Cardiac Risk Stratification via HRT Measurements
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Solution Overview
Problem
Current medical devices lack an effective method to stratify cardiac risk based on physiological parameters, particularly heart rate turbulence (HRT), which is crucial for predicting cardiac arrhythmia and mortality, and determining the need for implantable therapy devices.
Innovation Solution
An implantable medical device (IMD) or external computing device generates a risk stratification indicator by computing HRT measurements from cardiac signals, including turbulence onset and slope, and compares these over time to determine the level of autonomic function recovery, thereby classifying patients into risk categories for cardiac arrhythmia or mortality.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If HRT measurements are computed and analyzed over time, then cardiac risk stratification accuracy is improved, but device complexity and computational requirements increase
Solution Approach 1:
The patent performs preliminary computation of HRT measurements (turbulence onset and slope) from cardiac signals and stores these computed values in the device memory. This preliminary action allows the device to prepare risk assessment data in advance, improving measurement precision while managing computational load by performing calculations during routine monitoring rather than on-demand.
Solution Approach 2:
The patent introduces an intermediary processing layer that computes HRT measurements from raw cardiac signals and stores them as intermediate results. This intermediary layer (the computed HRT values) mediates between the raw physiological data and the final risk stratification output, enabling accurate risk assessment without requiring the device to perform complex real-time analysis during clinical decision-making.
2Measurement precision
If multiple physiological parameters are monitored and analyzed, then patient risk classification accuracy is improved, but data processing time and computational resources increase
Solution Approach 1:
The patent computes and stores HRT measurements (turbulence onset and slope) as preliminary processed data during routine device operation. By performing this computation in advance and storing the results, the system prepares multiple physiological parameters for analysis without requiring intensive real-time processing when risk assessment is needed, thus reducing data processing time while maintaining classification accuracy.
3Measurement precision
If HRT measurements are stored and compared over time periods, then autonomic function recovery assessment is improved, but memory requirements and device complexity increase
Solution Approach 1:
The patent extracts and stores only the essential HRT measurement parameters (turbulence onset and slope values) from the complete cardiac signal data. By taking out only these critical computed values for storage and comparison over time, the system achieves accurate autonomic function recovery assessment while minimizing memory requirements, avoiding the need to store entire raw signal datasets.
Data Source
AI summary
This disclosure describes techniques for generating a risk stratification indicator based on HRT measurements computed using physiological parameters sensed by an implantable medical device (IMD). In some examples, the HRT measurements may be computed by the IMD based on the physiological parameters. In other examples, the IMD may sense the physiological parameters, and transmit data representative of the parameters to an external computing device, such as an IMD programmer, which then computes the HRT measurements. Exemplary physiological parameters include cardiac signals.