Single-Lead Heartbeat Waveform Processing for Atypical Myocardial Infarction Detection
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Solution Overview
Problem
Current methods for detecting myocardial infarction (heart attack) often rely on symptoms that can be atypical, especially in women and older adults, leading to delayed recognition and potential permanent damage or death due to lack of timely treatment.
Innovation Solution
A computer-implemented method that receives a single-lead heartbeat waveform, compares it to machine learning-generated waveform features, and provides a heart health indicator to determine if a user is experiencing a heart attack, using differential voltage potential measurements from electrodes on opposite limbs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional symptom-based detection methods are used, then the system is simple to operate, but the detection accuracy is low due to atypical symptoms in women and older adults
Solution Approach 1:
The patent introduces an intermediary processing layer between the raw ECG waveform and the diagnosis. Machine learning models process the waveform data, extracting meaningful features and patterns that are not directly observable. This intermediary layer enables accurate detection of atypical heart attack presentations without requiring complex manual analysis or multiple sensors.
Solution Approach 2:
The patent replaces traditional mechanical/symptom-based detection methods with an automated computational system. Instead of relying on users to recognize and report symptoms, the system automatically analyzes ECG waveforms using machine learning algorithms, substituting human symptom interpretation with automated pattern recognition.
2Loss of time
If rapid waveform analysis is implemented, then the response time is reduced for timely treatment, but the computational processing time increases
Solution Approach 1:
The patent performs preliminary actions by pre-training machine learning models on extensive ECG datasets before deployment. The models are pre-computed and optimized to rapidly analyze new waveforms. This preliminary training phase separates the heavy computational burden from the actual diagnosis time, enabling fast real-time analysis with minimal energy consumption during patient evaluation.
Solution Approach 2:
The system performs partial analysis by focusing on specific ECG waveform features and time segments most relevant to heart attack detection. Rather than analyzing the entire waveform in exhaustive detail, the machine learning model identifies and processes critical portions, reducing overall computational requirements while maintaining diagnostic accuracy.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
Enables rapid and accurate identification of heart attacks, potentially reducing mortality by providing timely medical attention, as it can analyze atypical symptoms and provide immediate indicators to users or third parties.
Implementation Method 1
The single-lead heartbeat waveform may be obtained via a differential voltage potential measurement concerning the heart of the user
Data Source
AI summary
A computer-implemented method, computer program product and computing system for receiving a single-lead heartbeat waveform for a user; comparing one or more portions of the single-lead heartbeat waveform to one or more ML-generated waveform features to associate a heart health indicator with the single-lead heartbeat waveform; and providing the heart health indicator to a recipient.