# US 8,786,624 B2 · Processing for multi-channel signals

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- Number: US 8,786,624 B2
- Document ID: US8786624B2
- Status: Granted
- Kind: B2
- Application number: 12/792,582
- Filed: 2010-06-02
- Published: 2010-12-02
- Granted: 2014-07-22
- Earliest claimed: 2009-06-02
- Assignees: Cyberonics, Inc.
- Applicants: Javier Ramón Echauz; David E. Snyder; Kent W. Leyde
- Inventors: Javier Ramón Echauz; David E. Snyder; Kent W. Leyde
- Web page: https://patentgrep.com/patent/US8786624B2

## CPC

- A61B 5/372: Analysis of electroencephalograms
- A61B 5/076: {Permanent implantation}
- A61B 5/4094: {Diagnosing or monitoring seizure diseases, e.g. epilepsy}
- G06T 11/26: Drawing of charts or graphs
- A61B 2560/0271: using a remote monitoring unit
- G06F 2218/12: Classification; Matching
- A61B: DIAGNOSIS; SURGERY; IDENTIFICATION (usefulness limited to only animals A61D)
- G06T: IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06F: ELECTRIC DIGITAL DATA PROCESSING (computer systems based on specific computational models G06N)

## 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

### Claim 1

A method for displaying information to a user, the method comprising:
accepting input from the user selecting a metric to be displayed, said selected metric being associated with a multi-channel signal derived from outputs of a plurality of sensors positioned to acquire physiological signals, wherein each channel of the multi-channel signal corresponds to a different one of the plurality of sensors;
displaying with a processor and a computer graphical display interface 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 with the processor and the computer graphical display interface a time event index wherein the corresponding metric meets a predetermined condition, wherein the predetermined condition comprises the selected metric being greater than a predetermined threshold;
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 when the user so specifies.

### Claim 2 (depends on 1)

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

### Claim 3 (depends on 1)

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.

### Claim 4 (depends on 1)

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

### Claim 5 (depends on 1)

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.

### Claim 6 (depends on 1)

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.

### Claim 7 (depends on 1)

The method of claim 1, further comprising:
accepting input from the user indicating a color scheme for the backdrop pattern to be used.

### Claim 8 (depends on 1)

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.

### Claim 9 (depends on 8)

The method of claim 8, the multi-channel signal comprising a multi-channel EEG signal.

### Claim 10

A method for detecting anomalies in a multi-channel signal, the method comprising:
sampling the multi-channel signal over a time window;
computing with a processor 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 positioned to acquire physiological signals from a body, 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.

### Claim 11

A method for detecting anomalies in a multi-channel signal, the method comprising:
sampling the multi-channel signal over a time window;
computing with a processor 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.

### Claim 12 (depends on 11)

The method of claim 11, further comprising, when 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.