Waveform Compression Using Differential Entropy Encoding
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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, using a combination of Huffman encoding and adaptive codeword lengths to achieve lossless compression, thereby reducing memory usage and power consumption.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If data compression is implemented to reduce memory usage, then memory capacity requirements decrease, but data reconstruction accuracy may be compromised
Solution Approach 1:
The patent transforms the data representation parameters by converting waveform data into derivative values and applying entropy encoding with variable codeword lengths. This parameter transformation enables significant compression (reducing memory capacity requirements) while preserving the ability to perfectly reconstruct the original waveform through inverse transformation, thus maintaining measurement precision.
Solution Approach 2:
The patent creates an encoded representation (copy) of the original waveform data using differential entropy encoding. This encoded copy uses fewer bits per data point while containing all necessary information to reconstruct the original waveform exactly, achieving both memory reduction and accuracy preservation.
2Quantity of substance
If more memory resources are allocated for data storage, then data collection capacity increases, but power consumption increases
Solution Approach 1:
By changing the data representation parameters through differential encoding and entropy compression, the patent reduces the storage capacity needed for the same amount of waveform data. This indirectly reduces power consumption since less memory resources are required to store the compressed data representation.
3Device complexity
If standard fixed-length encoding is used for simplicity, then device complexity decreases, but compression efficiency deteriorates
Solution Approach 1:
The patent applies local quality by using variable-length codewords where the most frequently occurring derivative values are represented by shorter codewords, while less frequent values use longer codewords. This local optimization of codeword length based on frequency distribution achieves high compression efficiency while maintaining manageable device complexity through the use of lookup tables.
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.