Multi-Dynamics Analysis of QRS Complex for Atrial Fibrillation Diagnosis - Aix-Marseille Université Access content directly
Conference Papers Year : 2018

Multi-Dynamics Analysis of QRS Complex for Atrial Fibrillation Diagnosis

Abstract

This paper presents an effective atrial fibrillation (AF) diagnosis algorithm based on multi-dynamics analysis of QRS complex. The idea behind this approach is to produce a variety of heartbeat time series features employing several linear and nonlinear functions via different dynamics of the QRS complex signal. These extracted features from these dynamics will be connected through machine learning based algorithms such as Support Vector Machine (SVM) and Multiple Kernel Learning (MKL), to detect AF episode occurrences. The reported performances of these methods were evaluated on the Long-Term AF Database which includes 84 of 24-hour ECG recording. Thereafter, each record was divided into consecutive intervals of one-minute segments to feed the classifier models. The obtained sensitivity, specificity and positive classification using SVM were 96.54%, 99.69%, and 99.62%, respectively, and for MKL they reached 95.47%, 99.89%, and 99.87%, respectively. Therefore, these medical-oriented detectors can be clinically valuable to healthcare professional for screening AF pathology.
Fichier principal
Vignette du fichier
TRARDI_CoDIT2018.pdf (482.53 Ko) Télécharger le fichier
Origin : Files produced by the author(s)
Loading...

Dates and versions

hal-01893141 , version 1 (11-10-2018)

Identifiers

Cite

Y. Trardi, Bouchra Ananou, Z. Haddi, M. Ouladsine. Multi-Dynamics Analysis of QRS Complex for Atrial Fibrillation Diagnosis. 2018 5th International Conference on Control, Decision and Information Technologies (CoDIT), Apr 2018, Thessaloniki, France. ⟨10.1109/CoDIT.2018.8394935⟩. ⟨hal-01893141⟩
256 View
163 Download

Altmetric

Share

Gmail Facebook X LinkedIn More