Detection of Atrial Fibrillation using 12-lead ECG for Mobile Applications
Authors
Abstract
Atrial Fibrillation (AF) is the most common arrhythmia and is associated with an increased risk of heartrelated deaths and the development of conditions such as heart failure, dementia, and stroke. Affecting mostly elderly people, AF is associated with high comorbidity, increased mortality and is a major socio-economic impact in our society. Therefore, the detection of AF episodes in personalized health (p-Health) environments can be decisive in the prevention of major cardiac threats and in the reduction of health care costs.In this paper we present a new algorithm for detection of AF based on the assessment of the three main physiological characteristics of AF: 1) the irregularity of the heart rate; 2) the absence of the P-wave and 3) the presence of fibrillatory waves. Several features were extracted from the analysis of 12-lead electrocardiogram (ECG) signals, the best features were selected and a support vector machine classification model was adopted to discriminate AF and non-AF episodes. Our results show that the inclusion of features from the analysis of the recovered atrial activity was able to increase the performance of the algorithm: sensitivity of 88.5 % and specificity of 92.9%.
In the WELCOME project it is being designed a novel light vest with an integrated sensor system that collects several signals, including 12-lead ECG signals. The proposed algorithm is currently integrated in the WELCOME feature extraction module, which is responsible for receiving raw signals, extraction higher level features (e.g. occurrence of AF episodes) and provide them to the clinical decision process.