Prediction of spontaneous ventricular tachyarrhythmia by an artificial neural network using parameters gleaned from short-term heart rate variability
Prediction of spontaneous ventricular tachyarrhythmia by an artificial neural network using parameters gleaned from short-term heart rate variability
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DOI：
10.1016/j.eswa.2011.09.097
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发表时间：
2012-02-15
影响因子：
8.5
通讯作者：
Huh, Soo-Jin
中科院分区：
文献类型：
--
作者：
Joo, Segyeong;Choi, Kee-Joon;Huh, Soo-Jin
减少心源性猝死造成的伤亡并预测室性心动过速（VIA）、室性心动过速（VT）或心室颤动（VF）是健康维护的关键问题。在本文中，我们提出了一种分类器，可以使用使用心率变异性 (HRV) 分析参数进行训练的人工神经网络 (ANN) 来预测 VTA 事件。使用自发性室性快速心律失常数据库（美敦力 1.0 版），其中包括 106 条 VT 前记录、26 条 VF 前记录和 126 条控制数据。每个数据集都经过预处理和参数提取。校正异位搏动后，裁剪每个事件 10 秒持续时间之前 5 分钟窗口中的数据以进行参数提取。随后进行时域和非线性参数的提取。提取参数数据库的三分之二用于训练人工神经网络，其余部分用于验证性能。开发了三个 ANN 对 VT、VF 和 VT + VF 信号进行分类，ANN 的灵敏度分别为 82.9%（特异性 71.4%）、88.9%（特异性 92.9%）和 77.3%（特异性 73.8%）。每个 ANN 的受试者工作特征 (ROC) 曲线下的归一化面积 (Azs) 分别为 0.75、0.93 和 0.76。 (C) 2011 Elsevier Ltd. 保留所有权利。
Reducing casualties due to sudden cardiac death and predicting ventricular tachyarrhythmia (VIA), ventricular tachycardia (VT) or ventricular fibrillation (VF), is a key issue in health maintenance. In this paper, we propose a classifier that can predict VTA events using artificial neural networks (ANNs) trained with parameters from heart rate variability (HRV) analysis. The Spontaneous Ventricular Tachyarrhythmia Database (Medtronic Version 1.0), comprising 106 pre-VT records, 26 pre-VF records, and 126 control data, was used. Each data set was subjected to preprocessing and parameter extraction. After correcting the ectopic beats, data in the 5 min window prior to the 10 s duration of each event was cropped for parameter extraction. Extraction of the time domain and non-linear parameters was performed subsequently. Two-thirds of the database of extracted parameters was used to train the ANNs, and the remainder was used to verify the performance. Three ANNs were developed to classify each of the VT, VF, and VT + VF signals, and the sensitivities of the ANNs were 82.9% (71.4% specificity), 88.9% (92.9% specificity), and 77.3% (73.8% specificity), respectively. The normalized areas (Azs) under the receiver operating characteristic (ROC) curve of each ANNs were 0.75, 0.93, and 0.76, respectively. (C) 2011 Elsevier Ltd. All rights reserved.