Nonlinear statistical methods for atrial fibrillation detection on electrocardiogram

Mabrouki, Rebeh; Khaddoumi, Balkine; Sayadi, Mounir

doi:10.1109/cistem.2014.7349356

Cited by 5 publications

(3 citation statements)

References 14 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Apparently, the AFDB merged to a single surrogate recorded, similar to Babaeizadeh et al (2009). Despite their age, the proposed classifiers by (Dash et al, 2009;Mabrouki et al, 2014;Zhou et al, 2015) still perform well. However, all three use a relatively large window of 128 beats with all inherent disadvantages.…”

Section: Comparison With the Literaturementioning

confidence: 98%

See 1 more Smart Citation

Atrial fibrillation detection using heart rate variability and atrial activity: A hybrid approach

Hirsch

Jensen

Poulsen³

et al. 2021

Expert Systems with Applications

View full text Add to dashboard Cite

Section: Comparison With the Literaturementioning

confidence: 98%

“…The algorithm was evaluated on the AFDB and MIT-BIH Arrhythmia Database (ARDB). Characteristics of the Poincare plot and the SEn were used by Mabrouki et al (2014). The thresholds for the classification were optimized on the AFDB and tested on the ARDB and AFDB.…”

Section: Comparison With the Literaturementioning

confidence: 99%

Atrial fibrillation detection using heart rate variability and atrial activity: A hybrid approach

Hirsch

Jensen

Poulsen³

et al. 2021

Expert Systems with Applications

View full text Add to dashboard Cite

“…However, they all suffered from inherent disadvantages due to quick changing rhythms of ECG signals [10][11][12][13][14][15]. Different techniques have been used in an attempt to classify the ECG signals with a maximum possible accuracy [16][17][18][19][20].…”

Section: Introductionmentioning

confidence: 99%

Classification of Atrial Arrhythmias using Neural Networks

Utkarsh¹,

Pandey²,

Dubey³

et al. 2018

IJ-AI

View full text Add to dashboard Cite

Electrocardiogram (ECG) is an important tool used by clinicians for successful diagnosis and detection of Arrhythmias, like Atrial Fibrillation (AF) and Atrial Flutter (AFL). In this manuscript, an efficient technique of classifying atrial arrhythmias from Normal Sinus Rhythm (NSR) has been presented. Autoregressive Modelling has been used to capture the features of the ECG signal, which are then fed as inputs to the neural network for classification. The standard database available at Physionet Bank repository has been used for training, validation and testing of the model. Exhaustive experimental study has been carried out by extracting ECG samples of duration of 5 seconds, 10 seconds and 20 seconds. It provides an accuracy of 99% and 94.3% on training and test set respectively for 5 sec recordings. In 10 sec and 20 sec samples it shows 100% accuracy. Thus, the proposed method can be used to detect the arrhythmias in a small duration recordings with a fairly high accuracy.

show abstract