An Efficient Method of ECG Signals Denoising Based on an Adaptive Algorithm Using Mean Filter and an Adaptive Dual Threshold Filter

Jenkal, Wissam; Latif, Rachid; Toumanari, Ahmed; Dliou, Azzedine; B’charri, Oussama El

doi:10.15866/irecos.v10i11.7821

Cited by 11 publications

(13 citation statements)

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“…α is essentially used to adapt the thresholding. According to the filtering process, α varies between 0% and 100%, lower values are recommended for a high concentration of noise, and higher values are tolerated in the opposite case [7].…”

Section: The Adtf Techniquementioning

confidence: 99%

“…The window selection is not arbitrary; it's used to compare the median sample to his left and right regions, as stated in [7], a window of five samples gives the best results in terms of MSE and SNRinput. Fig.…”

Section: The Adtf Techniquementioning

confidence: 99%

“…To simplify the interpretation task, several processing and filtering algorithms are proposed in the literature [2][3][4][5][6]. Digital Filters (FIR and IIR), Empirical Mode Decomposition (EMD), Wavelet Transform denoising techniques as Discrete Wavelet Transform (DWT) and the Adaptive Dual Threshold Filter (ADTF) [7][8].…”

Section: Introductionmentioning

confidence: 99%

“…The EMD and the DWT give satisfying results, but they are characterized by their algorithmic complexity that requires more hardware resources and important computation time [10][11]. The ADTF proposed by W. Jenkal et al in [7] has a great denoising capacity, especially when the signal is mixed with the highfrequency noises. The advantage of this technique is the very low complexity.…”

Section: Introductionmentioning

confidence: 99%

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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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Section: The Adtf Techniquementioning

confidence: 99%

Section: The Adtf Techniquementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

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

Mejhoudi¹,

Latif²,

Saddik³

et al. 2021

IJACSA

View full text Add to dashboard Cite

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“…This signal is recorded using the electrodes placed on the surface of the patient's skin. The theoretical and the practical basis of the ECG recording were set out by Einthoven in 1901 [1][2]. The ECG signal contains different waves and segments .e.g.…”

Section: Introductionmentioning

confidence: 99%

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

B’charri¹,

Latif²,

Jenkal³

et al. 2016

ijacsa

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Abstract-The storage capacity of the ECG records presents an important issue in the medical practices. These data could contain hours of recording, which needs a large space for storage to save these records. The compression of the ECG signal is widely used to deal with this issue. The problem with this process is the possibility of losing some important features of the ECG signal. This loss could influence negatively the analyzing of the heart condition. In this paper, we shall propose an efficient method of the ECG signal compression using the discrete wavelet transform and the run length encoding. This method is based on the decomposition of the ECG signal, the thresholding stage and the encoding of the final data. This method is tested on some of the MIT-BIH arrhythmia signals from the international database Physionet. This method shows high performances comparing to other methods recently published.

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