Atrial Fibrillation Stratification Via Super-Resolution Analysis of Fibrillatory Waves

Saumitra Mishra, Sreehari Rammohan, K. Rajab, G. Dhillon, P. Lambiase, R. Hunter, E. Chew
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引用次数: 1

Abstract

We use the Filter Diagonalization Method (FDM), a harmonic inversion technique, to extract f-wave features in electrocardiographic (ECG) traces for atrial fibrillation (AF) stratification. The FDM detects f-wave frequencies and amplitudes at frame sizes of 0.15 seconds. We demonstrate our method on a dataset comprising of ECG recordings from 23 patients (61.65 ± 11.63 years, 78.26% male) before cryoablation; 2 paroxysmal AF, 16 early persistent AF (<12 months duration), and 4 longstanding persistent AF (>12 months duration). Moreover, some of these patients received adenosine to enhance their RR intervals before ablation. Our method extracts features from FDM outputs to train statistical machine learning classifiers. Tenfold cross-validation demonstrates that the Random Forest and Decision Tree models performed best for the pre-ablation without and with adenosine datasets, with accuracy 60.89 ± 0.31% and 59.58% ± 0.04%, respectively. While the results are modest, they demonstrate that f-wave features can be used for AF stratification. The accuracies are similar for the two tests, slightly better for the case without adenosine, showing that the FDM can successfully model short f-waves without the need to concatenate f-wave sequences or adenosine to elongate RR intervals.
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通过超分辨率分析心房颤动波分层
我们使用滤波对角化方法(FDM),一种谐波反演技术,提取心房颤动(AF)分层的心电图(ECG)痕迹中的f波特征。FDM检测帧大小为0.15秒的f波频率和幅度。我们在包含23例患者(61.65±11.63岁,78.26%男性)冷冻消融前心电图记录的数据集上展示了我们的方法;阵发性房颤2例,早期持续性房颤16例(病程12个月)。此外,其中一些患者在消融前接受腺苷治疗以提高RR间期。我们的方法从FDM输出中提取特征来训练统计机器学习分类器。十倍交叉验证表明,随机森林和决策树模型在无腺苷和有腺苷数据集的预消融中表现最好,准确率分别为60.89±0.31%和59.58%±0.04%。虽然结果是适度的,但它们表明f波特征可以用于AF分层。两种测试的准确性相似,在不含腺苷的情况下稍微好一些,这表明FDM可以成功地模拟短f波,而不需要连接f波序列或腺苷来延长RR间隔。
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