Journal ArticleDOI
ECG data compression techniques-a unified approach
Reads0
Chats0
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. >read more
Citations
More filters
Proceedings ArticleDOI
Compression of ECG using a code excited linear prediction (CELP)
TL;DR: A lossy ECG signal compression method using CELP is introduced here and uses an analysis by synthesis structure which models short term changes in the characteristics of individual pulses.
Proceedings ArticleDOI
Multichannel ECG data compression method based on a new modeling method
A. Prieto,Corinne Mailhes +1 more
TL;DR: This work addresses the problem of multichannel ECG data compression by considering different independent leads as the outputs of linear filters in which the excitation signal is one of these leads suitably chosen.
Journal ArticleDOI
A dynamic processing system for sensor data in IoT
TL;DR: A dynamic sensor data processing (SDP) system to capture and process sensor data continuously on the basis of data streaming technology is proposed and can compress sensor data through dynamically balancing the accuracy and compression rate.
Proceedings ArticleDOI
An ECG compression approach based on a segment dictionary and bezier approximations
TL;DR: This paper proposes a methodology for ECG (electrocardiograms) data compression based on R-R segmentation, which uses a segment dictionary combined with an efficient form of progressive error codification.
References
More filters
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.
Journal ArticleDOI
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
K. R. Rao,N. U. Ahmed +1 more
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.