Hemodynamic Sensor Latent Space Endotype Classification
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Solution Overview
Problem
Current hemodynamic monitoring systems are inadequate in accurately determining the endotype of hypotension in patients, which is crucial for effective treatment and patient care.
Innovation Solution
A system that utilizes a hemodynamic sensor and a converter to obtain and process arterial pressure signal waveforms, employing a fully connected deep learning model to encode heart health parameters into latent space parameters, thereby determining the endotype of hypotension and generating alerts.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional hemodynamic monitoring systems are used, then basic heart health parameters can be monitored, but the ability to accurately determine hypotension endotypes is inadequate
Solution Approach 1:
The patent transforms hemodynamic data from traditional parameter space into latent space using a fully connected deep learning model. This dimensional transformation enables the system to capture complex non-linear relationships between hemodynamic variables and identify endotypes that are not discernible in traditional parameter space, thereby improving measurement precision without requiring proportionally more complex hardware
Solution Approach 2:
The patent replaces traditional mechanical signal processing methods with a fully connected deep learning model that processes hemodynamic data. This substitution allows for more sophisticated pattern recognition and endotype classification, achieving higher accuracy in determining hypotension endotypes while maintaining system manageability through software-based processing
2Measurement precision
If deep learning models are used to encode heart health parameters into latent space, then endotype determination accuracy improves, but processing time and computational requirements increase
Solution Approach 1:
The system performs preliminary actions by continuously monitoring hemodynamic parameters and pre-processing the data through the deep learning model's encoding function. This allows the system to maintain an updated latent space representation of the patient's hemodynamic state, enabling rapid endotype determination without requiring time-consuming batch processing for each new measurement
Solution Approach 2:
The fully connected deep learning model continuously refines its own encoding of hemodynamic parameters into latent space based on incoming data, allowing the system to adapt to individual patient patterns. This self-service approach enables the model to improve its efficiency over time and reduce processing requirements while maintaining high accuracy in endotype determination
Data Source
AI summary
A system for determining an endotype of hypotension of a patient can include a hemodynamic sensor and a converter. The system can receive, from the hemodynamic sensor, an analog hemodynamic sensor signal from the patient. The system can convert, using the converter, the analog hemodynamic sensor signal to an arterial pressure signal waveform and extract from the arterial pressure signal waveform a plurality of heart health parameters. Using a deep learning model, the system can encode the plurality of heart health parameters into one or more latent space heart health parameters. The system can generate a location in latent space of the arterial pressure signal waveform and determine a relative location of the arterial pressure signal waveform in latent space. Based on the relative location, the system can determine the endotype of hypotension of the patient and display an alert indicating the endotype of hypotension.