Multi-Dynamics Analysis of QRS Complex for Atrial Fibrillation Diagnosis

Trardi, Youssef; Ananou, Bouchra; Haddi, Z.; Ouladsine, Mustapha

doi:10.1109/codit.2018.8394935

Cited by 6 publications

(2 citation statements)

References 22 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Hence, PCA allows the reduction of multidimensional data to a lower dimensional approximation, while simplifying the interpretation of the data by the first two or three principal components (PC1, PC2, and PC3) in two or three dimensions and preserving most of the variance in the data [9,10]. RVM-a method first introduced by Tipping [11]-is a data-driven method with the Bayesian treatment of the support vector machine (SVM) pipeline [12,13] as defined by Equation 4. RVM is a Bayesian sparse kernel model that introduces a prior distribution over the model weights that are governed by a set of hyper-parameters [14].…”

Section: Pattern Recognition Methodsmentioning

confidence: 99%

An Efficient Pattern Recognition Kernel-Based Method for Atrial Fibrillation Diagnosis

Haddi¹,

Ananou²,

Trardi³

et al. 2018

2018 Computing in Cardiology Conference (CinC)

View full text Add to dashboard Cite

The aim of this work is to develop an efficient diagnosis method for atrial fibrillation (AF) arrhythmia based on inter-beat interval time series analysis and relevance vector machine (RVM) classifier. Automatic and fast AF diagnosis is still a major concern for the healthcare professional. Several algorithms based on univariate and multivariate analysis have been developed to detect AF. The published results do not show satisfactory detection accuracy especially for brief duration as short as one minute. Although RVM has been applied on tasks such as computer vision, natural language processing, speech recognition etc., this is the first attempt to adopt RVM for AF diagnosis. Four publicly-accessible sets of clinical data (AF Termination Challenge Database, MIT-BIH AF, Normal Sinus Rhythm RR Interval Database, and MIT-BIH Normal Sinus Rhythm Databases) were used for assessment. All time series were segmented in 1 min RR interval window and then three specific features were calculated. The RVM classifier was trained on 2000 randomly selected samples from the merged database. The results showed that the RVM model performed better than do existing algorithms, with 99.20% for both sensitivity and specificity.

show abstract

Section: Pattern Recognition Methodsmentioning

confidence: 99%

An Efficient Pattern Recognition Kernel-Based Method for Atrial Fibrillation Diagnosis

Haddi¹,

Ananou²,

Trardi³

et al. 2018

2018 Computing in Cardiology Conference (CinC)

View full text Add to dashboard Cite

show abstract

“…For this end, we focused in our previous works on the diagnosis of AF with machine learning (ML) methods. For instance, we conducted a multi-dynamics analysis of QRS complex with support vector machine (SVM) and multiple kernel learning (MKL) in Trardi et al, 3 which reached respective sensitivity values of 96.54% and 95.47%. Other works were mainly based on the extraction of different features from R-wave derivatives for automatic medical decision-making, especially for AF detection as in literature.…”

Section: Introductionmentioning

confidence: 99%

A Literature Review: ECG-Based Models for Arrhythmia Diagnosis Using Artificial Intelligence Techniques

Boulif

Ananou

Ouladsine

et al. 2023

Bioinform Biol Insights

View full text Add to dashboard Cite

In the health care and medical domain, it has been proven challenging to diagnose correctly many diseases with complicated and interferential symptoms, including arrhythmia. However, with the evolution of artificial intelligence (AI) techniques, the diagnosis and prognosis of arrhythmia became easier for the physicians and practitioners using only an electrocardiogram (ECG) examination. This review presents a synthesis of the studies conducted in the last 12 years to predict arrhythmia’s occurrence by classifying automatically different heartbeat rhythms. From a variety of research academic databases, 40 studies were selected to analyze, among which 29 of them applied deep learning methods (72.5%), 9 of them addressed the problem with machine learning methods (22.5%), and 2 of them combined both deep learning and machine learning to predict arrhythmia (5%). Indeed, the use of AI for arrhythmia diagnosis is emerging in literature, although there are some challenging issues, such as the explicability of the Deep Learning methods and the computational resources needed to achieve high performance. However, with the continuous development of cloud platforms and quantum calculation for AI, we can achieve a breakthrough in arrhythmia diagnosis.

show abstract