Cardiac Waveform Compression Using Truncated 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 maximize benefit while minimizing power consumption and memory usage.
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 by encoding data points within an optimized band and using shorter codewords for frequent values, while longer codewords are used for less frequent values, thereby achieving a positive compression ratio.
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 QRS complexes, P waves, T waves) rather than storing complete high-resolution waveform data. This extraction approach reduces memory requirements while preserving clinically relevant information for diagnosis and monitoring.
Solution Approach 2:
The patent segments continuous cardiac waveform data into discrete, meaningful events or features. By dividing the waveform into identifiable components (atrial activation, ventricular activation, repolarization phases) and storing only these segmented features with their temporal characteristics, the system achieves efficient compression without losing diagnostic value.
2Measurement precision
If higher resolution cardiac waveform data is collected, then measurement precision is improved, but memory usage and power consumption increase
Solution Approach 1:
The patent extracts critical waveform features and events (such as onset, peak, and offset times of cardiac waves) rather than storing complete high-resolution waveform sequences. This selective extraction maintains measurement precision for diagnostic purposes while dramatically reducing the volume of data requiring storage.
Solution Approach 2:
The patent transforms continuous waveform data into discrete parameter representations (such as timing intervals, amplitude measurements, and event markers). By changing the data representation from high-resolution continuous signals to compressed parameter sets, the system preserves essential measurement information while reducing memory requirements.
3Reliability
If complete cardiac waveform data is stored without compression, then data accuracy is improved, but device complexity and power consumption increase
Solution Approach 1:
The patent extracts and stores only the essential diagnostic features from cardiac waveforms (such as R-wave detection, PR interval, QRS duration, QT interval) rather than preserving complete waveform data. This extraction maintains sufficient data accuracy for clinical interpretation while simplifying storage and processing requirements.
Solution Approach 2:
The patent performs preliminary processing and feature extraction at the time of data acquisition, identifying and marking critical waveform events before storage. By performing this preprocessing action in advance, the system reduces the complexity of subsequent data handling while ensuring that accurate feature information is captured and preserved.
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.