A wavelet transform-based ECG compression method guaranteeing desired signal quality
TL;DR: A new electrocardiogram compression method based on orthonormal wavelet transform and an adaptive quantization strategy, by which a predetermined percent root mean square difference (PRD) can be guaranteed with high compression ratio and low implementation complexity are presented.
Abstract: This paper presents a new electrocardiogram (ECG) compression method based on orthonormal wavelet transform and an adaptive quantization strategy, by which a predetermined percent root mean square difference (PRD) can be guaranteed with high compression ratio and low implementation complexity.
Citations
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Dissertation•
27 Mar 2013
TL;DR: The study of the efficiency of the compression method for the storage and transmission of EEG data gives an overview on the relevant quality criteria of compression rate and quality of the compressed data as well as the art of EEGData compression.
Abstract: Paul Younan: Kompression von EEG-Daten mittels raumlich harmonischer
Zerlegung
The present work deals with the compression of the data generated during
an investigation electroencephalography (EEG) using spatial harmonic
decomposition. In an EEG-recording with large number of electrodes (256 to
512) and high sampling rates (up to 20 KHz) very large amounts of data
could be recorded. To use less storage space and bandwidth for storing or
transferring these data, an efficient compression tool is required, that is
not allowed to distort the medical diagnosis. A spatially harmonic
decomposition method based on the eigenanalysis of the Laplace-Beltrami
operator on the triangulated surface of the electrode positions could
enable an efficient compression of the recorded EEG data. This approach
involves two steps. Firstly, basis functions will be determined from the
information which is obtained from the electrodes position for the
subsequent data decomposition. Secondly, during a multichannel EEG
examination the recorded data are decomposed using of these basic
functions in coefficients. This work is dedicated to the study of the
efficiency of the compression method for the storage and transmission of
EEG data. This work gives an overview on the relevant quality criteria of
compression rate and quality of the compressed data as well as the art of
EEG data compression. Then the influence of standard and tracked
electrodes position with same number is analyzed. Further strategies of
data compression are designed and analyzed. These strategies are applied
and compared to different EEG records with same and different number of
the electrodes (256 and 64). At the end of this work is the effect of the
strongly noised EEG data on the compression investigated.
1 citations
Additional excerpts
...bei EKG Signalen eingesetzt [27] [28] [29]....
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26 Mar 2009
TL;DR: A fast quality-on-demand (QOD) algorithm for wavelet-based electrocardiogram (ECG) signal compression and a linear prediction model for quantization scale refinement are proposed.
Abstract: In this paper, a fast quality-on-demand (QOD) algorithm for wavelet-based electrocardiogram (ECG) signal compression is proposed. The algorithm based on the 1-D reversible round-off non-recursive discrete periodized wavelet transform (1-D RRO-NRDPWT) and an approximately linear compression performance design to build a linear prediction model for quantization scale refinement. The convergence of quality control is verified to be always held. By using the MIT-BIH arrhythmia database, the experimental results show that the proposed method can effectively improved the computational complexity and convergence speed of quality control.
1 citations
01 Jan 2013
1 citations
Dissertation•
01 Mar 2018
TL;DR: In this article , the authors investigated using BERT (Bidirectional Encoder Representations from Transformers) model, which is a bidirectional neural network that was originally designed for natural language.
Abstract: Electrocardiogram (ECG) is a commonly used tool in biological diagnosis of heart diseases. ECG allows the representation of electrical signals which cause heart muscles to contract and relax. Recently, accurate deep learning methods have been developed to overcome manual diagnosis in terms of time and effort. However, most of current automatic medical diagnosis use long electrocardiogram (ECG) signals to inspect different types of heart arrhythmia. Therefore, ECG signal files tend to require large storage to store and may cause significant overhead when exchanged over a computer network. This raises the need to come up with effective compression methods for ECG signals. In this work, the authors investigate using BERT (Bidirectional Encoder Representations from Transformers) model, which is a bidirectional neural network that was originally designed for natural language. The authors evaluate the model with respect to its compression ratio and information preservation, and measure information preservation in terms of the of the accuracy of a convolutional neural network in classifying the decompressed signal. The results show that the method can achieve up to 83% saving in storage. Also, the classification accuracy of the decompressed signals is around 92.41%. Furthermore, the method enables the user to balance the compression ratio and the required accuracy of the CNN classifiers.
