Multi-Channel Signal Anomaly Detection via Data Condition Number
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Multi-channel signal processing is hindered by anomalies due to external interference and sensor disruptions, which degrade signal quality and accuracy in applications like neurological monitoring and seismic activity prediction.
Innovation Solution
A method for detecting anomalies in multi-channel signals using a data condition number (DCN) metric, where the condition number is computed and adjusted based on signal parameters, and thresholds are determined through histogram analysis and cumulative distribution functions to identify and flag anomalous segments for further processing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If multi-channel signal processing is performed over extended periods, then more information about the sampled state is obtained, but artifacts and anomalies appear in the signal
Solution Approach 1:
The system performs preliminary anomaly detection by computing the data condition number continuously as signals are sampled. This preliminary action identifies potential artifacts before they significantly degrade the overall signal quality, allowing for preventive measures or flagging of affected segments while preserving the majority of useful information from extended monitoring periods.
Solution Approach 2:
The invention extracts and isolates anomalous segments from the multi-channel signal by comparing the data condition number against thresholds. By separating the identified artifacts from the clean signal portions, the system removes harmful elements while preserving the valuable information contained in the majority of the extended signal record, thus resolving the contradiction between information retention and reliability.
2Reliability
If anomaly detection is performed on multi-channel signals, then signal quality is improved, but processing complexity increases
Solution Approach 1:
The invention replaces complex visual inspection and manual anomaly identification with an automated computational system based on the data condition number. This mathematical approach systematically evaluates signal quality without requiring human intervention, improving reliability while the automation itself manages the processing complexity burden.
Solution Approach 2:
The system transforms the complex multi-channel signal analysis into a simplified parameter evaluation by computing the data condition number, which condenses the quality assessment into a single metric. This parameter transformation reduces the complexity of analyzing multiple channels simultaneously while maintaining comprehensive quality monitoring through threshold comparisons.
3Measurement precision
If thresholds for anomaly detection are set to be highly sensitive, then more anomalies are detected, but false positives increase
Solution Approach 1:
The system initially applies a sensitive threshold to detect all potential anomalies, then performs a second-stage verification by analyzing the characteristics of detected events. This partial application of sensitivity followed by filtering removes false positives while retaining true anomalies, achieving both high detection precision and low false positive rates through staged processing.
Data Source
AI summary
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.