ECG Diagnosis Model Learning Using Attention Intervals
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Solution Overview
Problem
Current disease diagnosis based on electrocardiogram waveforms may overlook important changes outside the predetermined waveform parts, leading to inaccurate diagnoses.
Innovation Solution
A learning device and method that acquire partial waveforms and attention intervals from electrocardiogram data to train models for disease diagnosis, integrating regular partial waveforms identified by attention intervals to improve diagnostic accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If a doctor or computer determines disease existence by observing predetermined waveform parts based on experimental knowledge, then the diagnosis process is simplified and can be performed efficiently, but important changes in other waveform parts may be overlooked leading to inaccurate diagnosis
Solution Approach 1:
The patent segments the electrocardiogram waveform into multiple partial waveforms (P wave, QRS complex, T wave, U wave) and applies different attention intervals to each segment. This allows the system to focus on disease-specific waveform parts while still monitoring other segments, thereby maintaining diagnostic simplicity while improving accuracy by preventing overlooked changes in non-predetermined waveform parts.
2Productivity
If only predetermined waveform parts are monitored for disease diagnosis, then the diagnostic process remains simple and efficient, but the reliability of diagnosis decreases due to potential overlooking of important changes in other waveform parts
Solution Approach 1:
The patent applies different attention intervals to different partial waveforms based on their specific diagnostic importance for each disease type. For example, longer attention intervals are applied to P waves when diagnosing atrial fibrillation, while shorter intervals are used for QRS complexes. This local differentiation maintains processing efficiency for critical segments while ensuring comprehensive monitoring of all waveform parts, thereby improving diagnostic reliability without sacrificing efficiency.
Data Source
AI summary
A learning device 1X mainly includes a first acquisition means 31X, a second acquisition means 32X, and a learning means 34X. The first acquisition means 31X acquires a partial waveform of electrocardiogram data regarding an electrocardiogram of a subject. The second acquisition means 32X acquires an attention interval, which is used as a basis for a diagnosis of a target disease, in a sequential waveform of the electrocardiogram data. The learning means 34X trains a model configured to diagnose the target disease, based on the partial waveform and the attention interval.