Physiological Signal Fiducial Point Detection via Spectral Subcomponent Selection
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Solution Overview
Problem
Current physiological signal processing technologies face challenges in accurately detecting fiducial points, such as the QRS complex in ECGs, due to noise, especially in-band noise, and multi-source signal complexities, leading to high false positive and false negative rates, which complicates arrhythmia detection and increases telecommunications and review costs.
Innovation Solution
The method involves decomposing physiological signals into subcomponents, selecting those with overlapping spectral energy with the region of interest, combining relevant subcomponents, and comparing the combination to a threshold to identify the location of the region of interest, using a multi-domain signal processing approach that includes modules for decomposition, selection, combination, and comparison.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If automated signal processing algorithms are used to detect fiducial points, then labor and costs are reduced, but detection accuracy deteriorates due to noise leading to high false positive and false negative rates
Solution Approach 1:
The signal processing is divided into multiple sequential stages: pre-processing with filtering, decomposition into subcomponents, identification of candidate regions, and verification. This multi-stage segmentation allows each stage to focus on specific aspects of signal analysis, improving overall detection accuracy while maintaining automated efficiency.
Solution Approach 2:
Candidate regions are introduced as an intermediary step between raw signal detection and final fiducial point identification. These candidate regions serve as intermediate structures that filter out false positives before final verification, acting as a mediator that improves detection precision without requiring manual review.
2Reliability
If signal processing is performed to extract information from noisy signals, then arrhythmia detection capability is improved, but false positive and false negative rates increase
Solution Approach 1:
The algorithm incorporates feedback mechanisms where detection results are verified against multiple criteria and previous detections. False positives are identified through consistency checks with signal morphology and temporal patterns, while false negatives are reduced by adaptive thresholding that learns from signal characteristics.
Solution Approach 2:
Pre-processing steps including filtering and candidate region identification are performed before final detection. This preliminary action prepares the signal by removing obvious noise and highlighting potential fiducial points, making the subsequent detection more reliable and reducing both false positives and false negatives.
3Measurement precision
If manual review is used to confirm automated analysis results, then detection accuracy is improved, but labor costs and review time increase
Solution Approach 1:
Instead of requiring full manual review of all signals, the system applies automated verification and filtering that handles the majority of cases. Manual review is reserved only for ambiguous cases that pass through multiple automated checks, achieving high accuracy while minimizing time loss.
4Reliability
If data transmission volume is increased to improve monitoring accuracy, then detection reliability is improved, but telecommunications costs and battery consumption increase
Solution Approach 1:
The system extracts and transmits only the essential fiducial point detection results and relevant signal features rather than transmitting complete raw signals. This extraction approach maintains monitoring accuracy by preserving critical information while dramatically reducing data transmission volume and associated energy consumption.
Data Source
AI summary
Various aspects are directed to identifying a region of interest in a physiological signal. As may be consistent with one or more embodiments, the physiological signal is decomposed into subcomponents, and a subset of the subcomponents is selected based upon overlap of spectral energy with expected spectral energy of the region of interest, in at least one of the subcomponents. At least two of the subcomponents in the subset are combined and compared to a threshold, with the comparison being used to identify the location of the region of interest.