Processing for Multi-Channel Signals

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.

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.

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.

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.