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Journal ArticleDOI

ECG data compression techniques-a unified approach

TLDR
The theoretical bases behind the direct ECG data compression schemes are presented and classified into three categories: tolerance-comparison compression, DPCM, and entropy coding methods and a framework for evaluation and comparison of ECG compression schemes is presented.
Abstract
Electrocardiogram (ECG) compression techniques are compared, and a unified view of these techniques is established. ECG data compression schemes are presented in two major groups: direct data compression and transformation methods. The direct data compression techniques are ECG differential pulse code modulation (DPCM) and entropy coding, AZTEC, Turning-point, CORTES, Fan and SAPA algorithms, peak-picking, and cycle-to-cycle compression methods. The transformation methods include Fourier, Walsh, and Karhunen-Loeve transforms. The theoretical bases behind the direct ECG data compression schemes are presented and classified into three categories: tolerance-comparison compression, DPCM, and entropy coding methods. A framework for evaluation and comparison of ECG compression schemes is presented. >

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Journal ArticleDOI

EEG data compression techniques

TL;DR: Electroencephalograph (EEG) and Holter EEG data compression techniques which allow perfect reconstruction of the recorded waveform from the compressed one are presented and discussed and the adoption of a collapsed Huffman tree for the encoding/decoding operations is shown.
Journal ArticleDOI

Family of iterative LS-based dictionary learning algorithms, ILS-DLA, for sparse signal representation

TL;DR: This work presents a family of iterative least squares based dictionary learning algorithms (ILS-DLA), including algorithms for design of signal dependent block based dictionaries and overlapping dictionaries, as generalizations of transforms and filter banks, respectively.
Journal ArticleDOI

ECG data compression using cut and align beats approach and 2-D transforms

TL;DR: A new electrocardiogram (ECG) data compression method is presented which employs a two dimensional (2-D) transform which illustrates substantial improvement in compression ratio over one-dimensional methods for comparable percent root-mean-square difference (PRD).
Journal ArticleDOI

ECG signal compression using analysis by synthesis coding

TL;DR: An electrocardiogram (ECG) compression algorithm, called analysis by synthesis ECG compressor (ASEC), is introduced and was found to be superior to several well-known ECG compression algorithms at all tested bit rates.
Journal ArticleDOI

u-Healthcare system: state-of-the-art review and challenges.

TL;DR: A comprehensive review of up-to-date requirements in hardware, communication, and computing for next-generation u-Health systems is presented and new technological trends and design challenges they have to cope with, while designing such systems are presented.
References
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Journal ArticleDOI

A mathematical theory of communication

TL;DR: This final installment of the paper considers the case where the signals or the messages or both are continuously variable, in contrast with the discrete nature assumed until now.
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A Method for the Construction of Minimum-Redundancy Codes

TL;DR: A minimum-redundancy code is one constructed in such a way that the average number of coding digits per message is minimized.
Journal ArticleDOI

Linear prediction: A tutorial review

TL;DR: This paper gives an exposition of linear prediction in the analysis of discrete signals as a linear combination of its past values and present and past values of a hypothetical input to a system whose output is the given signal.
Journal ArticleDOI

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.
Proceedings ArticleDOI

Orthogonal transforms for digital signal processing

TL;DR: The utility and effectiveness of these transforms are evaluated in terms of some standard performance criteria such as computational complexity, variance distribution, mean-square error, correlated rms error, rate distortion, data compression, classification error, and digital hardware realization.
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