Wavelet transform in electrocardiography—data compression

Provazník, Ivo; Kozumplík, J

doi:10.1016/s1386-5056(97)00040-3

Cited by 21 publications

(8 citation statements)

References 10 publications

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“…However, the existing literature is not consistent in the use of the terms CR and CF. In some sources, the authors use the term CR for ( 3 ) rather than CF ([ 5 , 6 , 13 – 21 ]), while in others ([ 22 ]), the CR is defined differently, that is, ( 2 ). Equations ( 1 ) and ( 3 ) below come from [ 23 ].…”

Section: Known Methods For Evaluation Of Compression Efficiencymentioning

confidence: 99%

A Comparative Analysis of Methods for Evaluation of ECG Signal Quality after Compression

Němcová

Smíšek

Maršánová

et al. 2018

BioMed Research International

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The assessment of ECG signal quality after compression is an essential part of the compression process. Compression facilitates the signal archiving, speeds up signal transmission, and reduces the energy consumption. Conversely, lossy compression distorts the signals. Therefore, it is necessary to express the compression performance through both compression efficiency and signal quality. This paper provides an overview of objective algorithms for the assessment of both ECG signal quality after compression and compression efficiency. In this area, there is a lack of standardization, and there is no extensive review as such. 40 methods were tested in terms of their suitability for quality assessment. For this purpose, the whole CSE database was used. The tested signals were compressed using an algorithm based on SPIHT with varying efficiency. As a reference, compressed signals were manually assessed by two experts and classified into three quality groups. Owing to the experts' classification, we determined corresponding ranges of selected quality evaluation methods' values. The suitability of the methods for quality assessment was evaluated based on five criteria. For the assessment of ECG signal quality after compression, we recommend using a combination of these methods: PSim SDNN, QS, SNR1, MSE, PRDN1, MAX, STDERR, and WEDD SWT.

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Section: Known Methods For Evaluation Of Compression Efficiencymentioning

confidence: 99%

A Comparative Analysis of Methods for Evaluation of ECG Signal Quality after Compression

Němcová

Smíšek

Maršánová

et al. 2018

BioMed Research International

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“…For many years, the wavelet techniques have proved their efficiency for ECG detection [17], de-noising [18], compression [19,20] and QRS detection [21]. These techniques seem to be well suited cases where there is spectral overlapping between noise and desired components of signal.…”

Section: Introductionmentioning

confidence: 99%

Removal of ECG interference from surface respiratory electromyography

Slim

Raoof

2010

IRBM

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“…Recently, the wavelet theory for denoising [10] has been widely exploited in noisy ECG filtering. Several wavelet denoising ECG signal algorithms were developed exploring each a particular parameter: the wavelet function, threshold calculus and level decomposition [11][12][13]. The wavelet packet method is a generalization of wavelet decomposition that offers a rich range of possibilities for signal analysis.…”

Section: Introductionmentioning

confidence: 99%

DWT based beat rate detection in ECG analysis

Joshi

Ghule²

2010

Proceedings of the International Conference and Workshop on Emerging Trends in Technology

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Electrocardiogram (ECG) classification systems have the potential to benefit from the inclusion of the automated measurement capabilities. The first stage in computerized processing of the ECG is beat detection. The accuracy of the beat detector is very important for the overall system performance hence there is a benefit in improving the accuracy. In present study we introduce the concept of Discrete Wavelet Transform which is suitable for the non stationary ECG signals as it has adequate scale values and shifting in time. As baseline Wander and different types of noise elimination are considered as classical problem in ECG analysis we present a wavelet based search algorithm in different scales for Denoising and subtractive procedure to isolate baseline wander from noisy ECG signal(to remove the noise). This algorithm is tested using the data record from MIT-BIH database and excellent results are obtained.

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Wavelet transform in electrocardiography—data compression

Cited by 21 publications

References 10 publications

A Comparative Analysis of Methods for Evaluation of ECG Signal Quality after Compression

A Comparative Analysis of Methods for Evaluation of ECG Signal Quality after Compression

Removal of ECG interference from surface respiratory electromyography

DWT based beat rate detection in ECG analysis

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