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首页> 外文期刊>Computer Methods and Programs in Biomedicine: An International Journal Devoted to the Development, Implementation and Exchange of Computing Methodology and Software Systems in Biomedical Research and Medical Practice >Prediction of paroxysmal atrial fibrillation based on non-linear analysis and spectrum and bispectrum features of the heart rate variability signal
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Prediction of paroxysmal atrial fibrillation based on non-linear analysis and spectrum and bispectrum features of the heart rate variability signal

机译：基于非线性分析以及心率变异性信号的频谱和双频谱特征的阵发性心房颤动预测

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摘要

In this paper, an effective paroxysmal atrial fibrillation (PAF) prediction algorithm is presented, which is based on analysis of the heart rate variability (HRV) signal. The proposed method consists of a preprocessing step for QRS detection and HRV signal extraction. In the next step, several features which can be used as markers for the prediction of PAF are extracted from the HRV signal. These features consist of spectrum features, bispectrum features, and non-linear features including sample entropy and Poincaré plot-extracted features. The spectrum features are able to discriminate the sympathetic and parasympathetic contents of the HRV signal, which are affected before PAF attacks. The bispectrum features are used in order to reveal information not presented on the spectral domain, and to detect quadratic phase coupled harmonics arising from non-linearities of the HRV signal. Moreover, the non-linear analysis can map the heart rate irregularities in the feature space and it leads to better understanding of the system dynamics before PAF attacks. In the final step, a support vector machine (SVM)-based classifier has been used for PAF prediction. The performance of the proposed method in prediction of PAF episodes was evaluated using the Atrial Fibrillation Prediction Database (AFPDB). The obtained sensitivity, specificity, and positive predictivity were 96.30%, 93.10%, and 92.86%, respectively. The proposed methodology presents better results than the other existing approaches. The other important advantage of the proposed method when compared to the other approaches is that we do not need the both records of a subject to specify which episode preceding PAF events.
机译：本文提出了一种有效的阵发性心房颤动（PAF）预测算法，该算法基于对心率变异性（HRV）信号的分析。所提出的方法包括用于QRS检测和HRV信号提取的预处理步骤。在下一步中，从HRV信号中提取几个可用作PAF预测标记的特征。这些特征包括光谱特征，双谱特征和非线性特征，包括样本熵和庞加莱图提取特征。频谱特征能够区分在PAF攻击之前受影响的HRV信号的同情和副交感内容。使用双频谱特征是为了揭示频谱域中未显示的信息，并检测由HRV信号的非线性引起的二次相位耦合谐波。此外，非线性分析可以映射特征空间中的心率不规则性，从而可以更好地理解PAF攻击之前的系统动力学。在最后一步中，基于支持向量机（SVM）的分类器已用于PAF预测。使用心房颤动预测数据库（AFPDB）评估了该方法在预测PAF发作中的性能。获得的敏感性，特异性和阳性预测性分别为96.30％，93.10％和92.86％。与其他现有方法相比，所提出的方法具有更好的结果。与其他方法相比，该方法的另一个重要优点是我们不需要受试者的两个记录来指定PAF事件之前的哪一集。

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