QRS Detection Based on an Advanced Multilevel Algorithm

Jenkal, Wissam; Latif, Rachid; Toumanari, Ahmed; Dliou, Azzedine; B’charri, Oussama El; Maoulainine, F. M. R.

doi:10.14569/ijacsa.2016.070135

Cited by 7 publications

(4 citation statements)

References 20 publications

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“…The length of the moving window is 5 seconds that permits to compare every QRS to its neighbors and extract the majority of these complex. In addition, this length doesn't allow to have a large difference between the largest and smallest QRS in this window due to the behavior of the ECG signal, which changes gradually [20] . Unlike most papers, such as [1] , [21] , where they use a single value as the local threshold in each moving window, we here adopt dual adaptive threshold algorithm introduced by [22] .…”

Section: Qrs Events Detectionmentioning

confidence: 99%

A Real-time QRS Detector Based on Low-pass Differentiator and Hilbert Transform

Yang

Zhang

2018

MATEC Web Conf.

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Electrocardiogram(ECG) is an important physiological signal of the human body. It is widely used in identification and arrhythmia detection. The first step of ECG application is signal segmentation, that is, the QRS detection. An effective and real-time QRS detection algorithm is proposed in this paper. A differentiator with adjustable center frequency is used to capture the first derivative information of the frequency band of the electrocardiogram. Then Hilbert transform is used to generate the envelope of the first derivative. After that, a dual threshold method is introduced to decrease FP and FN. Finally, a more precise R wave position is determined based on derivative method. The detector is validated on MIT-BIH arrhythmia database. The result show that the proposed algorithm has a high Sensitivity of 99.87%, Specificity of 99.84%, and the detection error rate is 0.28%. The average execution time of a 30 minutes record is 2.45s.

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Section: Qrs Events Detectionmentioning

confidence: 99%

A Real-time QRS Detector Based on Low-pass Differentiator and Hilbert Transform

Yang

Zhang

2018

MATEC Web Conf.

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“…ECG is an essential element in the diagnosis and the detection of cardiovascular disease or also in the monitoring of patients [1]. However, it is often correlated with different types of noise, which generates a distortion of the signal and a loss of valuable information.…”

Section: Introductionmentioning

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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“…The purpose of an electrocardiogram (ECG) is to measure electrical potential changes in the heart over a specific time period. Particular cells in the heart produce electrical impulses that spread through the heart causing it to contract, thereby controlling the rate of the heart's beating and causing these changes in the electrical potentials [1][2][3][4]. In the ECG, these electrical changes are measured through electrodes positioned on the chest.…”

Section: Introductionmentioning

confidence: 99%

TLBO Optimization Algorithm Based-Type2 Fuzzy Adaptive Filter for ECG Signals Denoising

Ouali¹,

Ghanai²,

Chafaa³

2020

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A novel type2-fuzzy adaptive filter is presented, which uses the concepts of type2-fuzzy logic, for electrocardiogram signals denoising. Type2-fuzzy adaptive filter is an information processor where both numerical and linguistic information are used as input-output pairs and fuzzy if-then rules, respectively. The proposed approach is based on an iterative procedure to achieve acceptable information extraction in the case where the statistical characteristics of the input-output signals are unknown. The proposed filter is presented as a dual-layered feedback system. Each layer has different function, the first layer being the type2-fuzzy autoregressive filter model. The second layer being responsible for training the membership function parameters. The second layer adjusts the type2-fuzzy adaptive filter parameters by using a teaching learning-based optimization algorithm (TLBO), which will allow the reaching of the required signal reconstruction by decreasing the criterion function. The proposed filter is validated and evaluated through some experimentations using the MIT-BIH ECGs databases where various artifacts were added to the ECGs signals; these included real and artificial noise. For comparison purposes, both model and non-modelbased methods recently published are used. Furthermore, the effect of the proposed filter on the malformation of diagnostic features of the ECG was studied and compared with several benchmark schemes. The results show that the proposed method performs better denoising for all noise power levels and for a different criteria viewpoint.

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QRS Detection Based on an Advanced Multilevel Algorithm

Cited by 7 publications

References 20 publications

A Real-time QRS Detector Based on Low-pass Differentiator and Hilbert Transform

A Real-time QRS Detector Based on Low-pass Differentiator and Hilbert Transform

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

TLBO Optimization Algorithm Based-Type2 Fuzzy Adaptive Filter for ECG Signals Denoising

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