Wearable ECG Monitor With Self-Optimizing Compression for P-Wave Capture
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Solution Overview
Problem
Current ECG monitoring systems are inadequate for long-term, self-sustained, and user-friendly monitoring of cardiac rhythm disorders, particularly in capturing low-amplitude P-wave signals due to electrode placement issues, discomfort, and inefficiencies in data compression.
Innovation Solution
A lightweight, wearable electrocardiography monitor with a flexible extended wear electrode patch and a reusable recorder that optimizes P-wave capture by positioning electrodes along the sternal midline, featuring a self-adjusting compression algorithm to ensure accurate and comfortable long-term monitoring.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Duration of action of moving object
If conventional ECG monitoring systems are used for long-term monitoring, then monitoring duration can be extended, but measurement precision of P-wave signals deteriorates due to electrode placement issues and discomfort
Solution Approach 1:
The system dynamically adjusts the compression algorithm parameters based on the detected P-wave signal characteristics. The compression ratio and filtering parameters are modified in real-time to maintain optimal P-wave detection precision throughout extended monitoring periods, resolving the contradiction between long-term operation and signal quality maintenance.
Solution Approach 2:
The patent changes the operational parameters of the compression algorithm during monitoring to adapt to varying signal conditions. By adjusting compression thresholds and algorithm selection based on detected P-wave morphology, the system maintains measurement precision while enabling extended monitoring duration.
2Duration of action of moving object
If data compression is applied to reduce storage needs, then loss of information increases, but monitoring duration can be extended
Solution Approach 1:
The system dynamically adjusts compression parameters based on the detected P-wave signal characteristics. When P-waves are detected, the compression algorithm uses more conservative thresholds to preserve diagnostic information; when P-waves are absent, higher compression ratios are applied, optimizing the balance between storage efficiency and information retention.
Solution Approach 2:
The system incorporates feedback mechanisms where the detected ECG signal quality and P-wave presence inform the compression algorithm selection. This feedback loop ensures that compression is applied selectively rather than uniformly, preserving critical diagnostic information while enabling extended monitoring.
3Device complexity
If fixed compression algorithms are used, then device complexity is reduced, but adaptability to varying cardiac conditions deteriorates
Solution Approach 1:
The system implements dynamic algorithm selection where multiple compression algorithms are available but the specific algorithm and its parameters are adjusted in real-time based on the detected cardiac rhythm and P-wave characteristics. This dynamic adaptation provides versatility for different cardiac conditions while maintaining manageable device complexity through automated selection.
Solution Approach 2:
The compression system performs self-optimization by automatically selecting and adjusting algorithm parameters based on the incoming ECG signal characteristics without requiring external intervention. The system self-adapts to varying cardiac conditions, providing versatility while keeping the user interface and device operation simple.
Data Source
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AI summary
An electrocardiography monitor (12, 430) configured for self-optimizing ECG data compression is provided. ECG waveform characteristics are rarely identical in patients with cardiac disease making this innovation crucial for the long-term data storage and analysis of complex cardiac rhythm disorders. The monitor (12, 430) includes a memory (62) and a microcontroller (61) operable to execute under a micro-programmable control and configured to: obtain a series of electrode voltage values (311); select (312) one or more of a plurality of compression algorithms for compressing the electrode voltage series; apply (313) one or more of the selected compression algorithms to the electrode voltage series; evaluate (314) a degree of compression of the electrode voltage series achieved using the application of the selected algorithms; apply (313) one or more of the compression algorithms to the compressed electrode voltage series upon the degree of compression not meeting a predefined threshold; and store (367,379,395) the compressed electrode voltage series within the memory (62).