ECG Heart State Estimation Using Myocardial Activity Parameters
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Solution Overview
Problem
The waveform of an electrocardiogram alone is insufficient for accurately grasping the state of the heart, as similar waveforms can mask differences in heart disease onset, and obtaining additional information through techniques like blood collection is impractical in daily life.
Innovation Solution
A learning device and method that processes electrocardiogram waveforms by setting specific time sections, applying cumulative distribution functions, and generating myocardial activity parameters to estimate heart states using an estimation model trained on heart activity data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If only electrocardiogram waveform is used to grasp heart state, then ease of operation is improved, but measurement precision deteriorates
Solution Approach 1:
The patent segments the electrocardiogram waveform into distinct time sections (R wave section and T wave section) and extracts specific features from each segment. By dividing the waveform into meaningful segments and analyzing them separately, the system achieves more precise heart state assessment without requiring additional invasive measurements, thus resolving the contradiction between ease of operation and measurement precision.
Solution Approach 2:
The patent transforms the one-dimensional waveform data into multi-dimensional feature space by extracting multiple parameters (amplitude, duration, slope, area) from different time sections and combining them with distribution characteristics. This dimensional transformation enables precise heart state differentiation using only ECG waveform data, maintaining ease of operation while improving measurement precision.
2Measurement precision
If additional information techniques (e.g., blood collection) are used to increase accuracy, then measurement precision is improved, but device complexity and ease of operation worsen
Solution Approach 1:
The patent extracts meaningful features and distribution characteristics directly from the electrocardiogram waveform data itself, without requiring external additional measurements. By taking out and analyzing specific waveform segments and their statistical properties, the system achieves high measurement precision while avoiding the device complexity and invasive procedures that would otherwise be needed.
Solution Approach 2:
The patent introduces distribution characteristics (cumulative distribution functions, probability density functions) as intermediary representations that bridge the raw waveform data and heart state assessment. This intermediary transformation enables precise heart state differentiation through mathematical modeling of waveform patterns, eliminating the need for additional physical measurements and reducing device complexity.
3Measurement precision
If additional information techniques (e.g., blood collection) are used to increase accuracy, then measurement precision is improved, but ease of operation worsens
Solution Approach 1:
The patent enables the electrocardiogram waveform data to serve itself by extracting sufficient diagnostic information directly from the waveform without requiring external additional measurements. The system uses self-contained waveform analysis with distribution characteristics to achieve precise heart state assessment, making the process as easy as obtaining a standard ECG while improving measurement precision.
Data Source
AI summary
A learning device includes a learning unit that sets, as a myocardial activity parameter set, a set of parameters representing an approximate function when a waveform in a time section included in a waveform for one cycle indicating a cardiac cycle of a heart is approximated by an approximate function including a difference or a weighted difference of a cumulative distribution function, and performs learning of an estimation model that obtains heart state information that is information representing a state of a heart corresponding to the myocardial activity parameter with a myocardial activity parameter set as an input, by using a learning data set, in which the learning data set includes a plurality of (Y) pieces of learning data, and each of the plurality of (Y) pieces of learning data includes a myocardial activity parameter set of a heart that is a target of y-th learning data, and heart state information that is information representing a state of the heart that is a target of the y-th learning data, where each of integers greater than or equal to 1 and less than or equal to Y is y.