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
Citations
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Book ChapterDOI
Compression of ECG Signals Using a Novel Discrete Wavelet Transform Algorithm for Dynamic Arrythmia Database
TL;DR: A wavelet-based electrocardiogram (ECG) data compression algorithm for dynamic Arrythmia database is presented and is compared with direct wavelet based compression algorithm which represents superior performance.
Journal ArticleDOI
Studying the Effects of Compression in EEG-Based Wearable Sleep Monitoring Systems
Deland Hu Liu,Syed Anas Imtiaz +1 more
TL;DR: This paper looks at how the overall sleep staging accuracy as well as the detection accuracy of different sleep stages is reduced as a result of different EEG compression methods, and shows that the SPIHT and predictor-based compression methods outperform wavelet and filter-based methods in preserving the relevant signal features.
Dissertation
ECG compression using rule based thresholding of wavelet coefficients
TL;DR: The proposed algorithm provides improved performance in terms of computational efficiency and compression rate where the clinically significant features in the reconstructed ECG signal are preserved and yields to good results in comparison with other wavelet transform based compression methods described in the literature.
Classification and compression of cardiac vascular disease to enhance rural health care system using softcomputing techniques
TL;DR: The abnormalities found in the ECG signals are analyzed by identifying the Normal, Bradycardia Arrhythmia, TachycardiaArrhythmmia and Ischemia signal using the method of Neuro Fuzzy Classifier.
Proceedings ArticleDOI
Electrocardiogram Compression Technique Using DWT-Based Residue Encoder with Desired Reconstruction Quality
TL;DR: A new compression technique which exploits the high correlations between the consecutive beats of an Electrocardiogram (ECG) and subjected to compression using Discrete Wavelet Transform (DWT).
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.