Multi-Channel Signal Anomaly Detection via Data Condition Number
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Solution Overview
Problem
Multi-channel signal processing is hindered by anomalies caused by external interference and sensor disruptions, which degrade signal quality and accuracy if left untreated.
Innovation Solution
A method and apparatus for detecting anomalies in multi-channel signals by computing an anomaly metric, such as the data condition number, over a time window and identifying anomalies based on its magnitude, along with an anomaly identification module to flag and address these anomalies.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Duration of action of moving object
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 and classification before the anomalies can significantly degrade signal quality. By continuously monitoring signal characteristics and identifying anomalies early in the processing pipeline, the system can take corrective actions such as flagging affected segments or adjusting processing parameters to maintain signal reliability over extended sampling periods
Solution Approach 2:
The patent introduces an intermediary anomaly detection and classification module that sits between the signal acquisition and processing stages. This intermediary component analyzes signal characteristics, identifies anomalies, and provides corrected or flagged signal data to subsequent processing stages, thereby maintaining signal quality even during extended sampling when artifacts are present
2Measurement precision
If anomaly detection and processing is implemented, then signal quality and accuracy are improved, but processing complexity increases
Solution Approach 1:
The anomaly detection and processing system is segmented into distinct functional modules: anomaly detection, anomaly classification, and anomaly handling. Each module performs a specific function with well-defined inputs and outputs, making the overall complex system manageable and maintainable while improving signal accuracy through systematic anomaly 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.