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Heart disease classification through HRV analysis using Parallel Cascade Identification and Fast Orthogonal Search

机译:使用平行叶栅识别和快速正交搜索通过HRV分析进行心脏病分类

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Heart rate variability (HRV) is an established indicator of cardiac health. Recent developments have shown the potential of nonlinear metrics for pattern classification of various heart conditions. Evidence indicates that the combination of multiple linear and nonlinear features leads to increased classification accuracy. In our paper, we demonstrate HRV classification using two dynamic nonlinear techniques called Parallel Cascade Identification (PCI) and Fast Orthogonal Search (FOS). We investigate the use of these two techniques for feature extraction from publicly available Physionet electrocardiogram (ECG) data to differentiate between normal sinus rhythm of the heart and 3 undesired conditions: arrhythmia, supraventricular arrhythmia, and congestive heart failure. Results compare well with previous studies which have used more features over the same dataset. We hypothesize that combining PCI and FOS features with traditional HRV features will show further improvement in classification accuracy and so can assist in real-time patient monitoring.
机译:心率变异性(HRV)是确定心脏健康的指标。最近的发展表明,非线性指标可用于各种心脏状况的模式分类。有证据表明,多个线性和非线性特征的组合导致分类精度提高。在本文中,我们使用两种动态非线性技术(称为并行级联识别(PCI)和快速正交搜索(FOS))演示了HRV分类。我们调查使用这两种技术从公开的Physionet心电图(ECG)数据中提取特征,以区分正常的窦性心律和3种不良情况:心律不齐,室上性心律失常和充血性心力衰竭。结果与以前的研究相比较,后者在同一数据集上使用了更多功能。我们假设将PCI和FOS功能与传统HRV功能相结合将显示出分类准确性的进一步提高,因此有助于实时患者监测。

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