11. A method for detecting anomalies in a multi-channel signal, the method comprising:

sampling the multi-channel signal over a time window;

computing an anomaly metric for the multi-channel signal over the time window; and

identifying the presence of an anomaly based on the magnitude of the anomaly metric;

the computing an anomaly metric comprising;

computing a condition number of the multi-channel signal over the time window; and

adjusting the condition number based on a parameter of the multi-channel signal to generate a data condition number (DCN);

the identifying the presence of an anomaly comprising comparing the magnitude of the data condition number (DCN) to at least one threshold;

the method further comprising generating one of the at least one threshold, the generating comprising;

generating a histogram of number of instances detected for each of a plurality of DCN values;

generating a cumulative distribution function from the histogram, the cumulative distribution function mapping each DCN value to a percentage value; and

determining the generated threshold as the DCN mapped to a corresponding predetermined percentage value by the cumulative distribution function.

12. A method for detecting anomalies in a multi-channel signal, the method comprising:

sampling the multi-channel signal over a time window;

computing an anomaly metric for the multi-channel signal over the time window; and

identifying the presence of an anomaly based on the magnitude of the anomaly metric;

the computing an anomaly metric comprising;

computing a condition number of the multi-channel signal over the time window; and

adjusting the condition number based on a parameter of the multi-channel signal to generate a data condition number (DCN);

the identifying the presence of an anomaly comprising comparing the magnitude of the rate of change of the data condition number (DCN) to at least one threshold.

13. A method for detecting anomalies in a multi-channel signal, the method comprising:

sampling the multi-channel signal over a time window;

computing an anomaly metric for the multi-channel signal over the time window; and

identifying the presence of an anomaly based on the magnitude of the anomaly metric;

the computing an anomaly metric comprising;

computing a condition number of the multi-channel signal over the time window; and

adjusting the condition number based on a parameter of the multi-channel signal to generate a data condition number (DCN);

the multi-channel signal comprising a signal sampled from a plurality of electrodes implanted on or in a patient'"'"'s brain, the method further comprising;

generating a DCN time series corresponding to a plurality of time windows;

generating an anomaly log based on the DCN time series;

merging anomalies in the anomaly log separated by less than a minimum separation to generate a modified anomaly log;

identifying segments of the multi-channel signal corresponding to anomalies in the modified anomaly log; and

outputting time-expanded versions of the identified segments to a record;

the identifying the presence of an anomaly comprising matching the DCN time series to at least one known pattern of DCN time series corresponding to an anomalous condition.

14. A method for detecting anomalies in a multi-channel signal, the method comprising:

sampling the multi-channel signal over a time window;

computing an anomaly metric for the multi-channel signal over the time window; and

identifying the presence of an anomaly by comparing the magnitude of the anomaly metric to an optimum threshold;

the computing an anomaly metric comprising;

computing a condition number of the multi-channel signal over the time window; and

adjusting the condition number based on a parameter of the multi-channel signal to generate a data condition number (DCN);

the method further comprising;

generating a DCN time series corresponding to a plurality of time windows;

generating an event log based on the DCN time series, the generating an event log comprising identifying an event as a contiguous set of DCN values greater than a candidate threshold;

merging events in the event log separated by less than a minimum separation to generate a modified event log;

repeating the steps of generating a DCN time series, generating an event log, and merging events using a plurality of candidate thresholds;

generating a plot of number of events identified in a modified event log versus candidate threshold used;

attempting to identify at least one inflection point in the generated plot; and

setting the optimum threshold to be the candidate threshold corresponding to the inflection point with the largest abscissa in the plot.

15. The method of claim 14, further comprising, if the attempt to identify at least one inflection point is unsuccessful:

identifying a maximum number of events corresponding to a candidate threshold in the histogram; and

setting the optimum threshold to be a candidate threshold whose corresponding number of events is less than or equal to a fixed percentage of the maximum number of events.