筛选阵发性心房颤动使用心房早搏和频谱措施

Brian Hickey, Conor Heneghan
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引用次数: 24

摘要

我们提出了一种技术筛选即将发作的阵发性心房颤动(PAF)通过自动评估30分钟段的心电图(ECG),其中不包含任何房颤发作。算法开发使用了从两个主题组抽取的75个半小时记录的训练数据库。第一组受试者在录制30分钟后的5分钟内提供PAF片段;第二组无PAF(对照组或非PAF心脏病理组)。为了区分前房颤段和非房颤段,开发了一种线性判别分类器,使用心房早搏(APCs)的数量和两种频谱测量作为特征。然后对72个记录(28个预paf和44个非paf)的独立测试集进行分类,准确率为75%(灵敏度79%,特异性72%)。当与没有已知心脏病理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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Screening for paroxysmal atrial fibrillation using atrial premature contractions and spectral measures
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%.
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