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首页> 外文期刊>Frontiers in Physiology >Early Detection of Sudden Cardiac Death by Using Ensemble Empirical Mode Decomposition-Based Entropy and Classical Linear Features From Heart Rate Variability Signals
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Early Detection of Sudden Cardiac Death by Using Ensemble Empirical Mode Decomposition-Based Entropy and Classical Linear Features From Heart Rate Variability Signals

机译：通过使用基于集成的经验模式分解的熵和来自心率变异信号的熵和经典线性特征，早期检测突然的心脏死亡

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Sudden cardiac death (SCD), which can deprive a person of life within minutes, is a destructive heart abnormality. Thus, providing early warning information for patients at risk of SCD, especially those outside hospitals, is essential. In this study, we investigated the performances of ensemble empirical mode decomposition (EEMD)-based entropy features on SCD identification. EEMD-based entropy features were obtained by using the following technology: (1) EEMD was performed on HRV beats to decompose them into intrinsic mode functions (IMFs), (2) five entropy parameters, namely Rényi entropy (RenEn), fuzzy entropy (FuEn), dispersion Entropy (DisEn), improved multiscale permutation entropy (IMPE), and Renyi distribution entropy(RdisEn), were computed from the first four IMFs obtained, which were named EEMD-based entropy features. Additionally, an automated scheme combining EEMD-based entropy and classical linear (time and frequency domains) features was proposed with the intention of detecting SCD early by analyzing 14 min (at seven successive intervals of 2 min) heart rate variability (HRV) in signals from a normal population and subjects at risk of SCD. Firstly, EEMD-based entropy and classical linear measurements were extracted from HRV beats, and then the integrated measurements were ranked by various methodologies, i.e., t -test, entropy, receiver-operating characteristics (ROC), Wilcoxon, and Bhattacharyya. Finally, these ranked features were fed into a k-Nearest Neighbor algorithm for classification. Compared with several state-of-the-art methods, the proposed scheme firstly predicted subjects at risk of SCD up to 14 min earlier with an accuracy of 96.1%, a sensitivity of 97.5%, and a specificity of 94.4% 14 min before SCD onset. The simulation results exhibited that EEMD-based entropy estimators showed significant difference between SCD patients and normal individuals and outperformed the classical linear estimators in SCD detection, the EEMD-based FuEn and IMPE indexes were particularly useful assessments for identification of patients at risk of SCD and can be used as novel indices to reveal the disorders of rhythm variations of the autonomic nervous system when affected by SCD.
机译：突然的心脏死亡（SCD），可以在几分钟内剥夺人生的人，是一种破坏性的心脏异常。因此，为有SCD的风险，特别是外部医院的患者提供预警信息至关重要。在本研究中，我们调查了基于SCD识别的集合经验模型分解（EEMD）的熵特征的性能。通过使用以下技术获得基于EEMD的熵特征：（1）在HRV节拍上进行EEMD，将它们分解为内在模式功能（IMF），（2）五个熵参数，即Rényi熵（Renen），模糊熵（ Fuen），分散熵（弱），改进的多尺度置换熵（IMPE）和renyi分布熵（RDisen）从获得的前四个IMF计算，其被命名为基于EEMD的熵特征。另外，提出了一种组合基于EEMD的熵和经典线性（时间和频域）特征的自动化方案，其目的是通过分析14分钟（以2分钟的七个连续间隔）的信号中的信号来自正常的人口和受到SCD风险的科目。首先，从HRV节拍中提取基于EEMD的熵和经典的线性测量，然后通过各种方法，即T -TEST，熵，接收器操作特征（ROC），WILCOXON和BHATTACHARYYA排序。最后，将这些排名特征馈入k最近邻域算法以进行分类。与若干最先进的方法相比，所提出的方案首先预测了SCD风险的受试者在早期的14分钟内，精度为96.1％，灵敏度为97.5％，并且在SCD之前的94.4％的特异性为94.4％发病。仿真结果表明，基于EEMD的熵估计器在SCD患者和正常人之间表现出显着差异，并且优于SCD检测中的经典线性估计，基于EEMD的Fuen和IMPE指标对于鉴定SCD风险的患者进行特别有用的评估。可以用作新颖的指数，以揭示受SCD影响的自主神经系统的节奏变化的障碍。

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