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首页> 外文期刊>PACE: Pacing and clinical electrophysiology >High accuracy of automatic detection of atrial fibrillation using wavelet transform of heart rate intervals.
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High accuracy of automatic detection of atrial fibrillation using wavelet transform of heart rate intervals.

机译:使用心率间隔的小波变换自动检测房颤的准确性很高。

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Permanent and paroxysmal AF is a risk factor for the occurrence and the recurrence of stroke, which can occur as its first manifestation. However, its automatic identification is still unsatisfactory. In this study, a new mathematical approach was evaluated to automate AF identification. A derivation set of 30 24-hour Holter recordings, 15 with chronic AF (CAF) and 15 with sinus rhythm (SR), allowed the authors to establish specific RR variability characteristics using wavelet and fractal analysis. Then, a validation set of 50 subjects was studied using these criteria, 19 with CAF, 16 with SR, and 15 with paroxysmal AF (PAF); and each QRS was classified as true or false sinus or AF beat. In the SR group, specificity reached 99.9%; in the CAF group, sensitivity reached 99.2%; in the PAF group, sensitivity reached 96.1%, and specificity 92.6%. However, classification on a patient basis provided a sensitivity of 100%. This new approach showed a high sensitivity and a high specificity for automatic AF detection, and could be used in screening for AF in large populations at risk.
机译:永久性和阵发性AF是中风发生和复发的危险因素,中风可能是其首发表现。但是,其自动识别仍然不能令人满意。在这项研究中,评估了一种新的数学方法来自动进行AF识别。推导的30个24小时动态心电图记录集,其中15个具有慢性AF(CAF),而15个具有窦性心律(SR),这使作者能够使用小波和分形分析建立特定的RR变异性特征。然后,使用这些标准研究了50名受试者的验证集,其中19名使用CAF,16名使用SR,以及15名使用阵发性AF(PAF)。每个QRS分为真假窦或AF搏动。在SR组中,特异性达到99.9%。在CAF组中,敏感性达到99.2%;在PAF组中,敏感性达到96.1%,特异性为92.6%。但是,按患者分类提供的灵敏度为100%。这种新方法显示了对自动AF检测的高灵敏度和高特异性,可用于筛查高危人群的AF。

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