Infant Congenital Heart Disease Prediction via ECG Wavelet Analysis
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Solution Overview
Problem
Current methods for diagnosing significant congenital heart disease in infants are limited, as cardiac ultrasound requires experienced operators and expensive equipment, and blood oxygen concentration testing cannot detect non-cyanotic congenital heart disease that may cause cardiac failure.
Innovation Solution
A prediction system that includes a processor and storage device, capable of converting electrocardiogram data from an XML format to a CSV format, performing continuous wavelet transformation, oversampling, and using transfer learning through pre-trained models to establish a significant congenital heart disease model.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If cardiac ultrasound is used for diagnosis, then detection accuracy is improved, but device complexity and cost increase
Solution Approach 1:
The patent replaces the mechanical cardiac ultrasound system with an electrocardiogram-based computational system. Instead of using complex ultrasound hardware requiring experienced operators, the invention uses standard ECG recordings processed through wavelet transformation and machine learning models to detect congenital heart diseases, thereby substituting mechanical/diagnostic equipment with a computational approach.
Solution Approach 2:
The patent creates a computational model that copies and simulates the diagnostic capabilities of cardiac ultrasound using readily available ECG data. By training machine learning models on ECG signals, the system replicates the detection functionality of ultrasound without requiring the actual ultrasound equipment, making the diagnostic capability accessible through simpler, more widely available tools.
2Ease of manufacture
If blood oxygen concentration testing is used, then cost is reduced and operation is simplified, but detection precision deteriorates
Solution Approach 1:
The patent replaces the blood oxygen concentration testing method with an ECG-based computational system. Instead of relying on blood oxygen measurements that miss non-cyanotic cases, the invention uses electrical heart activity recordings processed through advanced signal analysis and machine learning to detect all types of significant congenital heart diseases, including those that do not cause cyanosis.
Solution Approach 2:
The patent changes the measurement parameter from blood oxygen concentration to electrocardiogram signal characteristics. By analyzing temporal and frequency domain features of ECG signals through wavelet transformation, the system detects hemodynamic influences of congenital heart diseases that are not visible through oxygen saturation measurements, thereby improving detection accuracy while maintaining operational simplicity.
3Ease of operation
If traditional electrocardiogram analysis is used, then ease of operation is maintained, but detection precision deteriorates
Solution Approach 1:
The patent enhances traditional ECG analysis by substituting manual interpretation with automated computational processing. The system applies continuous wavelet transformation and machine learning algorithms to ECG signals, automatically detecting patterns indicative of congenital heart diseases without requiring expert operators, thereby maintaining ease of operation while dramatically improving detection precision.
Solution Approach 2:
The patent introduces computational intermediaries (wavelet transformation algorithms and machine learning models) between the raw ECG signal and the diagnostic conclusion. These intermediaries process the electrical heart activity data to extract subtle features related to chamber dilation and axis changes, serving as a bridge that enhances the information content of simple ECG recordings without adding operational complexity.
Data Source
AI summary
The present disclosure provides an operation method of a prediction system of significant congenital heart disease in infants in infants, which includes steps as follows. The continuous wavelet transformation is performed on the electrocardiogram to obtain the processed electrocardiogram; the processed electrocardiogram is oversampled to obtain multiple electrocardiogram segments; the transfer learning through multiple pre-trained models based on the multiple electrocardiogram segments is used to establish a significant congenital heart disease model.