Entropy Encoding of Cardiac Waveforms for Low-Power IMD 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, while maintaining reliability and minimizing size, which is exacerbated by the need for various features and functions.
Innovation Solution
The implementation of a data compression module within IMDs that uses linear predictive encoding and truncated entropy encoding to compress cardiac data, allowing for lossless compression and efficient storage, while minimizing power consumption by dynamically adjusting codeword lengths based on data point probabilities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If data is stored with high resolution and no compression, then measurement precision is improved, but memory usage increases and power consumption increases
Solution Approach 1:
The patent applies entropy encoding to change the parameter of data representation from fixed-length binary codes to variable-length codewords based on probability distributions. This transforms the storage parameter from uniform bit allocation to optimized code length allocation, achieving compression while maintaining full data resolution for lossless reconstruction.
2Productivity
If data is compressed using fixed-length codes, then storage efficiency is improved, but measurement precision is lost due to quantization
Solution Approach 1:
The patent uses entropy encoding to create an optimized copy representation of the data where information is preserved but encoded more efficiently. The encoding process creates a compressed copy that can be losslessly decoded back to the original high-resolution data, avoiding the information loss inherent in fixed-length quantization schemes.
3Quantity of substance
If variable length codes are used for compression, then memory usage is reduced, but device complexity increases
Solution Approach 1:
The patent performs preliminary analysis to determine the probability distribution of data values before encoding. By pre-calculating the entropy and optimal codeword assignments based on observed frequencies, the system prepares the encoding scheme in advance, which simplifies the actual encoding process and reduces runtime complexity while achieving effective compression.
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.