DocumentCode
760929
Title
Automatic Real-Time ECG Coding Methodology Guaranteeing Signal Interpretation Quality
Author
Alesanco, Alvaro ; Garcia, J.
Author_Institution
Aragon Inst. of Eng. Res., Univ. of Zaragoza, Zaragoza
Volume
55
Issue
11
fYear
2008
Firstpage
2519
Lastpage
2527
Abstract
This paper introduces a new methodology for compressing ECG signals in an automatic way guaranteeing signal interpretation quality. The approach is based on noise estimation in the ECG signal that is used as a compression threshold in the coding stage. The Set Partitioning in Hierarchical Trees algorithm is used to code the signal in the wavelet domain. Forty different ECG records from two different ECG databases commonly used in ECG compression have been considered to validate the approach. Three cardiologists have participated in the clinical trial using mean opinion score tests in order to rate the signals quality. Results showed that the approach not only achieves very good ECG reconstruction quality but also enhances the visual quality of the ECG signal.
Keywords
electrocardiography; encoding; image reconstruction; medical signal processing; wavelet transforms; automatic real-time ECG coding methodology; hierarchical trees algorithm; image reconstruction; noise estimation; set partitioning; wavelet domain; Cardiology; Communications technology; Data compression; Databases; Distortion measurement; Electrocardiography; Fourier transforms; IEEE members; Paramagnetic resonance; Partitioning algorithms; Visual databases; Wavelet domain; Wavelet transforms; Clinical evaluation; ECG coding; Set Partitioning in Hierarchical Trees (SPIHT); Algorithms; Analysis of Variance; Arrhythmias, Cardiac; Data Compression; Electrocardiography; Humans; Information Storage and Retrieval; Medical Records Systems, Computerized; Quality Control; Reproducibility of Results;
fLanguage
English
Journal_Title
Biomedical Engineering, IEEE Transactions on
Publisher
ieee
ISSN
0018-9294
Type
jour
DOI
10.1109/TBME.2008.2001263
Filename
4547479
Link To Document