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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Proceedings ArticleDOI
Quality controlled ECG compression using Discrete Cosine transform (DCT) and Laplacian Pyramid (LP)
TL;DR: A new quality controlled Discrete Cosine transform (DCT) and Laplacian Pyramid based compression method for electrocardiogram (ECG) signal and results show that DCT gives better performance at low PRD.
Journal ArticleDOI
ECG compression method using Lorentzian functions model
Abdelaziz Ouamri,Amine Nait-Ali +1 more
TL;DR: An ECG compression algorithm using a combination of Lorentzian functions model and discrete Fourier transform is proposed and tested for its coding efficiency and reconstruction capability by applying it to several popular, benchmark ECG signals.
Proceedings ArticleDOI
A two dimensional wavelet packet approach for ECG compression
A.R.A. Moghaddam,K. Nayebi +1 more
TL;DR: One simple compression algorithm in the 2-D WPT domain, which is applied to some records in the MIT-BIH arrhythmia database shows lower percent root mean square difference (PRD) than 1-D wavelet based compression methods for the same compression ratio (CR).
Accurate determination of respiratory rhythm and pulse rate using an under-pillow sensor based on wavelet transformation
TL;DR: A real-time noninvasive and unconstrained method is proposed to determine the respiratory rhythm and pulse rate with an under-pillow sensor during sleep by employing the a trous algorithm of wavelet transformation (WT).
Proceedings ArticleDOI
Signal Characteristics on Sensor Data Compression in IoT -An Investigation
TL;DR: The aim of the work is to classify the compression algorithms based on the signal characteristics of sensor data and to map them to different sensor data types to ensure efficient compression.
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.