Cardiac Event Detection via Arterial Pressure Waveform Analysis
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Solution Overview
Problem
Current methods for detecting myocardial infarction and ischemia are invasive, costly, and time-consuming, lacking specificity and requiring trained operators, while traditional techniques for determining infarct size are limited by radiation exposure, expert requirements, and high costs.
Innovation Solution
A machine learning model configured to detect cardiac events and determine myocardial infarction size using intrinsic frequency analysis of arterial pressure waveforms, processed by client devices and computing systems, enabling non-invasive, instantaneous, and cost-effective assessments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional diagnostic techniques (echocardiography, MRI, CT, SPECT, PET) are used to determine infarct size, then measurement precision is improved, but device complexity and cost increase significantly
Solution Approach 1:
The patent replaces complex mechanical imaging systems (echocardiography, MRI, CT) with a simplified acoustic-based intrinsic frequency analysis system. By analyzing natural cardiac acoustic signals and their frequency characteristics, the system determines infarct size without requiring sophisticated imaging hardware, thereby reducing device complexity while maintaining measurement capability
Solution Approach 2:
The patent creates a simplified model of cardiac function by analyzing intrinsic frequencies of acoustic signals rather than directly imaging the heart structure. This computational model copies the essential functional information needed for infarct size determination from complex imaging data, allowing accurate measurement without the need for complex imaging devices
2Measurement precision
If advanced imaging techniques (MRI, CT, SPECT, PET) are employed, then measurement precision improves, but loss of time increases due to data acquisition and analysis duration
Solution Approach 1:
The patent performs preliminary analysis of intrinsic frequencies from readily available acoustic signals (such as ECG or phonocardiogram data) that can be obtained instantly during patient presentation. By having the computational model ready to process these signals immediately, the system eliminates the time delay associated with scheduling and performing complex imaging procedures, enabling rapid infarct size determination at the point of care
Solution Approach 2:
The patent extracts the essential diagnostic information (intrinsic frequencies) from readily available cardiac acoustic signals, separating the critical measurement data from the need for time-consuming imaging procedures. This extraction approach obtains the necessary information instantly from existing signals without requiring additional time for data acquisition through complex imaging
3Ease of operation
If ECG is used for detection, then ease of operation is improved, but measurement precision deteriorates due to lack of specificity
Solution Approach 1:
The patent transforms the static ECG waveform into a dynamic frequency analysis by calculating intrinsic frequencies that change with cardiac conditions. This dynamic approach allows the system to maintain the ease of ECG operation while significantly improving measurement precision through frequency-based detection of ischemic changes that are not visible in standard ECG interpretations
4Measurement precision
If trained operators are required for diagnosis, then measurement precision improves, but ease of operation deteriorates
Solution Approach 1:
The patent implements a self-service diagnostic system where the computational model automatically analyzes intrinsic frequencies and provides diagnostic results without requiring trained operators. The system performs self-diagnosis by processing cardiac acoustic signals and generating infarct size measurements and cardiac event detections autonomously, eliminating the need for specialized operator training while maintaining high measurement precision through algorithmic analysis
Data Source
AI summary
The present application relates to using noninvasive techniques to determine whether a patient has experienced a cardiac event. The present application also relates to using noninvasive techniques to determine a size of a myocardial infarction experienced by a patient. In some embodiments, arterial pressure waveforms may be obtained, and from the arterial pressure waveform, a set of cardiac parameters may be extracted. The extracted cardiac parameters may be provided, as input, to the trained machine learning model, which may output a result indicating whether the patient experienced a cardiac event, a size of a myocardial infarction experience by a patient, or other information about the patient's cardiac health.