Atrial fibrillation detection by DFA and SDCST methods

Q4 Mathematics

Model Assisted Statistics and Applications Pub Date : 2021-08-27 DOI:10.3233/mas-210532

R. N. Vargas, Antônio C. P. Veiga, R. Linhares

引用次数: 0

Abstract

Many cardiac disorders were diagnosed by analyzing an electrocardiogram signal, in particular, atrial fibrillation. We join the SDCST method with the Detrended Fluctuation Analysis (DFA) and the backpropagation net to identify atrial fibrillation in one hundred ECG signals obtained from Physionet Challenge 2017 database. The accuracy of the proposed classifier parameter is 97% for the training set and 95% for the test set.

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DFA和SDCST方法检测心房颤动

许多心脏疾病是通过分析心电图信号来诊断的，特别是房颤。我们将SDCST方法与去趋势波动分析(DFA)和反向传播网络结合起来，对来自Physionet Challenge 2017数据库的100个心电信号进行心房颤动识别。所提出的分类器参数对训练集的准确率为97%，对测试集的准确率为95%。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Model Assisted Statistics and Applications Mathematics-Applied Mathematics

CiteScore

1.00

自引率

0.00%

发文量

期刊介绍： Model Assisted Statistics and Applications is a peer reviewed international journal. Model Assisted Statistics means an improvement of inference and analysis by use of correlated information, or an underlying theoretical or design model. This might be the design, adjustment, estimation, or analytical phase of statistical project. This information may be survey generated or coming from an independent source. Original papers in the field of sampling theory, econometrics, time-series, design of experiments, and multivariate analysis will be preferred. Papers of both applied and theoretical topics are acceptable.