Journal ArticleDOI
Wavelet and wavelet packet compression of electrocardiograms
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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
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Wavelets on graphs via spectral graph theory
TL;DR: A novel method for constructing wavelet transforms of functions defined on the vertices of an arbitrary finite weighted graph using the spectral decomposition of the discrete graph Laplacian L, based on defining scaling using the graph analogue of the Fourier domain.
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Wavelets on Graphs via Spectral Graph Theory
TL;DR: In this paper, the spectral graph wavelet operator is defined based on spectral decomposition of the discrete graph Laplacian, and a wavelet generating kernel and a scale parameter are used to localize this operator to an indicator function.
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Exploiting smart e-Health gateways at the edge of healthcare Internet-of-Things
Amir M. Rahmani,Tuan Nguyen Gia,Behailu Negash,Arman Anzanpour,Iman Azimi,Mingzhe Jiang,Pasi Liljeberg +6 more
TL;DR: This paper proposes to exploit the concept of Fog Computing in Healthcare IoT systems by forming a Geo-distributed intermediary layer of intelligence between sensor nodes and Cloud and presents a prototype of a Smart e-Health Gateway called UT-GATE.
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Compressed Sensing for Real-Time Energy-Efficient ECG Compression on Wireless Body Sensor Nodes
TL;DR: This paper quantifies the potential of the emerging compressed sensing (CS) signal acquisition/compression paradigm for low-complexity energy-efficient ECG compression on the state-of-the-art Shimmer WBSN mote and shows that CS represents a competitive alternative to state- of- the-art digital wavelet transform (DWT)-basedECG compression solutions in the context of WBSn-based ECG monitoring systems.
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Recommendations for the Standardization and Interpretation of the Electrocardiogram
Paul Kligfield,Leonard S. Gettes,James J. Bailey,Rory Childers,Barbara J. Deal,E. William Hancock,Gerard van Herpen,Jan A. Kors,Peter W. Macfarlane,David M. Mirvis,Olle Pahlm,Pentti M. Rautaharju,Galen S. Wagner +12 more
TL;DR: This statement examines the relation of the resting ECG to its technology to establish standards that will improve the accuracy and usefulness of the ECG in practice and to recommend recommendations for ECG standards.
References
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Embedded image coding using zerotrees of wavelet coefficients
TL;DR: The embedded zerotree wavelet algorithm (EZW) is a simple, yet remarkably effective, image compression algorithm, having the property that the bits in the bit stream are generated in order of importance, yielding a fully embedded code.
Journal ArticleDOI
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Arithmetic coding for data compression
TL;DR: The state of the art in data compression is arithmetic coding, not the better-known Huffman method, which gives greater compression, is faster for adaptive models, and clearly separates the model from the channel encoding.
Journal ArticleDOI
Detection of ECG characteristic points using wavelet transforms
TL;DR: An algorithm based on wavelet transforms (WT's) has been developed for detecting ECG characteristic points and the relation between the characteristic points of ECG signal and those of modulus maximum pairs of its WT's is illustrated.