Neural Network Cardiac Waveform Analysis for Ischemia Detection
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Solution Overview
Problem
The analysis of complex echocardiogram waveforms for detecting myocardial ischemia is intensive, subjective, and prone to human error, as it relies on comparing limited parameters like peaks and crossover points, disregarding rich information in the waveforms and requiring skilled real-time evaluation.
Innovation Solution
A system and method using a neural network to probabilistically analyze entire cardiac waveforms, normalizing and processing data to generate metrics for classification as normal or abnormal, aided by a diagnostic interpretation module for accurate pathology identification.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If traditional functional analysis methods are used to evaluate echocardiogram waveforms, then the analysis can be performed with simple parameter comparison (peaks or crossover points), but only a small fraction of the data contained in the waveforms is considered, leading to loss of information
Solution Approach 1:
The patent extracts and analyzes the entire waveform data rather than just selected parameters. The neural network processes complete echocardiogram waveforms to capture all diagnostic information, eliminating the need to choose between simplicity and information completeness.
Solution Approach 2:
The patent transforms the analysis approach from examining discrete parameters (peaks, crossover points) to analyzing the continuous waveform signal itself. This parameter transformation enables comprehensive information utilization while maintaining computational feasibility through neural network processing.
2Reliability
If skilled physicians or technicians manually evaluate and compare echocardiogram waveforms in real-time, then diagnostic decisions can be made, but the evaluation is extremely intensive and prone to human error and subjectivity
Solution Approach 1:
The patent introduces a neural network as an intermediary between waveform acquisition and diagnostic interpretation. This intermediary automatically processes complex waveform comparisons, eliminating human subjectivity and error while handling the analytical complexity, thereby improving reliability without requiring manual expert evaluation.
Solution Approach 2:
The patent replaces the mechanical process of manual waveform evaluation by skilled personnel with an automated computational system. The neural network substitutes human cognitive processing, eliminating variability and fatigue-related errors while systematically analyzing waveform data.
3Measurement precision
If the entire functional waveform is analyzed instead of just peaks or crossover points, then diagnostic accuracy is improved, but the analysis becomes extremely intensive and time-consuming
Solution Approach 1:
The patent uses a neural network trained on representative waveform patterns to rapidly classify new waveforms. The network learns from training data and creates a computational model that can quickly evaluate entire waveforms without requiring intensive real-time processing, thus achieving both high accuracy and fast performance.
Solution Approach 2:
The patent performs preliminary training of the neural network using extensive waveform data before actual diagnostic use. This preliminary action pre-computes the complex analysis patterns, enabling rapid real-time classification of new waveforms with high accuracy without intensive processing during actual diagnosis.
Data Source
AI summary
A trainable, adaptable system for analyzing functional or structural clinical data can be used to identify a given pathology based on functional data. The system includes a signal processor that receives functional data from a device monitoring a subject and normalizes the functional data over at least one cycle of functional data. The system also includes a neural network having a plurality of weights selected based on predetermined data and receiving and processing the normalized functional data based on the plurality of weights to generate at least one metric indicating a degree of relation between the normalized functional data to the predetermined data. A diagnostic interpretation module is included for receiving the at least one metric from the neural network and classifying the functional data as indicative of the given pathology or not indicative of the given pathology based on a comparison of the at least one metric to at least one probability distribution of a likelihood of the given pathology.