The ECG Signal Compression Using an Efficient Algorithm Based on the DWT

B’charri, Oussama El; Latif, Rachid; Jenkal, Wissam; Abenaou, Abdenbi

doi:10.14569/ijacsa.2016.070325

Cited by 10 publications

(5 citation statements)

References 19 publications

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“…Discrete Wavelet Transform (DWT) is a multi-resolution/multi-frequency representation [13] [14]. It makes it possible to efficiently analyze signals where phenomena of very different scales combine.…”

Section: Wavelet Transformmentioning

confidence: 99%

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New Ecg Signal Compression Model Based on Set Theory Applied to Images

Kabiena¹,

Djomadji²,

Tonyé³

2023

JCC

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Cardiovascular diseases are the origin of many causes of death worldwide. They impose on practitioners optimal diagnostic methods such as telemedicine in order to be able to quickly detect anomalies for daily care and monitoring of patients. The Electrocardiogram (ECG) is an examination that can detect abnormal functioning of the heart and generates a large number of digital data which can be stored or transmitted for further analysis. For storage or transmission purposes, one of the challenges is to reduce the space occupied by ECG signal and for that, it is important to offer more and more efficient algorithms capable of achieving high compression rates, while offering a good quality of reconstruction in a relatively short time. We propose in this paper a new ECG compression scheme that is based on a subset of signal splitting and 2D processing, the wavelet transform (DWT) and SPIHT coding which has proved their worth in the field of signal processing and compression. They are exploited for decorrelation and coding of the signal. The results obtained are significant and offer many perspectives.

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Section: Wavelet Transformmentioning

confidence: 99%

“…< ≤  (13) i and j being the lines and columns respectively ∑∑ representing an empirical sum in the sense of subsets and not arithmetic. Figure 6 presents the grouping into subsets of four blocks based on QRS, T, P and U.…”

Section: Compression Schemementioning

confidence: 99%

New Ecg Signal Compression Model Based on Set Theory Applied to Images

Kabiena¹,

Djomadji²,

Tonyé³

2023

JCC

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“…The biomedical engineering revolution forces researchers to enhance the automatic diagnosis by optimization of ECG processing algorithms in order to ensure real-time monitoring of cardiac data [12][13][14][15][16]; and their implementation on embedded systems as recent technological resources [8,[17][18][19].…”

Section: A Ecg Signal Processingmentioning

confidence: 99%

Speeding up an Adaptive Filter based ECG Signal Pre-processing on Embedded Architectures

Mejhoudi¹,

Latif²,

Saddik³

et al. 2021

IJACSA

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Medical applications increasingly require complex calculations with constraints of accelerated processing time. These applications are therefore oriented towards the integration of high-performance embedded architectures. In this context, the detection of cardiac abnormalities is a task that remains a high priority in emergency medicine. ECG analysis is a complex task that requires significant computing time since a large amount of information must be analyzed in parallel with high frequencies. Real-time processing is the biggest challenge for researchers, when talking about applications that require time constraints like that of cardiac activity monitoring. This work evaluates the Adaptive Dual Threshold Filter (ADTF) algorithm dedicated to ECG signal filtering using various embedded architectures: A Raspberry 3B+ and Odroid XU4. The implementation has been based on C/C++ and OpenMP to exploit the parallelism in the used architectures. The evaluation was validated using several ECG signals proposed in MIT-BIH Arrhythmia database with a sampling frequency of 360 Hz. Based on an algorithmic complexity study and a parallelization of the functional blocks which present significant workloads, the evaluation results show a mean execution time of 7.5 ms on the Raspberry 3B+ and 0.34 ms on the Odroid XU4. With an efficient parallelization on the Odroid XU4 architecture, real-time performance can be achieved.

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“…By applying wavelet transforms to ECG signals, the resulting coefficients can be analyzed and processed to identify and discard redundant or irrelevant information. In several studies such as [32][33][34][35], compression of ECG signals using wavelet transformation has been investigated. Other techniques such as adaptive Fourier transform, DCT, modified run-length encoding, empirical wavelet transform, and wavelet packet have also been proposed for ECG compression in various studies [15,[36][37][38][39][40][41].…”

Section: Introductionmentioning

confidence: 99%

Electrocardiography signal compression using non-decimated stationary wavelet transform-based technique

Sharma

Sunkaria

2023

Biomed. Phys. Eng. Express

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Background:In telecardiology, the bio-signal acquisition processing and communication for clinical purposes occupies larger storage and significant bandwidth over a communication channel. Electrocardiograph (ECG) compression with effective reproductivity is highly desired. In the present work, a compression technique for ECG signals with less distortion by using a non-decimated stationary wavelet with a run-length encoding scheme has been proposed.Method:In the present work non-decimated stationary wavelet transform (NSWT) method has been developed to compress the ECG signals. The signal is subdivided into N levels with different thresholding values. The wavelet coefficients having values larger than the threshold are evaluated and the remaining are suppressed. In the presented technique, the biorthogonal wavelet is employed as it improves the compression ratio as well percentage root means square ratio when compared to the existing method and exhibits better results. Then the coefficients are pre-processed by using the Savitzky-Golay filter which discards the corrupted signals. The wavelet coefficients are quantized using dead-zone quantization which removes the value close to zeros. To encode these values run length encoding scheme is applied and compressed ECG signals are obtained.Results:The presented methodology has been evaluated on the MIT-BIH arrhythmias database which contains 4800 ECG fragments from forty-eight clinical records. An average compression ratio = 33.12, PRD =1.99, NPRD =2.53, and QS = 16.57 is obtained in the proposed technique.Conclusion:The proposed technique exhibits a high compression ratio and reduces distortion compared to the existing method.

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The ECG Signal Compression Using an Efficient Algorithm Based on the DWT

Cited by 10 publications

References 19 publications

New Ecg Signal Compression Model Based on Set Theory Applied to Images

New Ecg Signal Compression Model Based on Set Theory Applied to Images

Speeding up an Adaptive Filter based ECG Signal Pre-processing on Embedded Architectures

Electrocardiography signal compression using non-decimated stationary wavelet transform-based technique

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