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目的为了准确、快速地识别出窦性心律(NSR)、室性心动过速(VT)与心室纤颤(VF)。方法本文引入基于多尺度分析的非线性算法——Hurst index,来量化ECG信号的非线性动力学特征。结果从MIT-BIH Arrhythmia Database、MIT-BIH Malignant Ventricular Ectopy Database与CU Ventricula Tachyarrhythmia Database典型数据库中引用数据,并对该算法进行验证与评价,结果表明:当滑动窗长度是5s时,NSR、VT与VF被完全正确地检出;另外,该算法的运算速率远高于经典的非线性算法———复杂度算法。结论在临床应用中,用Hurst指标识别室性心律失常具有极大的潜力。
Purpose To accurately and quickly identify sinus rhythm (NSR), ventricular tachycardia (VT) and ventricular fibrillation (VF). Methods A nonlinear algorithm based on multiscale analysis, Hurst index, is introduced to quantify the nonlinear dynamics of ECG signals. Results The data were referenced from the database of MIT-BIH Arrhythmia Database, MIT-BIH Malignant Ventricular Ectopy Database and CU Ventricula Tachyarrhythmia Database, and the algorithm was validated and evaluated. The results show that when the sliding window length is 5s, NSR, VT and VF is detected completely correctly. In addition, the computational speed of this algorithm is much higher than that of the classical nonlinear algorithm --- complexity algorithm. Conclusion In clinical application, Hurst index has great potential to identify ventricular arrhythmias.