Multichannel ECG Compression Using Cross-Channel Predictive Coding
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Solution Overview
Problem
Current methods for compressing multichannel electrocardiogram (ECG) signals face challenges in achieving high compression ratios while maintaining losslessness, due to power consumption constraints in portable devices and the need for compliance with medical norms that restrict distortion.
Innovation Solution
A method for lossless compression of multichannel ECG signals using a combination of delta encoding, in-channel and cross-channel predictive encoding, and entropy coding, which reduces statistical redundancy and allows for efficient data representation with low computational complexity, suitable for portable devices.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If lossless compression methods are used to maintain data quality for medical diagnosis, then the compression ratio is limited and computational complexity increases, but if simple compression methods are used, then computational complexity decreases but compression efficiency is insufficient
Solution Approach 1:
The compression method segments the ECG signal processing into distinct stages: delta encoding to remove baseline drift, linear predictive coding to exploit temporal redundancy, and entropy coding to achieve final compression. This segmentation allows each stage to be optimized independently, maintaining lossless compression while managing computational complexity through modular processing.
Solution Approach 2:
The method applies preliminary delta encoding and linear predictive coding before final entropy coding. By performing preliminary compression stages that remove obvious redundancies first, the subsequent entropy coding operates on already simplified data, reducing the overall computational burden while maintaining lossless compression ratios.
2Loss of information
If advanced compression algorithms are used to achieve high compression ratios, then compression efficiency improves, but power consumption increases which is unacceptable for portable devices
Solution Approach 1:
The patent replaces complex mechanical signal processing with efficient digital algorithms. Specifically, it substitutes heavy computational methods with lightweight linear predictive coding and entropy coding techniques that achieve high compression ratios with minimal power consumption, suitable for battery-powered portable ECG devices.
Solution Approach 2:
The method changes the parameter representation of ECG signals by transforming time-domain signals into prediction residuals and entropy-coded bitstreams. This parameter transformation enables compact representation with fewer bits while maintaining diagnostic quality, reducing the energy required for data transmission and storage in portable devices.
3Reliability
If multiple ECG channels are processed simultaneously to provide redundancy for reliable diagnosis, then reliability improves, but the data volume and processing complexity increase
Solution Approach 1:
The patent merges the processing of multiple ECG channels by applying the same compression algorithm suite (delta encoding, linear predictive coding, entropy coding) to each channel simultaneously. This unified approach processes multiple channels through a single standardized pipeline, reducing overall system complexity while maintaining the diagnostic reliability benefits of multichannel redundancy.
Data Source
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AI summary
The object of the invention is a method for lossless compression of a multichannel electrocardiogram signal carried out by a computer system. The method comprises encoding (1), predictors training (2), decoding (3). Encoding (1) comprises: delta encoding (10) and overflow prevention (11) of an electrocardiogram signal from each channel, detecting heartbeats, entropy coding (14) with the use of a variance of a prediction error, multiplexing (15) encoded signals from multiple channels into sequence of symbols. The invention is characterized in: in-channel predictive encoding (12) using the provided in-channel predictor coefficients, separate for heartbeats and background components of the electrocardiogram signal, cross-channel predictive decoding (13) using the provided tree of dependencies and cross-channel predictor coefficients. Parameters of prediction and entropy coding are obtained during predictors training (2) for blocks with predefined length of electrocardiogram signal from at least one channel