Adaptive Event Storage for Implantable Cardiac Monitors
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Solution Overview
Problem
Traditional cardiac rhythm management devices often fail to accurately record the onset of tachyarrhythmia episodes due to memory constraints, rendering the recorded data useless for physicians.
Innovation Solution
The implementation of an implantable system with multiple data buffers and additional trigger criteria, including a processor that monitors physiological data parameters to detect conditions predictive of a pathological episode, allowing for the capture and association of onset data with actual episode data, using a combination of static and rolling memory buffers.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional single buffer memory approach is used, then device complexity is reduced, but measurement precision of episode onset is lost
Solution Approach 1:
The memory system is divided into multiple specialized buffers: a first buffer for pre-episode data, a second buffer for post-episode data, and a third buffer for continuous monitoring. This segmentation allows each buffer to capture specific temporal segments of physiological data, enabling precise episode onset detection without requiring a single large complex memory structure.
Solution Approach 2:
The system continuously stores physiological data in the first buffer before an episode occurs, preparing the data in advance. When an episode is detected, the pre-stored data is already available for immediate analysis, eliminating the need to wait for post-episode data collection and ensuring accurate capture of the onset moment.
2Loss of information
If multiple data buffers are implemented, then information completeness is improved, but device complexity increases
Solution Approach 1:
Different buffers are assigned to store different types and time periods of physiological data. The first buffer stores pre-episode data, the second stores post-episode data, and the third provides continuous monitoring. This segmentation ensures comprehensive information retention while using simple, dedicated storage structures for each buffer type.
Solution Approach 2:
The system extracts and stores only the critical temporal segments of physiological data needed for episode analysis. By separating pre-episode, post-episode, and continuous data into different buffers, the system retains only the most relevant information without storing redundant complete continuous data streams.
3Measurement precision
If continuous data monitoring is performed, then measurement precision is improved, but energy consumption increases
Solution Approach 1:
The system uses periodic sampling of physiological parameters rather than continuous monitoring. Data is collected at regular intervals and stored in buffers, which reduces the frequency of sensor activation and data processing operations while still capturing sufficient information for accurate episode detection and analysis.
Solution Approach 2:
Physiological data is continuously stored in the first buffer during the pre-episode period at reduced sampling rates. This preliminary data collection prepares the system for potential episodes without requiring high-rate continuous monitoring, thereby reducing energy consumption while maintaining measurement precision when needed.
Data Source
AI summary
Monitoring physiological parameter using an implantable physiological monitor in order to detect a condition predictive of a possible future pathological episode and collecting additional physiological data associated with the condition predictive of a possible future pathological episode. Monitoring another physiological parameter in order to detect a condition indicative of the beginning of a present pathological episode and collecting additional pathological data in response to the condition. Determining that the condition predictive of a future episode and the condition indicative of a present episode are associated and, in response thereto, storing all the collected physiological data.