Heart Failure Assessment Program Using ECG Signal Analysis
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Solution Overview
Problem
Current methods for diagnosing heart failure are inadequate as they rely on clinical symptoms and echocardiograms, which may not detect left ventricular diastolic dysfunction without symptoms, necessitating a more convenient and accurate assessment method.
Innovation Solution
A method involving an ECG signal database processed using data preprocessing steps like offset correction and normalization, followed by feature analysis with a machine learning algorithm to establish a heart failure assessment program that determines the presence and severity of heart failure.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If heart failure diagnosis is based on clinical symptoms and echocardiograms, then diagnostic accuracy for symptomatic patients is improved, but detection capability for asymptomatic left ventricular diastolic dysfunction is insufficient
Solution Approach 1:
The patent segments the diagnostic process into multiple independent analysis components: ECG signal processing, machine learning model analysis, and risk score calculation. This segmentation allows the system to analyze ECG data independently of clinical symptoms, enabling detection of asymptomatic left ventricular diastolic dysfunction while maintaining diagnostic accuracy for symptomatic patients.
Solution Approach 2:
The patent introduces ECG signals as an intermediary marker that reflects underlying cardiac structural and functional changes before clinical symptoms appear. The machine learning model acts as a mediator that translates ECG signal patterns into diagnostic information about left ventricular diastolic dysfunction, bridging the gap between asymptomatic electrical changes and future clinical manifestations.
2Reliability
If traditional diagnostic methods using clinical symptoms and echocardiograms are used, then comprehensive assessment is achieved, but convenience and accessibility are reduced
Solution Approach 1:
The patent creates a computational model that copies and analyzes the diagnostic information already present in routine ECG signals. Instead of requiring additional echocardiogram procedures, the system extracts diagnostic features from the existing ECG data through machine learning, providing comprehensive assessment convenience through data replication and analysis rather than additional invasive testing.
Solution Approach 2:
The patent replaces the mechanical echocardiogram procedure with an automated machine learning analysis system that processes ECG signals. This substitution maintains diagnostic reliability by using algorithms trained to detect subtle patterns while significantly improving ease of operation, as ECG analysis can be performed automatically without requiring specialized echocardiography equipment or operator expertise.
3Measurement precision
If machine learning algorithms are applied to ECG signal analysis, then assessment accuracy for heart failure occurrence is improved, but computational complexity and data processing requirements increase
Solution Approach 1:
The patent performs preliminary actions by pre-processing ECG signals to extract relevant features before applying machine learning algorithms. This includes signal filtering, normalization, and feature extraction that prepare the data in advance, reducing the computational burden during the actual machine learning analysis phase while maintaining high assessment accuracy for heart failure occurrence.
Solution Approach 2:
The patent extracts only the most relevant features from ECG signals for machine learning analysis, rather than processing the entire raw signal. By taking out and focusing on specific diagnostic features identified through feature selection, the system improves assessment accuracy while reducing computational complexity and data processing requirements.
Data Source
AI summary
A method for assessing occurrence of heart failure includes the following steps. A heart failure assessment program established is provided. A target ECG signal data of the subject is provided, wherein the target ECG signal data includes a plurality of target heartbeat waveform data and a plurality of target heart rate data. A data pre-processing step is performed, wherein the target ECG signal data is pre-processed by the data processing module so as to obtain a processed target ECG signal data. An analyzing step is performed, wherein the processed target ECG signal data is analyzed by the heart failure assessment program so as to obtain a heart failure occurrence assessing result, and the heart failure occurrence assessing result presents a heart failure occurring condition and the severity of the heart failure of the subject.