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. >read more
Citations
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Journal ArticleDOI
Wavelet transforms and the ECG: a review.
TL;DR: In this review, the emerging role of the wavelet transform in the interrogation of the ECG is discussed in detail, where both the continuous and the discrete transform are considered in turn.
Journal ArticleDOI
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.
Journal ArticleDOI
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.
Journal ArticleDOI
Recommendations for the Standardization and Interpretation of the Electrocardiogram: Part I: The Electrocardiogram and Its Technology A Scientific Statement From the American Heart Association Electrocardiography and Arrhythmias Committee, Council on Clinical Cardiology; the American College of Cardiology Foundation; and the Heart Rhythm Society Endorsed by the International Society for Computerized Electrocardiology
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.
Journal ArticleDOI
Wavelet compression of ECG signals by the set partitioning in hierarchical trees algorithm
TL;DR: A wavelet electrocardiogram (ECG) data codec based on the set partitioning in hierarchical trees (SPIHT) compression algorithm is proposed and is significantly more efficient in compression and in computation than previously proposed ECG compression schemes.
References
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Compression of the ECG by Prediction or Interpolation and Entropy Encoding
TL;DR: In a sample of 220 Frank4ead ECG's the removal of signal redundancy by second-order prediction or interpolation with subsequent entropy encoding of the respective residual errors was investigated, finding interpolation provided a 6 dB smaller residual error variance than prediction.
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Special Feature An Overview of Data Compression Techniques
TL;DR: Reducing redundancy in data representation leads to decreased data storage requirements and lower costs for data communication.