以Hurst指标识别室性心动过速与心室纤颤(英文)

Recognizing Ventricular Tachycardia and Fibrillation with Hurst Index

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。

Objective To recognize normal sinus rhythm （NSR）, ventricular tachycardia （VT） and ventricular fibrillation （VF） from each other accurately and promptly. Methods A nonlinear descriptor based on multi-scale analysis, Hurst index, as a feature to quantify the nonlinear dynamics behavior of the ECG signal, was quoted in this paper. Results The nonlinear technique, Hurst index was examined and evaluated with ECG signals extracted from MIT-BIH Arrhythmia Database, MIT-BIH Malignant Ventricular Ectopy Database, and CU Ventricular Tachyarrhythmia Database under a specific moving-window length. The experiment showed good performance of this nonlinear descriptor. When the window length was 5 s long, the recognition accuracy for each of NSR, VT and VF was 100%. Besides, the computing speed was much faster than the speed obtained with a traditional non-linear technique, the complexity measure algorithm. Conclusion The Hurst index has a strong potential for life-threatening ventricular arrhythmia recognition in clinical applications.