ECG Analysis Support Device for Noisy Long-Term Waveforms
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Solution Overview
Problem
Existing electrocardiogram analysis systems struggle to accurately detect diseases over long periods due to waveforms not clearly appearing, and supervised learning models may miss or incorrectly identify sections with diseases, especially when noise and artifacts are present.
Innovation Solution
A support system that includes an electrocardiograph, electrocardiogram analyzer, and support device, which divides electrocardiograms into sections, allows user selection of non-analysis sections, and performs secondary analysis on candidate sections to enhance detection accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Duration of action of moving object
If long-time measurement is carried out to detect diseases, then the measurement duration is extended, but the waveform clarity deteriorates and disease signs do not clearly appear
Solution Approach 1:
The electrocardiogram is divided into multiple sections (e.g., 30-second intervals) to manage long-term measurement data. This segmentation allows the system to process and display manageable portions of the extended measurement while maintaining clarity, resolving the contradiction between long measurement duration and waveform clarity.
2Extent of automation
If supervised learning models are used for automatic disease detection, then automation is improved, but detection accuracy deteriorates when noise and artifacts are present
Solution Approach 1:
The system performs preliminary actions by automatically identifying and excluding sections with noise and artifacts before disease detection. This pre-processing step cleans the data, ensuring that subsequent supervised learning models analyze only high-quality segments, thereby maintaining high detection accuracy while preserving automation.
Solution Approach 2:
The system introduces an intermediary layer between the raw electrocardiogram data and the disease detection model. This intermediary component (the section classification and exclusion mechanism) filters out noise and artifacts, acting as a mediator that protects the detection accuracy from degrading due to poor quality data.
3Reliability
If all sections are analyzed for disease detection, then comprehensive coverage is improved, but analysis time increases and efficiency decreases
Solution Approach 1:
The system extracts and removes sections with noise and artifacts from the analysis pool. By taking out these unusable sections, the system maintains comprehensive disease detection coverage through the remaining high-quality sections while significantly reducing the total analysis time and improving efficiency.
Data Source
AI summary
The support system includes an electrocardiograph, an electrocardiogram analyzer, and a support device. The electrocardiograph is configured to acquire an electrocardiogram of a subject. The electrocardiogram analyzer is configured to receive the electrocardiogram, to divide the electrocardiogram into a plurality of sections, and to extract the sections other than specified sections from the plurality of sections as candidate sections. The support device is configured to be communicatively connected to the electrocardiogram analyzer. The support device includes a computing device and a display device controlled by the computing device. The functions of the computing device are described in the specification in detail.