Deep Feature Learning for Sudden Cardiac Arrest Detection in Automated External Defibrillators
Deep Feature Learning for Sudden Cardiac Arrest Detection in Automated External Defibrillators
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DOI：
10.1038/s41598-018-33424-9
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发表时间：
2018-11-21
影响因子：
4.6
通讯作者：
Kim, Kiseon
中科院分区：
文献类型：
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作者：
Minh Tuan Nguyen;Binh Van Nguyen;Kim, Kiseon
心室颤动和室性心动过速（VF/VT），即可电击（SH）节律，是心脏骤停（SCA）的主要原因，自动体外除颤器（AED）能有效地治疗SCA。最近，通过使用机器学习技术和各种传统特征，应用于AED的电击建议算法（SAA）的性能得到了改善。本文提出了一种新的具有较高性能的心电信号SCA检测算法。该算法由卷积神经网络作为特征提取器（CNNE）和Boosting（BS）分类器组成。网格搜索与嵌套的5倍交叉验证（CV）是用来选择CNNE训练与预处理的ECG，SH，和NSH信号，使用修改的变分模式分解技术。该CNNE学习的深度特征向量在第一个全连接层提取，然后输入BS分类器，使用5倍CV过程验证其性能。BS分类器的二次学习和CNNE的三个输入通道的使用确实提高了所提出的SAA的检测性能，验证的准确性为99.26%，灵敏度为97.07%，特异性为99.44%。
Ventricular fibrillation and ventricular tachycardia (VF/VT), known as shockable (SH) rhythms, are the mainly cause of sudden cardiac arrests (SCA), which is cured efficiently by the automated external defibrillator (AED). The performance of the shock advice algorithm (SAA) applied in the AED has been improved by using machine learning technique and variously conventional features, recently. In this paper, we propose a novel algorithm with relatively high performance for the SCA detection on electrocardiogram (ECG) signal. The algorithm consists of a convolutional neural network as a feature extractor (CNNE) and a Boosting (BS) classifier. A grid search with nested 5-folds cross validation (CV) is used to select the CNNE trained with preprocessed ECG, SH, and NSH signals using the modified variational mode decomposition technique. The deep feature vector learned by this CNNE is extracted at the first fully connected layer and then fed into BS classifier to validate its performance using 5-folds CV procedure. The secondary learning of the BS classifier and the use of three input channels for the CNNE improve certainly the detection performance of the proposed SAA with the validated accuracy of 99.26%, sensitivity of 97.07%, and specificity of 99.44%.