Conference Paper

Detection of Atrial Fibrillation Using Model-based ECG Analysis

Centre for Inf. & Syst., Univ. of Coimbra, Coimbra
DOI: 10.1109/ICPR.2008.4761755 Conference: Pattern Recognition, 2008. ICPR 2008. 19th International Conference on
Source: IEEE Xplore


Atrial fibrillation (AF) is an arrhythmia that can lead to several patient risks. This kind of arrhythmia affects mostly elderly people, in particular those who suffer from heart failure (one of the main causes of hospitalization). Thus, detection of AF becomes decisive in the prevention of cardiac threats. In this paper an algorithm for AF detection based on a novel algorithm architecture and feature extraction methods is proposed. The aforementioned architecture is based on the analysis of the three main physiological characteristics of AF: i) P wave absence ii) heart rate irregularity and iii) atrial activity (AA). Discriminative features are extracted using model-based statistic and frequency based approaches. Sensitivity and specificity results (respectively, 93.80% and 96.09% using the MIT-BIH AF database) show that the proposed algorithm is able to outperform state-of-the-art methods.

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Available from: Juscelino Henriques, Sep 29, 2015
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    • "Figura 1: Episódio de fibrilação atrial (à esquerda) e ciclo cardíaco (à direita) [6] Alguns sintomas levam a pessoa com fibrilação atrial a procurar o pronto socorro como, palpitações, cansaço repentino, tontura, incapacidade de realizar esforços habituais causada por dispneia (desconforto para respirar) [7]. E apesar desses sintomas a pessoa pode viver meses ou mesmo anos com a doença, mas com eficiência do bombeamento cardíaco global reduzida o que a longo prazo pode acarretar complicações, como o tromboembolismo sistêmico, formação de coágulos no coração que se desprendem e levam ao entupimento das artérias em diversas partes do corpo. "
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    ABSTRACT: Resumo: O artigo visa estudar e desenvolver um algoritmo que faça a detecção da fibrilação atrial a partir de sinais de eletrocardiograma. A fibrilação atrial é uma arritmia cardíaca muito comum onde são observados impulsos descontrolados nos átrios o que gera múltiplas frentes de onda vagando na região atrial do coração com padrões de propagação diferentes. A ideia é detectar a doença através de sinais eletrocardiográficos do banco de dados Physionet utilizando um método que analisa a irregularidade dos intervalos RR. Palavras-chave: Fibrilação Atrial, Detecção da fibrilação atrial, ECG, intervalos RR. Abstract: The paper aims to study and develop an algorithm that makes the detection of atrial fibrillation from the electrocardiogram signals. Atrial fibrillation is a common cardiac arrhythmia where uncontrolled impulses in the atria are observed. These impulses generate multiple wavefronts wandering in the atrial region of the heart with different propagation patterns. The idea is to detect the disease by electrocardiographic bank Physionet data using a method that analyzes the irregularity of RR intervals.
    XXIV Brazilian Congress on Biomedical Engineering, Uberlândia; 10/2014
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    • "Hence, it could be an effective way to investigate the presence of AF and to detect when a single event starts and ends. Several examples exist in the literature (see [13] [14] [15] [16]), which are focused on the peculiar variance of RR intervals during the AF process, and this variance is much greater than the one during the physiological heartbeat. Anyway, in many situations, an AF event does not follow a physiological time slot but comes after other types of arrythmia. "
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    ABSTRACT: Atrial Fibrillation (AF) is the most common cardiac arrhythmia. It naturally tends to become a chronic condition, and chronic Atrial Fibrillation leads to an increase in the risk of death. The study of the electrocardiographic signal, and in particular of the tachogram series, is an usual and eff�ective way to investigate the presence of Atrial Fibrillation and to detect when a single event starts and ends. This work presents a new statistical method to deal with the identi�cation of Atrial Fibrillation events, based on the order identi�cation of the ARIMA models used for describing the RR time series that characterize the di�erent phases of AF (pre-, during and post- AF). A simulation study is carried out in order to assess the performance of the proposed method. Moreover, an application to real data concerning patients a�ected by Atrial Fibrillation is presented and discussed. Since the proposed method looks at structural changes of ARIMA models �tted on the RR time series for the AF event with respect to the pre- and post- AF phases, it is able to identify starting and ending points of an AF event even when AF follows or comes before irregular heartbeat time slots.
    Computational and Mathematical Methods in Medicine 04/2013; 2013. DOI:10.1155/2013/373401 · 0.77 Impact Factor
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    • "FSA) 40 87.27 95.47 92.75 7.80 Couceiro et al. [14] "
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    ABSTRACT: Automatic detection of Atrial Fibrillation (AF) is necessary for the long-term monitoring of patients who are suspected to have AF. Several methods for AF detection exist in the literature. These methods are mainly based on two different characteristics of AF ECGs: the irregularity of RR intervals (RRI) and the fibrillatory electrical Atrial Activity (AA). The electrical AA is characterized by the absence of the P-wave (PWA) and special frequency properties (FSA). Nine AF detection algorithms were selected from literature and evaluated with the same protocol in order to study their performance under different conditions. Results showed that the highest sensitivity (Se=97.64%) and specificity (Sp=96.08%) was achieved with methods based on analysis of irregularity of RR interval, while combining RR and atrial activity analysis gave the highest positive predictive value (PPV=92.75%). Algorithms based on RR irregularity were also the most robust against noise (Se=85.79% and Sp=81.90% for SNR=0dB; and Se=82.52% and Sp=40.47% for SNR=-5dB).
    Computing in Cardiology, 2011; 01/2011
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