Couceiro R, Henriques J, Paiva R P, Antunes M, Carvalho P
Annu Int Conf IEEE Eng Med Biol Soc. 2017 Jul;2017:1278-1281. doi: 10.1109/EMBC.2017.8037065.
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 a Support Vector Machine (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, in the same database.
心房颤动（AF）是最常见的心律失常，据估计全球有3350万人受其影响。AF与死亡率和发病率增加相关，如心力衰竭和中风，且主要影响老年人及患有其他疾病（如心力衰竭和冠状动脉疾病）的人。为了预防此类危及生命和降低生活质量的情况，提供能够集成到低成本个性化健康系统中的更好算法至关重要。本文提出了一种用于AF检测的新算法，该算法基于对AF的三个生理特征的分析：1）心率不规则；2）P波缺失；3）颤动波的存在。基于这些特征，从12导联心电图（ECG）中提取了几个特征，并根据其判别能力进行了选择。使用支持向量机（SVM）分类模型对AF和非AF发作进行分类。我们的结果表明，在同一数据库中，与我们之前的单通道方法相比，利用从12导联ECG分析中提取的所提出特征来识别颤动模式，可将算法性能提高到灵敏度为88.5%，特异性为92.9%。
