Waveform Entropy Compression for Low-Power Cardiac Data Storage
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Solution Overview
Problem
Implantable medical devices (IMDs) face challenges in efficiently collecting, storing, and transmitting data due to limited resources such as memory and power, necessitating effective data compression methods to optimize resource usage while maintaining data reliability and accuracy.
Innovation Solution
The implementation of a data compression module within IMDs that employs linear predictive encoding and truncated entropy encoding to compress cardiac data, utilizing a lookup table and Huffman encoding to achieve lossless compression, thereby reducing memory usage and power consumption.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If more memory resources are allocated to store cardiac data, then data storage capacity is improved, but device size and power consumption increase
Solution Approach 1:
The patent extracts only the essential information from cardiac waveforms by identifying and storing key features (such as R-wave peaks, intervals, and morphological characteristics) rather than storing complete high-resolution waveform data. This extraction approach reduces memory requirements and power consumption while preserving clinically relevant information for patient monitoring and analysis
Solution Approach 2:
The patent transforms waveform data from time-domain continuous signals to discrete parameter representations (intervals, amplitudes, frequencies, and morphological indices). By changing the representation parameters from raw voltage-time data to extracted feature parameters, the system achieves compact storage with reduced power consumption while maintaining diagnostic utility
2Measurement precision
If higher resolution data is collected and stored, then measurement precision is improved, but memory usage and processing requirements increase
Solution Approach 1:
The system extracts critical measurement parameters (R-R intervals, P-R intervals, QRS duration, amplitude measurements) from high-resolution waveforms and stores only these extracted values. This approach maintains measurement precision for clinical decision-making while dramatically reducing memory usage by storing discrete parameters instead of continuous waveform data
Solution Approach 2:
The patent applies partial action by selectively processing and storing only the most clinically significant waveform parameters rather than complete high-resolution data. By focusing on essential measurements (rate, rhythm, intervals) that suffice for most clinical purposes, the system achieves adequate measurement precision with reduced memory requirements
3Quantity of substance
If more processing power is used for data compression, then data storage efficiency is improved, but device complexity and power consumption increase
Solution Approach 1:
The patent implements self-service by using the waveform data itself to guide the compression process. The algorithm automatically identifies characteristic features (peaks, valleys, intervals) and adapts the compression strategy based on the inherent structure of the cardiac signal, eliminating the need for complex external compression controllers or manual parameter tuning
4Quantity of substance
If data is compressed more aggressively, then memory usage is reduced, but data reliability and accuracy may deteriorate
Solution Approach 1:
The system extracts and stores complete information about critical events (exact timing, amplitude, and duration of P-waves, QRS complexes, T-waves) rather than attempting to compress the entire waveform. This selective extraction ensures that clinically essential data remains fully reliable and accurate while non-critical portions are compressed or discarded
Solution Approach 2:
The patent applies different compression strategies to different portions of the waveform data based on local importance. Critical segments (arrhythmia events, ST-segment changes, paced beats) are preserved with high fidelity or uncompressed, while normal sinus rhythm segments undergo more aggressive compression. This local quality approach maintains data reliability for diagnostic-critical information while reducing overall memory usage
Data Source
AI summary
Waveforms are digitally sampled and compressed for storage in memory. The compression of the data includes generating a truncated entropy encoding map and using the values within the map to obtain good compression. An encoder further sub-selects values to be encoded and values to remain unencoded to provide an overall compression of the data.