Automatic Classification of Heartbeats Using Wavelet Neural Network

Benali, R.; Reguig, F. Bereksi; Slimane, Zinedine Hadj

doi:10.1007/s10916-010-9551-7

Cited by 44 publications

(14 citation statements)

References 26 publications

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“…Recently, the wavelet neural networks (WNN) are used with great success in the heartbeat classification [55]. WNN is a classifier that combines classical neural networks and wavelet analysis.…”

Section: Neural Network Approachesmentioning

confidence: 99%

See 1 more Smart Citation

Ectopic beats detection and correction methods: A review

Dib

Reguig

2015

Biomedical Signal Processing and Control

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Section: Neural Network Approachesmentioning

confidence: 99%

“…[55], Benali et al employed the WNN to discriminate five different heartbeats including APC and PVC beats. Their developed classifier is based on the use of the Morlet wavelets basis as the activation functions.…”

Section: Neural Network Approachesmentioning

confidence: 99%

Ectopic beats detection and correction methods: A review

Dib

Reguig

2015

Biomedical Signal Processing and Control

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“…Niwas et al 2005 Classification In this work, Classification of ECG signal into normal beat and nine different arrhythmias. The overall accuracy was 99.02% of the proposed approach Benali et al 2012 Classification In this paper, automatic classification based on wavelet neural network can be considered as an effective tool for cardiac arrhythmias classification with high accuracy of over 98.78%. Wavelet Neural Network (WNN) The following classifiers are used in this work.…”

Section: Related Workmentioning

confidence: 96%

Big Data in Complex Systems

Hassanien¹,

Azar²,

Snåšel³

et al. 2015

Studies in Big Data

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The series "Studies in Big Data" (SBD) publishes new developments and advances in the various areas of Big Data-quickly and with a high quality. The intent is to cover the theory, research, development, and applications of Big Data, as embedded in the fields of engineering, computer science, physics, economics and life sciences. The books of the series refer to the analysis and understanding of large, complex, and/or distributed data sets generated from recent digital sources coming from sensors or other physical instruments as well as simulations, crowd sourcing, social networks or other internet transactions, such as emails or video click streams and other. The series contains monographs, lecture notes and edited volumes in Big Data spanning the areas of computational intelligence incl. neural networks, evolutionary computation, soft computing, fuzzy systems, as well as artificial intelligence, data mining, modern statistics and Operations research, as well as self-organizing systems. Of particular value to both the contributors and the readership are the short publication timeframe and the world-wide distribution, which enable both wide and rapid dissemination of research output.More information about this series at

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“…R-peak detection is the datum since all other features are extracted after the R-peak location [ 12 ]. Accurate R-peak detection is critical for arrhythmia diagnosis such as atrial premature contraction, tachycardia, and bradycardia [ 13 ]. Nevertheless, efficient R-peak extraction is still difficult in the dynamic and noisy environment due to the time-varying waveform morphology.…”

Section: Introductionmentioning

confidence: 99%

An Adaptive and Time-Efficient ECG R-Peak Detection Algorithm

Qin

Yue

et al. 2017

Journal of Healthcare Engineering

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R-peak detection is crucial in electrocardiogram (ECG) signal analysis. This study proposed an adaptive and time-efficient R-peak detection algorithm for ECG processing. First, wavelet multiresolution analysis was applied to enhance the ECG signal representation. Then, ECG was mirrored to convert large negative R-peaks to positive ones. After that, local maximums were calculated by the first-order forward differential approach and were truncated by the amplitude and time interval thresholds to locate the R-peaks. The algorithm performances, including detection accuracy and time consumption, were tested on the MIT-BIH arrhythmia database and the QT database. Experimental results showed that the proposed algorithm achieved mean sensitivity of 99.39%, positive predictivity of 99.49%, and accuracy of 98.89% on the MIT-BIH arrhythmia database and 99.83%, 99.90%, and 99.73%, respectively, on the QT database. By processing one ECG record, the mean time consumptions were 0.872 s and 0.763 s for the MIT-BIH arrhythmia database and QT database, respectively, yielding 30.6% and 32.9% of time reduction compared to the traditional Pan-Tompkins method.

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Automatic Classification of Heartbeats Using Wavelet Neural Network

Cited by 44 publications

References 26 publications

Ectopic beats detection and correction methods: A review

Ectopic beats detection and correction methods: A review

Big Data in Complex Systems

An Adaptive and Time-Efficient ECG R-Peak Detection Algorithm

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