Physiological Variable Estimation in Implantable Devices
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Solution Overview
Problem
Implantable medical devices (IMDs) face limitations in computing therapy control parameters due to memory and processing power constraints, especially when dealing with long-term measurements of physiological variables, which require complex computational methods and significant resources.
Innovation Solution
The implementation of a method that uses a hierarchy of computationally simpler metrics to estimate physiological variables like myocardial action potential duration (APD) based on heart rate history, reducing memory and processing demands by storing and updating long-term metrics, allowing for efficient computation of therapy control parameters on demand.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If complex computational methods are used to compute physiological variables from long-term signal measurements, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The patent segments the computational process into distinct phases: signal acquisition over long periods, intermediate processing to extract key features, and final physiological variable computation. This segmentation allows complex computations to be broken down into manageable steps that can be executed within IMD constraints while maintaining accuracy.
Solution Approach 2:
The patent performs preliminary processing of raw signals to extract essential features and store them in an organized manner before final physiological variable computation. By pre-processing and organizing data during the signal acquisition phase, the system reduces the computational burden during therapy delivery while preserving measurement precision.
2Measurement precision
If long-term signal data is stored for computing physiological variables, then measurement precision is improved, but quantity of substance increases
Solution Approach 1:
The patent extracts only the essential features and key characteristics from long-term signal data rather than storing complete raw signals. By taking out only the necessary information needed for physiological variable computation, the system maintains measurement precision while significantly reducing memory storage requirements to fit within IMD constraints.
Solution Approach 2:
Instead of storing all raw signals and processing them later, the patent inverts the approach by processing signals continuously during acquisition and storing only the processed results and essential features. This inversion dramatically reduces storage requirements while preserving the information needed for accurate physiological variable estimation.
3Measurement precision
If complex computational methods are used for therapy control parameter computation, then measurement precision is improved, but productivity decreases
Solution Approach 1:
The patent performs preliminary computation and organization of signal data during periods when therapy is not immediately needed. By pre-computing intermediate results and organizing data structures in advance, the system enables rapid final computations when therapy control parameters are needed, thus maintaining both precision and productivity.
Solution Approach 2:
The patent implements dynamic computation strategies that adapt to real-time needs. When therapy delivery is urgent, the system uses pre-computed data and simplified algorithms for rapid response. When time is available, more comprehensive processing is performed to enhance precision. This dynamic approach balances processing speed and accuracy based on clinical needs.
Data Source
AI summary
A medical device performs a method for computing an estimate of a physiological variable. The method includes sensing a physiological signal and measuring an event of the physiological signal. The device initializes a value of a long-term metric of the event measurement, wherein the long-term metric corresponds to a time interval correlated to a response time of the physiological variable to changes in the event. The estimate of the long-term metric is updated in a memory of the medical device using a previous long-term metric and a current measurement of the event. The device detects a need for computing the physiological variable and computes an estimate of the physiological variable using the updated long-term metric.