Adaptive Reporting Intervals for Chronic Physiological Data Characterization
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Solution Overview
Problem
Current implantable medical devices (IMDs) face challenges in accurately characterizing temporal patterns of physiological data, particularly for conditions like atrial arrhythmia burden, due to inadequate reporting intervals, leading to inefficient resource usage and potential mischaracterization of patient conditions.
Innovation Solution
The IMD processes and reports data based on patterns of recurrence using techniques like fast Fourier transforms to determine dominant frequencies, allowing for adaptive reporting intervals and optimized memory allocation, and utilizes self-organizing maps to categorize data for improved diagnosis and therapy planning.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If data is reported at fixed scheduled intervals, then the system is simple to operate, but temporal patterns with periods longer than the reporting interval cannot be accurately characterized
Solution Approach 1:
The patent transforms the static fixed reporting interval into a dynamic adaptive reporting interval that adjusts based on the detected temporal patterns in the physiological data. The system analyzes the data to identify characteristic periods and modifies reporting intervals accordingly, allowing the system to remain simple to operate while accurately capturing temporal patterns of varying durations.
Solution Approach 2:
The system changes the reporting interval parameter based on the characteristics of the physiological data being monitored. By analyzing the temporal patterns and adjusting the reporting interval parameter to match the characteristic periods of the data, the system achieves both operational simplicity and measurement precision.
2Measurement precision
If reporting intervals are shortened to capture temporal patterns, then measurement precision improves, but resource consumption in the IMD increases
Solution Approach 1:
The system performs preliminary analysis of the physiological data to identify temporal patterns and characteristic periods before determining the optimal reporting interval. This preliminary action allows the system to set appropriate reporting intervals that capture necessary temporal patterns without unnecessarily shortening intervals, thereby reducing energy consumption while maintaining measurement precision.
Solution Approach 2:
The reporting interval is made dynamic rather than fixed, allowing the system to use longer intervals when temporal patterns are well-captured and shorter intervals only when necessary to capture critical patterns. This dynamic adjustment optimizes the balance between measurement precision and energy consumption.
3Measurement precision
If more memory is allocated to store temporal data, then data characterization accuracy improves, but device complexity and resource usage increase
Solution Approach 1:
The system extracts and stores only the essential temporal pattern characteristics rather than storing all raw temporal data. By identifying and storing key parameters such as characteristic periods, recurrence patterns, and dominant frequencies, the system achieves accurate data characterization without requiring extensive memory resources, thereby reducing device complexity.
Solution Approach 2:
Instead of storing complete temporal waveforms, the system creates simplified representations or copies that capture the essential temporal patterns. These compressed representations maintain the necessary diagnostic information while significantly reducing memory requirements and device complexity.
Data Source
AI summary
An implantable medical device (IMD) senses physiological episodes and stores data associated with the physiological episodes in the IMD. The data is then processed based on a pattern of recurrence of the physiological episodes.