References
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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.
Abstract: We construct orthonormal bases of compactly supported wavelets, with arbitrarily high regularity. The order of regularity increases linearly with the support width. We start by reviewing the concept of multiresolution analysis as well as several algorithms in vision decomposition and reconstruction. The construction then follows from a synthesis of these different approaches.
8,588 citations
"A wavelet transform-based ECG compr..." refers methods in this paper
...Since detailed mathematical aspects of wavelet theory can b found elsewhere [16], here, we shall merely describe the structure of a DOWT-based coding system shown in Fig....
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...The proposed algorithm was implemented on a SparcStation 2 computer, where the wavelet-based filters with 10-taps were designed by Daubechies’s algorithm [16], the layer was set to , the buffer size for segmenting input ECG signals was set to , and the Lempel–Ziv–Welch (LZW) encoder [20] was chosen as the entropy encoder for simplicity....
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TL;DR: A new compression algorithm is introduced that is based on principles not found in existing commercial methods in that it dynamically adapts to the redundancy characteristics of the data being compressed, and serves to illustrate system problems inherent in using any compression scheme.
Abstract: Data stored on disks and tapes or transferred over communications links in commercial computer systems generally contains significant redundancy. A mechanism or procedure which recodes the data to lessen the redundancy could possibly double or triple the effective data densitites in stored or communicated data. Moreover, if compression is automatic, it can also aid in the rise of software development costs. A transparent compression mechanism could permit the use of "sloppy" data structures, in that empty space or sparse encoding of data would not greatly expand the use of storage space or transfer time; however , that requires a good compression procedure. Several problems encountered when common compression methods are integrated into computer systems have prevented the widespread use of automatic data compression. For example (1) poor runtime execution speeds interfere in the attainment of very high data rates; (2) most compression techniques are not flexible enough to process different types of redundancy; (3) blocks of compressed data that have unpredictable lengths present storage space management problems. Each compression ' This article was written while Welch was employed at Sperry Research Center; he is now employed with Digital Equipment Corporation. 8 m, 2 /R4/OflAb l strategy poses a different set of these problems and, consequently , the use of each strategy is restricted to applications where its inherent weaknesses present no critical problems. This article introduces a new compression algorithm that is based on principles not found in existing commercial methods. This algorithm avoids many of the problems associated with older methods in that it dynamically adapts to the redundancy characteristics of the data being compressed. An investigation into possible application of this algorithm yields insight into the compressibility of various types of data and serves to illustrate system problems inherent in using any compression scheme. For readers interested in simple but subtle procedures, some details of this algorithm and its implementations are also described. The focus throughout this article will be on transparent compression in which the computer programmer is not aware of the existence of compression except in system performance. This form of compression is "noiseless," the decompressed data is an exact replica of the input data, and the compression apparatus is given no special program information, such as data type or usage statistics. Transparency is perceived to be important because putting an extra burden on the application programmer would cause
2,426 citations
"A wavelet transform-based ECG compr..." refers methods in this paper
...The proposed algorithm was implemented on a SparcStation 2 computer, where the wavelet-based filters with 10-taps were designed by Daubechies’s algorithm [16], the layer was set to , the buffer size for segmenting input ECG signals was set to , and the Lempel‐Ziv‐Welch (LZW) encoder [ 20 ] was chosen as the entropy encoder for simplicity....
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869 citations
TL;DR: 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. >
690 citations
"A wavelet transform-based ECG compr..." refers methods in this paper
...In most cases, direct methods are superior to transform methods with respect to system complexity and the error control mechanism, however, transform methods usually achieve higher compression ratios and are insensitive to the noise contained in original ECG signals [1]....
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...In direct methods, the compression is done directly on the ECG samples; examples include the amplitude zone time epoch coding (AZTEC), the turning point (TP), the coordinate reduction time encoding system (CORTES), the scan-along polygonal approximation (SAPA), peak-picking, cycle-to-cycle, and differential pulse code modulation (DPCM) [1]–[4]....
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TL;DR: 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.
445 citations