A Novel Blaschke Unwinding Adaptive-Fourier-Decomposition-Based Signal Compression Algorithm With Application on ECG Signals

This paper presents a novel signal compression algorithm based on the Blaschke unwinding adaptive Fourier decomposition (AFD). The Blaschke unwinding AFD is a newly developed signal decomposition theory. It utilizes the Nevanlinna factorization and the maximal selection principle in each decompositi...

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Vydáno v:IEEE journal of biomedical and health informatics Ročník 23; číslo 2; s. 672 - 682
Hlavní autoři: Tan, Chunyu, Zhang, Liming, Wu, Hau-tieng
Médium: Journal Article
Jazyk:angličtina
Vydáno: United States IEEE 01.03.2019
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:
Adaptive algorithms
adaptive Fourier decomposition (AFD)
Algorithms
Arrhythmia
Blaschke product
Compression
Compression algorithms
Compression tests
Data Compression - methods
Databases, Factual
Decomposition
E-health
EKG
electrocardiogram (ECG)
Electrocardiography
Electrocardiography - methods
Fourier Analysis
Fourier series
Heart rate
Humans
Image reconstruction
Informatics
signal compression
Signal processing algorithms
Telemedicine - methods
Transforms
Unwinding
ISSN:2168-2194, 2168-2208, 2168-2208
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Shrnutí:This paper presents a novel signal compression algorithm based on the Blaschke unwinding adaptive Fourier decomposition (AFD). The Blaschke unwinding AFD is a newly developed signal decomposition theory. It utilizes the Nevanlinna factorization and the maximal selection principle in each decomposition step, and achieves a faster convergence rate with higher fidelity. The proposed compression algorithm is applied to the electrocardiogram signal. To assess the performance of the proposed compression algorithm, in addition to the generic assessment criteria, we consider the less discussed criteria related to the clinical needs-for the heart rate variability analysis purpose, how accurate the R-peak information is preserved is evaluated. The experiments are conducted on the MIT-BIH arrhythmia benchmark database. The results show that the proposed algorithm performs better than other state-of-the-art approaches. Meanwhile, it also well preserves the R-peak information.
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ISSN:2168-2194
2168-2208
2168-2208
DOI:10.1109/JBHI.2018.2817192