ECG Evaluation Using Z-Score Nomograms for Reliable Diagnosis
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Solution Overview
Problem
Current ECG machines lack reliable, automated, and reproducible methods for interpreting ECG variables, leading to inconsistent diagnoses and potential misdiagnosis of life-threatening heart conditions, especially in young individuals.
Innovation Solution
Development of a novel ECG evaluation system using Z-scores for over 170 ECG variables based on the largest historical cohort of more than 70,000 healthy subjects, integrated into an ECG machine with a computer interface and adaptive confirmatory enhancement module for accurate and reproducible diagnoses.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional normative ECG standards based on small cohorts are used, then the assessment method is simple, but the reliability and reproducibility of ECG interpretation are poor
Solution Approach 1:
The patent applies preliminary action by pre-establishing a large normative database (70,000+ healthy subjects) with Z-score calculations for over 170 ECG variables before actual ECG interpretation. This pre-computed reference framework enables consistent, reproducible assessment without requiring clinicians to manually reference small historical datasets, directly resolving the contradiction between reliability and complexity.
Solution Approach 2:
The patent transforms ECG variable assessment by introducing Z-scores as a new parameter system. Instead of using absolute values or traditional reference ranges from small cohorts, the system converts all ECG variables into Z-scores standardized against the large normative database. This parameter transformation enables objective, reproducible interpretation across different clinicians and settings, directly improving reliability while the automated calculation keeps complexity manageable.
2Productivity
If automated ECG interpretation is implemented, then productivity and consistency improve, but the system requires complex algorithms and large databases
Solution Approach 1:
The patent applies copying by creating a comprehensive digital replica of normative ECG data from 70,000+ healthy subjects. This digital database with pre-calculated Z-scores for over 170 variables serves as a reference copy that can be rapidly compared against patient ECGs. The copying approach enables automated interpretation without requiring complex real-time analysis algorithms, improving productivity while keeping the system complexity manageable through efficient data retrieval and comparison.
Solution Approach 2:
The system performs preliminary calculation of Z-scores and normative ranges for all 170+ ECG variables during database establishment. This pre-computation eliminates the need for complex real-time calculations during actual ECG interpretation, enabling rapid automated assessment. The preliminary action embedded in the database structure allows the automated system to achieve high productivity through simple lookup and comparison operations rather than complex real-time computation.
3Measurement precision
If Z-score based nomograms from large cohorts are implemented, then measurement precision and objectivity improve, but the database requirements and system complexity increase
Solution Approach 1:
The patent transforms raw ECG data into Z-score parameters standardized against the large cohort. This parameter change condenses the information from 70,000+ subjects into compact reference ranges and Z-score thresholds for each of the 170+ variables. The transformation maintains high measurement precision through statistical standardization while reducing the practical data burden to manageable reference tables and algorithms that can be implemented in clinical ECG machines.
Solution Approach 2:
The patent segments the large normative database into discrete, manageable components: over 170 specific ECG variables, each with its own Z-score distribution and reference ranges. This segmentation allows the system to handle the large cohort data in organized, variable-specific modules rather than as one monolithic dataset. Each variable can be independently assessed and updated, making the system scalable and manageable despite the large underlying data requirements.
Data Source
AI summary
Embodiments include an automated non-invasive method of assessment of an examined subject utilizing an electrocardiogram (ECG) system including: an electronic unit configured to connect to the examined subject, a memory unit configured to contain a database of Z-score-based nomograms of a first set of ECG variables from historic data of healthy individuals, a computer interface system, an adaptive confirmatory enhancement (ACE) module connected to various machine learning algorithms, an oversAIght module with artificial intelligence determining which algorithm to use, and a report generator, the method comprising: determining, by the computer interface system of the ECG system, a diagnosis of Kawasaki disease of the examined subject based on a determination that the digital ECG values of the second set of ECG variables of the examined subject are abnormal.