Adaptive ECG Evaluation Using Additional Data for Waveform Classification
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Solution Overview
Problem
Existing electrocardiogram evaluation methods struggle with accurately distinguishing between normal and anomalous waveforms, often misclassifying normal electrocardiograms as disease-related and failing to differentiate between disease and noise.
Innovation Solution
An electrocardiogram evaluation method that acquires additional data based on electrocardiogram data to enhance the evaluation process, including determining the need for and acquiring additional data such as vital signs, past electrocardiograms, and remeasurements, and evaluating using machine learning models.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If machine learning models are used to automatically evaluate electrocardiograms, then evaluation efficiency is improved, but measurement precision deteriorates due to misclassification of normal and anomalous waveforms
Solution Approach 1:
The patent combines multiple data sources including electrocardiogram waveforms, vital signs data, and past medical history into a unified evaluation system. This integration allows the machine learning model to consider comprehensive information, reducing misclassification of normal versus anomalous electrocardiograms while maintaining automated evaluation efficiency.
Solution Approach 2:
The patent introduces an additional data acquisition mechanism as an intermediary between the electrocardiogram input and the machine learning model. This intermediary collects and processes supplementary information (vital signs, historical data) that mediates the evaluation process, improving diagnostic accuracy without compromising automation.
2Measurement precision
If additional data acquisition is performed to improve evaluation accuracy, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The patent implements a dynamic data acquisition strategy where the system adaptively determines whether to acquire additional data based on the characteristics of the input electrocardiogram. This dynamic approach allows the system to increase measurement precision only when necessary, avoiding unnecessary complexity in cases where the initial electrocardiogram data is sufficient for accurate evaluation.
Data Source
AI summary
An electrocardiogram evaluation apparatus of the present invention includes: an electrocardiogram acquiring unit that acquires electrocardiogram data measured from a person; an additional data acquiring unit that determines whether or not to acquire additional data to be used for evaluating the electrocardiogram data based on the electrocardiogram data, and acquires the additional data; and an evaluating unit that evaluates the electrocardiogram data based on the additional data. The electrocardiogram evaluation apparatus of the present invention can support decision-making by a medical professional, for example.