The comparison of different transform based methods for ECG data compression

Shinde, Amita A.; Kanjalkar, Pramod

doi:10.1109/icsccn.2011.6024570

Cited by 27 publications

(12 citation statements)

References 8 publications

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“…[5], Ayrık Kosinüs Dönüşüm (AKD) tekniğini EKG verilerini sıkıştırmak için kullanmıştır. Shinde ve Kanjalkar [6], EKG verilerinin sıkıştırılması için FTD, AKD ve Dalgacık Tabanlı Dönüşüm (DTD) tekniklerinin karşılaştırılmalı bir çalışmasını gerçekleştirmiştir. Bu çalışmada, DTD sıkıştırma algoritmasının FTD ve AKD sıkıştırma algoritmalarından daha iyi performans sağladığı gösterilmiştir.…”

Section: Gi̇ri̇ş (Introduction)unclassified

EKG verilerinin destek vektör regresyon yöntemiyle sıkıştırılması

Karal¹

2018

Gazi Üniversitesi Mühendislik Mimarlık Fakültesi Dergisi

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Highlights:Graphical/Tabular Abstract Purpose: Electrocardiogram (ECG) signals must be continuously recorded and monitored to effectively detect diseases caused by fast or slow heartbeat, that is, rhythm disorders. However, long monitoring periods generates large amount of data that are difficult to store and transmit. Moreover, these records may be subject to noise due to the environment. For this reason, an effective and reliable data compression technique is needed for ECG data compression without losing the clinical information content. In order to address the aforementioned problems, in this paper, SVR based compression algorithm is introduced. Theory and Methods: SVR provides a better generalization capability because it tries to minimize both the empirical risk minimization and the structural risk minimization principle. Therefore, SVR has been widely used in many scientific areas. The success of SVR in application is dependent on the ε-insensitive Laplace (Vapnik) loss function that ignores the errors lower than the user-defined ε value. Small noisy training samples falling into the ε-insensitive zone are not included in the solution representation. So, SVR yields a sparse (compressed) model in the obtained solution. The compressed signal is expressed as the weighted sum of basis functions. Different from the other transformation-based compression methods, the number, position, and shape of these functions are automatically determined by the SVR algorithm based on the solution of the quadratic optimization problem. Results:From the experimental results, the user defined ε parameter allows us to check the selection of the samples (support vectors) related to the direct compression. If the value of the ε parameter increases, the Compression Ratio (CR) also increases, and at the same time increases the Percent Root Mean Square Difference(PRD) and Root Mean Square Error (RMSE) values, which leads to distortions in the compressed signal. Also, when the value of ε increases, the SVR algorithm also increases in generalization ability (lowers w). Conclusion:Computer simulation results show that the performance of the proposed SVR-based compression algorithm is better than other transformation-based compression techniques such as the commonly used Fourier Transform (FT), Discrete Cosine Transform (DCT), and Discrete Wavelet Transform (DWT). As can be seen from the table, at the same compression ratio (SO = 6.38), the SVR technique reached minimum PRD = 17.30 and RMSE = 0.021. In addition, the proposed method has important features such as being independent of the sampling conditions, being a single minimum in the global sense, and not requiring additional algorithms (preprocessing of the ECG signal). As a result, SVR based compression algorithm is an attractive candidate for compressing ECG signals.

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Section: Gi̇ri̇ş (Introduction)unclassified

EKG verilerinin destek vektör regresyon yöntemiyle sıkıştırılması

Karal¹

2018

Gazi Üniversitesi Mühendislik Mimarlık Fakültesi Dergisi

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“…Many tools are available for the efficient compression of ECG signal such as Fourier transform, Discrete cosine transform, Wavelet transform. Wavelet transform is one of the promising tool for the ECG compression [2]. It has received significant attention recently due to it's performance in ECG compression [3].…”

Section: Introductionmentioning

confidence: 99%

Comparison and Analysis of Orthogonal and Biorthogonal Wavelets for Ecg Comprssion

.¹

2014

IJRET

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In the present work we analyze the performance of orthogonal and Biorthogonal wavelet for electrocardiogram (ECG) compression. Both types of wavelets applied on different types of records taken from MIT-BIH database. A thresholding algorithm based on energy packing efficiency of the wavelet coefficients is used to threshold the wavelet coefficients. We divide wavelet coefficients in two groups approximation coefficients group and detail coefficients group then, select an appropriate threshold value for each detail sub band according to the energy packing efficiency of the each sub band . We have implemented different types of wavelets for the compression of the ECG signal and compared their performance. We have evaluated the effect of decomposition levels on compression ratio (CR) and preserved energy. We measured CR and percentage root mean square difference (PRD) and it is found that Bi orthogonal wavelet performs well than the orthogonal ones in both the criteria.

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“…Thus original samples of ECG are subjected to a transformation and the compression is performed in the entirely new domain like Fourier transform (FT), DCT and wavelet etc [8,12,16,28,31]. These techniques pose higher CR than direct techniques and are insensitive to noise present in ECG signals.…”

Section: Transform-domain Techniquesmentioning

confidence: 99%

“…With FT the frequency-amplitude representation of the signal is obtained [8,12]. To reconstruct the signal inverse FFT is applied.…”

Section: Ft Domainmentioning

confidence: 99%

“…ECG signal compression techniques widely fall into three categories of direct method, transformation method and parameter extraction method [7,8]. The direct data compression method openly analyzes and reduces data points in the time domain and the example includes turning point (TP) [25], amplitude zone time epoch coding (AZTEC) [3], Improved modified AZTEC technique [9], coordinate reduction time encoding system (CORTES) [48], SLOPE [10], the delta algorithm and the Fan algorithm [11].…”

Section: Fig 1: Time Intervals Of Ecgmentioning

confidence: 99%

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A Review of ECG Data Compression Techniques

Singh¹,

Kaur²,

Singh³

2015

IJCA

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Electrocardiogram (ECG) data compression reduced the storage requirements to develop a more efficient telecardiology system for cardiac analysis and diagnosis. The ECG compression without loss of diagnostic information is based on the fact that consecutive samples of the digitized ECG carry redundant information that can be removed with very less computing effort. This paper focuses on providing a comparison of the major techniques (direct, transform, parameter extraction and 2D approaches) of ECG data compression which are intended to attain a lossless compressed data with relatively high compression ratio (CR) and low percent root mean square difference (PRD).The paper concludes with the presentation of a framework for evaluation and comparison of ECG compression schemes.

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The comparison of different transform based methods for ECG data compression

Cited by 27 publications

References 8 publications

EKG verilerinin destek vektör regresyon yöntemiyle sıkıştırılması

EKG verilerinin destek vektör regresyon yöntemiyle sıkıştırılması

Comparison and Analysis of Orthogonal and Biorthogonal Wavelets for Ecg Comprssion

A Review of ECG Data Compression Techniques

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