Using contexts and R–R interval estimation in lossless ECG compression

The paper presents a new lossless ECG compression scheme. The short-term predictor and the coder use conditioning on a small number of contexts. The long-term prediction is based on an algorithm for R–R interval estimation. Several QRS detection algorithms are investigated to select a low complexity...

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Published inComputer methods and programs in biomedicine Vol. 67; no. 3; pp. 177 - 186
Main Authors GIURCANEANU, Ciprian Doru, TABUS, Ioan, MEREUTA, Serban
Format Journal Article
LanguageEnglish
Published Shannon Elsevier Ireland Ltd 01.03.2002
Elsevier Science
Subjects
Algorithms
Biological and medical sciences
Computerized, statistical medical data processing and models in biomedicine
Context coding
Electrocardiography - methods
Electrocardiography. Vectocardiography
Electrodiagnosis. Electric activity recording
Escape codes
General aspects. Methods
Heart - physiopathology
Humans
Investigative techniques, diagnostic techniques (general aspects)
Lossless coding
Medical sciences
R–R interval
R–R interval
Context coding
Escape codes
Lossless coding
Context
Human
Evaluation
Parameter estimation
Data compression
QRS interval
Experimental study
Algorithm
RR interval
Electrodiagnosis
Biomedical data processing
Coding
Electrocardiography
Signal processing
Detection
Biomedical engineering
Online AccessGet full text
ISSN0169-2607
1872-7565
DOI10.1016/S0169-2607(01)00126-2

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Abstract The paper presents a new lossless ECG compression scheme. The short-term predictor and the coder use conditioning on a small number of contexts. The long-term prediction is based on an algorithm for R–R interval estimation. Several QRS detection algorithms are investigated to select a low complexity and reliable detection algorithm. The coding of prediction residuals uses primarily the Golomb-Rice (GR) codes, but, to improve the coding results, escape codes GR–ESC are used in some contexts for a limited number of samples. Experimental results indicate the good overall performance of the lossless ECG compression algorithms (reducing the storage needs from 12 to about 3–4 bits per sample). The scheme consistently outperforms other waveform or general purpose coding algorithms.
AbstractList The paper presents a new lossless ECG compression scheme. The short-term predictor and the coder use conditioning on a small number of contexts. The long-term prediction is based on an algorithm for R-R interval estimation. Several QRS detection algorithms are investigated to select a low complexity and reliable detection algorithm. The coding of prediction residuals uses primarily the Golomb-Rice (GR) codes, but, to improve the coding results, escape codes GR- ESC are used in some contexts for a limited number of samples. Experimental results indicate the good overall performance of the lossless ECG compression algorithms (reducing the storage needs from 12 to about 3-4 bits per sample). The scheme consistently outperforms other waveform or general purpose coding algorithms.
The paper presents a new lossless ECG compression scheme. The short-term predictor and the coder use conditioning on a small number of contexts. The long-term prediction is based on an algorithm for R–R interval estimation. Several QRS detection algorithms are investigated to select a low complexity and reliable detection algorithm. The coding of prediction residuals uses primarily the Golomb-Rice (GR) codes, but, to improve the coding results, escape codes GR–ESC are used in some contexts for a limited number of samples. Experimental results indicate the good overall performance of the lossless ECG compression algorithms (reducing the storage needs from 12 to about 3–4 bits per sample). The scheme consistently outperforms other waveform or general purpose coding algorithms.
The paper presents a new lossless ECG compression scheme. The short-term predictor and the coder use conditioning on a small number of contexts. The long-term prediction is based on an algorithm for R-R interval estimation. Several QRS detection algorithms are investigated to select a low complexity and reliable detection algorithm. The coding of prediction residuals uses primarily the Golomb-Rice (GR) codes, but, to improve the coding results, escape codes GR-ESC are used in some contexts for a limited number of samples. Experimental results indicate the good overall performance of the lossless ECG compression algorithms (reducing the storage needs from 12 to about 3-4 bits per sample). The scheme consistently outperforms other waveform or general purpose coding algorithms.The paper presents a new lossless ECG compression scheme. The short-term predictor and the coder use conditioning on a small number of contexts. The long-term prediction is based on an algorithm for R-R interval estimation. Several QRS detection algorithms are investigated to select a low complexity and reliable detection algorithm. The coding of prediction residuals uses primarily the Golomb-Rice (GR) codes, but, to improve the coding results, escape codes GR-ESC are used in some contexts for a limited number of samples. Experimental results indicate the good overall performance of the lossless ECG compression algorithms (reducing the storage needs from 12 to about 3-4 bits per sample). The scheme consistently outperforms other waveform or general purpose coding algorithms.
Author Giurcăneanu, Ciprian Doru
Mereuţă, Şerban
Tăbuş, Ioan
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Issue 3
Keywords R–R interval
Context coding
Escape codes
Lossless coding
Context
Human
Evaluation
Parameter estimation
Data compression
QRS interval
Experimental study
Algorithm
RR interval
Electrodiagnosis
Biomedical data processing
Coding
Electrocardiography
Signal processing
Detection
Biomedical engineering
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Snippet The paper presents a new lossless ECG compression scheme. The short-term predictor and the coder use conditioning on a small number of contexts. The long-term...
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SubjectTerms Algorithms
Biological and medical sciences
Computerized, statistical medical data processing and models in biomedicine
Context coding
Electrocardiography - methods
Electrocardiography. Vectocardiography
Electrodiagnosis. Electric activity recording
Escape codes
General aspects. Methods
Heart - physiopathology
Humans
Investigative techniques, diagnostic techniques (general aspects)
Lossless coding
Medical sciences
R–R interval
Title Using contexts and R–R interval estimation in lossless ECG compression
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