scholarly journals Application of Joint Notch Filtering and Wavelet Transform for Enhanced Powerline Interference Removal in Atrial Fibrillation Electrograms

2018 ◽  
Author(s):  
Miguel Martínez ◽  
Juan Ródenas ◽  
José J Rieta ◽  
Raul Alcaraz
Keyword(s):  
Atrial Fibrillation ◽  
Wavelet Transform ◽  
Powerline Interference ◽  
Notch Filtering

High Accuracy of Automatic Detection of Atrial Fibrillation Using Wavelet Transform of Heart Rate Intervals

2002 ◽  
Vol 25 (4) ◽  
pp. 457-462 ◽  
Author(s):  
DAVID DUVERNEY ◽  
JEAN-MICHEL GASPOZ ◽  
VINCENT PICHOT ◽  
FREDERIC ROCHE ◽  
RICHARD BRION ◽  
...  
Keyword(s):  
Atrial Fibrillation ◽  
Heart Rate ◽  
Wavelet Transform ◽  
Automatic Detection ◽  
High Accuracy

A novel wavelet-based filtering strategy to remove powerline interference from electrocardiograms with atrial fibrillation

2018 ◽  
Vol 39 (11) ◽  
pp. 115006 ◽  
Author(s):  
Manuel García ◽  
Miguel Martínez-Iniesta ◽  
Juan Ródenas ◽  
José J Rieta ◽  
Raúl Alcaraz
Keyword(s):  
Atrial Fibrillation ◽  
Powerline Interference

scholarly journals SS-SWT and SI-CNN: An Atrial Fibrillation Detection Framework for Time-Frequency ECG Signal

2020 ◽  
Vol 2020 ◽  
pp. 1-11
Author(s):  
Hongpo Zhang ◽  
Renke He ◽  
Honghua Dai ◽  
Mingliang Xu ◽  
Zongmin Wang
Keyword(s):  
Atrial Fibrillation ◽  
Wavelet Transform ◽  
Morbidity And Mortality ◽  
High Morbidity ◽  
Ecg Signal ◽  
Time Frequency ◽  
Ecg Signals ◽  
Convolution Process ◽  
The Time Domain ◽  
Atrial Fibrillation Detection

Atrial fibrillation is the most common arrhythmia and is associated with high morbidity and mortality from stroke, heart failure, myocardial infarction, and cerebral thrombosis. Effective and rapid detection of atrial fibrillation is critical to reducing morbidity and mortality in patients. Screening atrial fibrillation quickly and efficiently remains a challenging task. In this paper, we propose SS-SWT and SI-CNN: an atrial fibrillation detection framework for the time-frequency ECG signal. First, specific-scale stationary wavelet transform (SS-SWT) is used to decompose a 5-s ECG signal into 8 scales. We select specific scales of coefficients as valid time-frequency features and abandon the other coefficients. The selected coefficients are fed to the scale-independent convolutional neural network (SI-CNN) as a two-dimensional (2D) matrix. In SI-CNN, a convolution kernel specifically for the time-frequency characteristics of ECG signals is designed. During the convolution process, the independence between each scale of coefficient is preserved, and the time domain and the frequency domain characteristics of the ECG signal are effectively extracted, and finally the atrial fibrillation signal is quickly and accurately identified. In this study, experiments are performed using the MIT-BIH AFDB data in 5-s data segments. We achieve 99.03% sensitivity, 99.35% specificity, and 99.23% overall accuracy. The SS-SWT and SI-CNN we propose simplify the feature extraction step, effectively extracts the features of ECG, and reduces the feature redundancy that may be caused by wavelet transform. The results shows that the method can effectively detect atrial fibrillation signals and has potential in clinical application.

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