Ventricular Arrhythmia Detection Using Hemodynamic Waveform Segmentation
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Solution Overview
Problem
Current methods for detecting ventricular arrhythmia, such as ventricular fibrillation and myocardial infarction, are limited by their inability to provide early and accurate characterization of cardiac disorders, often requiring extensive clinical knowledge and invasive procedures, and struggle in noisy environments, leading to potential delays in cardiac rhythm management.
Innovation Solution
A system that processes SPO2 oximetric signal waveform data to determine specific parameters like amplitude and timing variations, synchronizing with ECG and blood pressure signals to generate alerts and provide early detection and characterization of ventricular arrhythmias, using non-invasive sensors and an artificial neural network for improved accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If ECG and ICEG signals are used to detect ventricular arrhythmia, then electrophysiological data can be analyzed, but detection occurs relatively late due to ventricular function variation affecting blood contraction and hemodynamic characteristics first
Solution Approach 1:
The patent applies preliminary action by analyzing hemodynamic parameters (blood pressure waveform morphology, pulse pressure, stroke volume) that change before electrophysiological abnormalities become evident in ECG signals. This allows early detection of ventricular arrhythmia by monitoring hemodynamic variations that precede electrical changes, enabling intervention before the condition progresses to life-threatening arrhythmia
2Measurement precision
If invasive methods such as ICEG signals are used, then better diagnostic results can be obtained, but patient risk increases
Solution Approach 1:
The patent uses non-invasive blood pressure monitoring as an intermediary to indirectly assess ventricular function and detect arrhythmia. Instead of directly measuring intracardiac electrical signals (ICEG), the system analyzes hemodynamic parameters from peripheral blood pressure waveforms, which serve as a mediator reflecting cardiac function without requiring invasive catheter placement, thus reducing patient risk while maintaining diagnostic capability
3Object-affected harmful factors
If non-invasive blood pressure monitoring is used, then patient safety is improved, but the ability to differentiate arrhythmia type and categorize severity is reduced
Solution Approach 1:
The patent applies segmentation by dividing the blood pressure waveform into distinct morphological components (upstroke, peak, dicrotic notch, downstroke) and analyzing specific parameters within each segment (rise time, peak pressure, area under curve). This detailed segmentation of the waveform allows differentiation of arrhythmia types and severity categorization by identifying characteristic patterns in different portions of the hemodynamic cycle, compensating for the non-invasive approach
4Productivity
If known ventricular arrhythmia analysis methods are used, then treatment can be provided, but the system does not operate well in noisy environments since ventricular activities may be buried in noise and artifacts
Solution Approach 1:
The patent implements feedback by continuously monitoring blood pressure waveform parameters and comparing them against established norms and patient-specific baselines. The system uses adaptive thresholding and trend analysis that adjusts to varying signal conditions, providing feedback-driven detection that maintains reliability in noisy environments by recognizing patterns over time rather than relying on single-point measurements, thus enabling accurate arrhythmia detection even when signals are contaminated with artifacts
Data Source
AI summary
A system for heart performance characterization and abnormality detection detects peaks and at least one of, a valley and a baseline comprising a substantially zero voltage level, of received signal data representing oxygen content of blood in a patient vessel over multiple heart beat cycles. The signal processor determines signal parameters including at least one of, (a) a signal amplitude magnitude between a maximum peak and minimum valley, of the received signal data, (b) a signal amplitude magnitude between a maximum peak and a baseline, of the received signal data and (c) a signal amplitude magnitude between a second highest maximum peak and minimum valley, of the received signal data. The system compares a determined signal parameter or value derived from the determined signal parameter, with a threshold value and generates an alert message associated with the threshold, in response to the comparison.