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Screening for paroxysmal atrial fibrillation using atrial premature contractions and spectral measures

机译:使用房性早搏和频谱测量筛查阵发性房颤

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We present a technique for screening for imminent onset of paroxysmal atrial fibrillation (PAF) through automated assessment of 30-minute segments of electrocardiogram (ECG), which do not contain any episodes of atrial fibrillation. Algorithmic development was carried out using a training database of 75 half-hour records drawn from two subject groups. Subjects in the first group provided segments with PAF in the five minutes after the 30-minute recording; the second group do not have PAF (control subjects or subjects with non-PAF cardiac pathology). To differentiate between pre-PAF segments and non-PAF segments a linear discriminant classifier was developed, using the number of Atrial Premature Contractions (APCs) and two spectral measures as features. An independent test set of 72 recordings (28 pre-PAF and 44 non-PAF) was then classified, with an accuracy of 75% (sensitivity 79%, specificity 72%). When tested against a second database of subjects with no known cardiac pathology, the specificity rose to 95%.
机译:我们提出了一种通过对心电图(ECG)的30分钟片段进行自动评估来筛查阵发性房颤(PAF)即将发作的技术,该技术不包含任何房颤发作。算法的开发是使用训练数据库进行的,该数据库包含从两个主题组中提取的75个半小时记录。记录30分钟后的五分钟内,第一组的受试者提供了PAF片段;第二组没有PAF(对照组或患有非PAF心脏病理的受试者)。为了区分前PAF段和非PAF段,开发了一种线性判别式分类器,以房性早搏(APC)的数量和两个频谱测量为特征。然后,对一个独立的测试集进行了分类,其中包含72个记录(28个PAF之前的记录和44个非PAF),准确度为75%(灵敏度为79%,特异性为72%)。当针对没有已知心脏病理学的受试者的第二个数据库进行测试时,特异性上升到95%。

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