Journal ArticleDOI
Wavelet and wavelet packet compression of electrocardiograms
TLDR
Pilot data from a blind evaluation of compressed ECG's by cardiologists suggest that the clinically useful information present in original ECG signals is preserved by 8:1 compression, and in most cases 16:1 compressed ECGs are clinically useful.Abstract:
Wavelets and wavelet packets have recently emerged as powerful tools for signal compression. Wavelet and wavelet packet-based compression algorithms based on embedded zerotree wavelet (EZW) coding are developed for electrocardiogram (ECG) signals, and eight different wavelets are evaluated for their ability to compress Holter ECG data. Pilot data from a blind evaluation of compressed ECG's by cardiologists suggest that the clinically useful information present in original ECG signals is preserved by 8:1 compression, and in most cases 16:1 compressed ECG's are clinically useful.read more
Citations
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Journal ArticleDOI
The effects of compression on ultra wideband radar signals
Brian McGinley,Martin O'Halloran,Raquel C. Conceicao,Garry Higgins,Edward Jones,Martin Glavin +5 more
TL;DR: This study examines the efiects of lossy signal compression on an UWB breast cancer classiflcation algorithm and compares the lossy JPEG2000 and Set Partitioning In Hierarchical Trees (SPIHT) algorithms for UWB signal compression.
Proceedings ArticleDOI
Real-time compressed sensing-based electrocardiogram compression on energy-constrained wireless body sensors
TL;DR: Interestingly, the results show that CS represents a competitive alternative to state-of-the-art digital wavelet transform (DWT)-based ECG compression solutions in terms of overall energy efficiency and Shimmer™ node lifetime extension.
A Combinatorial Model for ECG Interpretation
TL;DR: A new, combinatorial model for analyzing and interpreting an electrocardiogram (ECG) is presented and an application of the model is QRS peak detection, demonstrated with an online algorithm, which is shown to be space as well as time efficient.
Wavelet transform in ECG signal processing
TL;DR: The theory of wavelet transform is introduced and several promising applications in cardiology are shown, including data compression and digital signal processing and analysis.
Book ChapterDOI
Sparse Encoding Algorithm for Real-Time ECG Compression
Rohan Basu Roy,Arani Roy,Amitava Mukherjee,Alekhya Ghosh,Soham Bhattacharyya,Mrinal Kanti Naskar +5 more
TL;DR: A sparse encoding algorithm consisting of two schemes namely geometry-based method (GBM) and the wavelet transform-based iterative thresholding (WTIT) that converts the sparse matrices into compressed, transmittable matrices.
References
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Journal ArticleDOI
A theory for multiresolution signal decomposition: the wavelet representation
TL;DR: In this paper, it is shown that the difference of information between the approximation of a signal at the resolutions 2/sup j+1/ and 2 /sup j/ (where j is an integer) can be extracted by decomposing this signal on a wavelet orthonormal basis of L/sup 2/(R/sup n/), the vector space of measurable, square-integrable n-dimensional functions.
Book
Ten lectures on wavelets
TL;DR: This paper presents a meta-analyses of the wavelet transforms of Coxeter’s inequality and its applications to multiresolutional analysis and orthonormal bases.
Journal ArticleDOI
Ten Lectures on Wavelets
TL;DR: In this article, the regularity of compactly supported wavelets and symmetry of wavelet bases are discussed. But the authors focus on the orthonormal bases of wavelets, rather than the continuous wavelet transform.
Journal ArticleDOI
Orthonormal bases of compactly supported wavelets
TL;DR: This work construct orthonormal bases of compactly supported wavelets, with arbitrarily high regularity, by reviewing the concept of multiresolution analysis as well as several algorithms in vision decomposition and reconstruction.
Journal ArticleDOI
A new, fast, and efficient image codec based on set partitioning in hierarchical trees
Amir Said,William A. Pearlman +1 more
TL;DR: The image coding results, calculated from actual file sizes and images reconstructed by the decoding algorithm, are either comparable to or surpass previous results obtained through much more sophisticated and computationally complex methods.