Discrete Wavelet Transform with Enhancement Filter for ECG Signal

Daqrouq, Khaled; Qawasmi, Abd Alrahman; Alfaouri, Mikhled

doi:10.26634/jse.4.3.1113

Cited by 5 publications

(6 citation statements)

References 6 publications

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“…1). In the experiments three types of arrhythmias were used for algorithm testing: atrial fibrillation AF, normal sinus rhythm NSR and congestive heart failure CHF as seen in figure (2) and Figure (3) In the next experiment Table (2), several methods were investigated for comparison. Shannon entropy with probabilistic neural network (SEPNN) [14], fast Fourier transform and FFBPN (FFTNN) [4], time-frequency and FFBPN (TFNN) [8], were studied for comparison.…”

Section: Resultsmentioning

confidence: 99%

“…• Pre-processing: Before the signal is given to the feature extraction unit, the ECG signals is to be pre-processed and normalized to remove probable drift of baseline [3], interferences, noises [2], etc. 8) where 𝑅 − 𝑅 1 is time interval between two subsequent R waves, 𝑃 is time interval between two subsequent 𝑃 waves, 𝑇 is time interval between two subsequent 𝑇 waves, 𝑚 is the mean value, and 𝑀𝑎𝑥, 𝑀𝑖𝑛 are the maximum and minimum, respectively.…”

Section: Methodsmentioning

confidence: 99%

“…These complications confuse the ECG and disease recognition. Hence, employment of sample classifier procedures may suggest an enhanced an ECG arrhythmia judgment [1][2][3]. For instant, Atrial fibrillation (AF) has in current era been the subject of deep study in framework of attaining a superior understanding of its system and refining its running.…”

Section: Introductionmentioning

confidence: 99%

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ECG Arrhythmias Classification by Combined Feature Extraction Method and Neural Network

Azzawi¹

2014

ETJ

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Electrocardiogram (ECG) became one of the most crucial tool for heart status diagnosis. Generally, several arrhythmias may appear based on different heart rate or ECG signal morphology variation. In this paper, a novel combined feature extraction method to present ECG arrhythmias is proposed. The combination between Wavelet Packet Transform (WPT) entropies and Power Spectrum Density (PSD) is suggested. For classification, Feed Forward Backpropagation Neural Network (FFBPN) is utilized. The experimental results showed that the proposed method can be beneficial for ECG signal arrhythmias classification. MIT-BIH Arrhythmia Database was used for algorithm testing. The proposed method was compared with three state of art methods, where was of better performance reached about 80%. The proposed method as well as other methods was tested in noisy environment for comparison investigations. The suggested method is promising approach for arrhythmias classification. However, enormous testing data set might significantly improve the results.

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Section: Resultsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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ECG Arrhythmias Classification by Combined Feature Extraction Method and Neural Network

Azzawi¹

2014

ETJ

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“…[4][5][6][7]. In Gokhale, 8 the wavelet was used to remove the power line interference from ECG signals.…”

Section: Introductionmentioning

confidence: 99%

“…There are different techniques available for use in denoising ECG signals, such as wavelet, [4][5][6][7][8][9] adaptive filtering, [11][12][13][14] empirical mode decomposition (EMD) 15 16 and more.…”

Section: Introductionmentioning

confidence: 99%

Denoising Autoencoder for Eletrocardiogram Signal Enhancement

Xiong¹,

Wang²,

Liu³

et al. 2015

j med imaging hlth inform

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Eletrocardiogram (ECG) is a useful diagnostic method to detect electrical signals of both healthy or diseased hearts. However, ECG signals are often contaminated by various types of noise, such as baseline wander, electrode contact noise, and motion artifacts. A Denoising Autoencoder (DAE) is selected to enhance ECG signal in this paper. The ECG signals used for DAE training is initially corrupted by means of a stochastic mapping and then reconstructed to the initial uncorrupted signals. DAEs are stacked to build deep architecture, which will improve the expression with multi-level feature extraction. The method is evaluated on ECG signals from the MIT-BIH Arrthythmia Database. MIT-BIH Noise Stress Test Database was used for generating noise. Computer simulation results show that the proposed system performed better than typically used ECG denoising methods with significant improvement on signal to noise ratio (SNR) and root mean square error (RMSE).

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