ECG Reliability Assessment via Subthreshold Noise Filtering
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Solution Overview
Problem
The high rate of false positive abnormality results in electrocardiogram (ECG) tests for athletes during pre-participation exams leads to increased healthcare costs and unnecessary psychological trauma, primarily due to the time and cost associated with physician review of ECG results, with a significant portion of results being falsely identified as abnormal.
Innovation Solution
A method and system for assessing ECG reliability by analyzing trace noise and identifying subthreshold characteristics in ECG data to filter out true 'Normal' results, reducing the need for physician review and focusing it on irregular or abnormal results, using noise quantification and subthreshold criteria to differentiate between 'Normal' and 'Abnormal' readings.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If all ECG results are reviewed by physicians to ensure accuracy, then the reliability of ECG interpretation is improved, but the healthcare cost and time consumption increase significantly
Solution Approach 1:
The patent segments the ECG review process into two distinct stages: (1) automated machine interpretation that performs initial analysis of all ECG results, and (2) selective physician review that focuses only on cases where the automated system identifies abnormal findings or low-confidence results. This segmentation allows the system to maintain high reliability through targeted expert review while dramatically reducing the overall number of physician hours required, thereby lowering healthcare costs.
Solution Approach 2:
The patent introduces an automated machine interpretation system as an intermediary between ECG data acquisition and physician review. This intermediary performs preliminary analysis, filters out clearly normal cases, and flags only suspicious or abnormal results for physician evaluation. The machine acts as a triage mechanism that preserves physician expertise for cases where it is most needed while eliminating unnecessary reviews of benign cases.
2Reliability
If all ECG results are reviewed by physicians to ensure accuracy, then the reliability of ECG interpretation is improved, but the time consumption increases significantly
Solution Approach 1:
The patent segments the ECG review process into two distinct stages: (1) automated machine interpretation that performs initial analysis of all ECG results, and (2) selective physician review that focuses only on cases where the automated system identifies abnormal findings or low-confidence results. This segmentation allows the system to maintain high reliability through targeted expert review while dramatically reducing the overall number of physician hours required, thereby lowering healthcare costs.
Solution Approach 2:
The patent applies preliminary action by having the automated machine interpretation system perform initial filtering and analysis of all ECG results before physician review. The system pre-identifies normal cases that can be automatically cleared and pre-flagges only abnormal or uncertain cases for physician evaluation. This preliminary sorting action eliminates the need for physicians to review every single ECG, thereby preserving their time for cases that truly require expert assessment.
3Productivity
If automated interpretation is used to reduce physician review, then the productivity increases, but the measurement precision of ECG results may deteriorate
Solution Approach 1:
The patent introduces an automated machine interpretation system as an intermediary between ECG data acquisition and physician review. This intermediary performs preliminary analysis, filters out clearly normal cases, and flags only suspicious or abnormal results for physician evaluation. The machine acts as a triage mechanism that preserves physician expertise for cases where it is most needed while eliminating unnecessary reviews of benign cases.
Solution Approach 2:
The patent implements feedback mechanisms where physician reviews of flagged cases are fed back into the automated system to continuously refine and improve its interpretation algorithms. The system learns from physician corrections and adjustments, progressively improving its precision in detecting true abnormalities while maintaining high productivity. This feedback loop ensures that automated interpretation accuracy continues to improve over time.
Data Source
AI summary
The present technology is an automated method for determining whether a patient-specific electrocardiogram (ECG) is either (a) Normal and can be excluded from manual review or (b) Abnormal and included for manual review. In one embodiment, the method comprises comparing a plurality of characteristics of the ECG with predetermined subthreshold levels that are set less than clinically significant levels of abnormality of the characteristics, wherein the characteristics of the ECG are selected from the group including T-Wave inversion, ST-Depression, QT segment duration, delta wave character, anterior S-wave character and ectopic or pre-mature beats. The method continues by selecting the ECG for manual review if the plurality of selected characteristics exceed the predetermined subthreshold levels yet are below the corresponding clinically significant threshold levels of abnormality of the characteristics.