Adaptive Physiological Data Storage Under Implant Memory Limits
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Solution Overview
Problem
Traditional cardiac rhythm management (CRM) devices face limitations in data storage and power capacity, making it challenging to retain and present comprehensive physiological data, particularly for long-term monitoring and reprogramming of implantable medical devices.
Innovation Solution
The implementation of adaptive data storage systems within CRM devices that utilize a combination of data compression techniques and automated data downloading to external devices, allowing for efficient storage and prioritization of physiological data based on episode type, device response, and available memory space.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If data compression techniques are implemented to manage limited storage space, then storage capacity is improved, but data integrity may be compromised
Solution Approach 1:
The patent applies different compression techniques to different types of physiological data based on their importance and characteristics. Critical data such as arrhythmia episodes are stored with higher fidelity using lossless compression, while less critical continuous monitoring data uses lossy compression. This local differentiation allows the system to maximize storage capacity while preserving data integrity where it matters most.
Solution Approach 2:
The system dynamically adjusts compression parameters based on storage space availability, data priority, and device response. When storage space is limited, the system increases compression ratios for lower-priority data. The compression level is modified based on the type of physiological data, the amount of available memory, and the clinical significance of the episode being recorded.
2Quantity of substance
If automated data downloading is implemented to transfer data to external devices, then storage space management is improved, but device complexity increases
Solution Approach 1:
The implantable medical device automatically monitors its own storage capacity and initiates data transfer to external devices without requiring manual intervention. The system self-manages the downloading process by detecting when storage space is insufficient, selecting appropriate data for transfer, and establishing communication links with external devices. This automation reduces the burden on users while managing storage space efficiently.
Solution Approach 2:
The system continuously monitors storage capacity and device battery levels, using this feedback information to make intelligent decisions about data compression and transfer. When storage space becomes limited, the system automatically adjusts compression techniques and initiates data downloading. The feedback loop ensures the device adapts to changing conditions and maintains optimal storage management without requiring manual reconfiguration.
3Productivity
If multiple compression techniques are selected based on episode type and device response, then storage efficiency is improved, but system complexity increases
Solution Approach 1:
The patent implements a dynamic compression selection system that adapts to different clinical scenarios. The compression technique is not fixed but is dynamically chosen based on the type of arrhythmia detected, the device response (such as shock delivery), available storage space, and battery level. This dynamic adaptation allows the system to optimize storage efficiency for each specific situation while managing complexity through automated decision-making algorithms.
Solution Approach 2:
The system pre-establishes a hierarchy of compression techniques and their appropriate applications for different episode types. Before actual data capture, the system is programmed with knowledge of which compression methods are most suitable for specific arrhythmia types and device responses. This preliminary preparation allows the system to quickly select the appropriate compression technique during actual operation without requiring complex real-time calculations, thus improving storage efficiency while managing system complexity.
Data Source
AI summary
In an example, a medical device includes a physiological data monitor (PDM), a memory, and a processor. The PDM is configured to monitor a physiological data parameter. The memory circuit is configured to store data collected by the PDM. The processor is configured to detect a data capture event and capture a first segment of physiological data associated with the data capture event. The processor is also configured to determine an amount of memory storage space available and determine a first priority level for the first segment of physiological data. The processor is further configured to determine a second priority level for a second segment of physiological data stored previously and select a processing scheme using the first and second priority levels. Finally, the processor is configured to process, using the processing scheme, the first and second segments of physiological data and store the first segment of physiological data.