ECG Waveform Segmentation for Atrial Fibrillation Probability Indexing
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Solution Overview
Problem
Current technologies for analyzing electrocardiographic waveforms lack the ability to provide detailed information on whether specific waveform portions indicate heart disease, particularly atrial fibrillation, with limited efficiency in confirming predictions and improving prediction accuracy.
Innovation Solution
A processing device that receives electrocardiographic waveform data, divides it into partial waveforms, calculates the probability of heart disease presence in each segment, and displays an index corresponding to the probability, along with a method for generating training data and a prediction model using machine learning to enhance prediction accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If the electrocardiographic waveform is analyzed as a whole, then the analysis is simple, but detailed information on specific waveform portions is lost
Solution Approach 1:
The electrocardiographic waveform is divided into multiple partial waveforms corresponding to different heartbeat cycles. This segmentation allows detailed analysis of each individual waveform portion while maintaining manageable complexity through automated processing of discrete segments.
2Measurement precision
If probability calculation is performed on each partial waveform, then detailed detection is achieved, but processing time increases
Solution Approach 1:
Beat information such as R-R intervals is extracted and prepared in advance from the electrocardiographic waveform. This preliminary action enables subsequent probability calculations to be performed more efficiently on each partial waveform without redundant processing.
Solution Approach 2:
The system calculates probability values for each partial waveform and provides visual feedback through indexed display. This feedback mechanism allows operators to quickly identify areas of interest for further review, reducing overall processing time by focusing detailed analysis only where needed.
3Reliability
If a prediction model is trained with beat information, then prediction accuracy improves, but data processing complexity increases
Solution Approach 1:
The prediction model is trained using multiple types of data including electrocardiographic waveform data and beat information such as R-R intervals. This multi-functional approach allows the same model to leverage diverse data sources for improved accuracy without requiring separate processing systems for each data type.
Data Source
AI summary
A processing device includes an interface configured to receive electrocardiographic waveform data corresponding to an electrocardiographic waveform of a subject, and a processor configured to divide the electrocardiographic waveform into a plurality of partial electrocardiographic waveforms, based on the electrocardiographic waveform data, calculate a probability that a waveform portion in which a heart disease is suspected is included in each of the plurality of partial electrocardiographic waveforms, and cause a display to display an index together with information, the index corresponding to the probability, the information corresponding to the partial electrocardiographic waveform from which the probability is calculated.