Physiologically Motivated Detection of Atrial Fibrillation



Atrial Fibrillation (AF) is the most common
arrhythmia and it is estimated to affect 33.5 million people
worldwide. AF is associated with an increased risk of mortality
and morbidity, such as heart failure and stroke and affects
mostly older persons and persons with other conditions (e.g.
heart failure and coronary artery disease). In order to prevent
such life threatening and life quality reducing conditions it is
essential to provide better algorithms, capable of being
integrated in low-cost personalized health systems.
This paper presents a new algorithm for AF detection, which
is based on the analysis of the three physiological
characteristics of AF: 1) Irregularity of heart rate and; 2)
Absence of P-waves and 3) Presence of fibrillatory waves.
Based on these characteristics several features were extracted
from 12-lead electrocardiograms (ECG) and selected according
to their discrimination ability. The classification between AF
and non-AF episodes was performed using an SVM
classification model.
Our results show that the identification of the fibrillatory
patterns, using the proposed features, extracted from the
analysis of 12-lead ECG improves the performance of the
algorithm to a sensitivity of 88.5% and specificity 92.9%, when
compared to our previous single-channel approach.
The proposed algorithm is currently integrated in the
feature extraction module that is being developed under the
WELCOME project.

Related Project

WELCOME - Wearable Sensing and Smart Cloud Computing for Integrated Care to COPD Patients with Comorbidities


39th Int. Conf. of the IEEE Engineering in Medicine and Biology Society – EMBC’2017, July 2017

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