ECG Time-Frequency Modeling for Low-Cost Echocardiogram Parameter Estimation
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Solution Overview
Problem
Current heart testing methods, such as electrocardiograms (ECGs) fail to detect subtle abnormalities, leading to misdiagnosis, while echocardiograms, though accurate, are costly and reserved for suspected heart issues, necessitating a more cost-effective and efficient method to assess diastolic function.
Innovation Solution
Machine-learned computational models trained on ECG measurements, incorporating time-frequency features and patient demographics, estimate echocardiogram parameters to quantify diastolic function, providing a cost-effective alternative to echocardiograms.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If echocardiograms are used to accurately assess diastolic function, then diagnostic accuracy is improved, but cost increases
Solution Approach 1:
The patent creates a computational model that copies the diagnostic capability of echocardiograms by training machine learning algorithms on ECG data paired with echocardiogram measurements. The model learns to predict echocardiogram parameters (e′, a′, E, A waves) from ECG signals, effectively creating a virtual copy of the expensive test that can be performed using only the inexpensive ECG modality.
Solution Approach 2:
The patent replaces the expensive echocardiogram with a cheap ECG-based computational model. The ECG is a low-cost, widely available test that can be performed repeatedly without significant resource consumption. By training a machine learning model on ECG data, the system creates a disposable, low-cost diagnostic tool that can be applied to any patient with an ECG recording.
2Quantity of substance
If ECG is used for heart testing, then cost is reduced, but measurement precision of diastolic function deteriorates
Solution Approach 1:
The patent transforms the ECG signal by applying time-frequency transforms (such as wavelet transforms) to extract additional features beyond standard ECG parameters. This parameter transformation allows the machine learning model to capture subtle temporal and spectral patterns in the ECG that correlate with diastolic function, thereby improving measurement precision while maintaining the low cost of the ECG modality.
Solution Approach 2:
The patent replaces the mechanical ultrasound-based measurement system (echocardiogram) with a computational signal processing system that analyzes electrical signals (ECG). By substituting the physical ultrasound measurement mechanism with a machine learning-based computational approach, the system achieves comparable diagnostic accuracy without the associated costs and complexities of echocardiography.
3Measurement precision
If machine-learned computational models are applied to ECG data, then diagnostic accuracy is improved, but device complexity increases
Solution Approach 1:
The patent segments the diagnostic process into distinct computational stages: (1) time-frequency transform of the ECG signal to extract spectral features, (2) feature extraction to identify relevant parameters, and (3) machine learning model prediction to estimate echocardiogram parameters. This segmentation allows each component to be optimized independently and simplifies the overall system architecture, making the complex diagnostic capability more manageable and interpretable.
Data Source
AI summary
Machine-learned computational models can be trained to estimate echocardiogram parameters (as conventionally measured by echocardiography) from electrocardiograms and/or electrocardiogram-derived time-domain and/or time-frequency features. In some embodiments, a multi-level model architecture includes a level to derive the echocardiogram parameter estimate(s), with input features to that level being computed in a preceding level, and/or with one or more echocardiogram parameter estimate(s) flowing into a subsequent layer to compute downstream qualitative or quantitative indicators of heart function.