ECG and ABP Waveform Analysis for Hemodynamic Instability Warning
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Solution Overview
Problem
Existing methods for predicting hemodynamic instability in critically ill patients in ICU settings have limitations, with AUROCs ranging from 0.82 to 0.92, and there is a need for improved predictive models that can utilize electrocardiogram (ECG) and arterial blood pressure (ABP) waveforms to enhance accuracy.
Innovation Solution
A system that includes ECG and ABP monitors, a workstation, and artificial intelligence to process and analyze ECG and ABP waveforms, extracting features and applying trained AI to predict hemodynamic instability, providing early warnings.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If nurse-charted vital signs and clinical measurements are used to predict hemodynamic instability, then the prediction can be made with existing data, but the AUROC is limited to 0.82-0.92
Solution Approach 1:
The patent introduces ECG and ABP waveforms as intermediary data sources that bridge the gap between basic vital signs and hemodynamic status. These waveforms serve as mediators that contain richer information about cardiac function and hemodynamic state, enabling more accurate predictions without replacing existing monitoring systems.
Solution Approach 2:
The patent transitions from traditional one-dimensional vital sign monitoring (heart rate, blood pressure values) to multi-dimensional waveform analysis. By incorporating temporal patterns, morphological features, and dynamic characteristics from ECG and ABP waveforms, the system adds new dimensions of information that significantly improve prediction accuracy.
2Measurement precision
If ECG and ABP waveforms are processed and analyzed to extract features, then prediction accuracy improves by 5% in AUROC and 9% in AUPRC, but the system complexity increases
Solution Approach 1:
The patent segments the waveform analysis process into distinct functional components: ECG waveform processing module, ABP waveform processing module, feature extraction module, and prediction module. This segmentation allows complex waveform analysis to be broken down into manageable tasks, improving computational efficiency and reducing overall system complexity.
Solution Approach 2:
The patent extracts relevant features from ECG and ABP waveforms (such as heart rate variability, waveform morphology parameters, and temporal patterns) and separates these features from the raw waveform data. This extraction process simplifies the input to the prediction model, reducing computational burden while maintaining prediction accuracy.
3Measurement precision
If waveform features are extracted and fed into trained AI models, then hemodynamic instability can be predicted with higher accuracy, but the computational resources and processing time increase
Solution Approach 1:
The patent performs preliminary feature extraction and transformation of ECG and ABP waveforms before feeding data to the AI prediction model. By pre-processing the waveforms to extract meaningful features (such as temporal patterns, morphological characteristics, and statistical parameters), the system reduces the computational burden on the AI model during prediction, as it only needs to process the extracted features rather than raw waveforms.
Data Source
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AI summary
A controller (150) for waveform-based hemodynamic instability warning includes a memory (151) that stores instructions and a processor (152) that executes the instructions. The controller (150) implements a process that includes receiving, via a first interface (153) that interfaces at least one electrocardiogram monitor (155) monitoring a patient, electrocardiogram waves; identifying (880) heart beats from the electrocardiogram waves; separating the plurality of heart beats into first temporal windows; extracting features of the heart beats in each of the first temporal windows as first extracted features for each first temporal window; generating, based on the first extracted features, generated features across a second temporal window that includes a plurality of the first temporal windows; applying trained artificial intelligence to the generated features; predicting hemodynamic instability for the patient based on applying the trained artificial intelligence to the generated features, and outputting an alert warning of the hemodynamic instability based on predicting the hemodynamic instability.