Wang Y, Zhu Y S, Thakor N V, Xu Y H
Department of Biomedical Engineering, Shanghai Jiao Tong University, China.
IEEE Trans Biomed Eng. 2001 Sep;48(9):989-95. doi: 10.1109/10.942588.
We have proposed the notion of short-time multifractality and used it to develop a novel approach for arrhythmia detection. Cardiac rhythms are characterized by short-time generalized dimensions (STGDs), and different kinds of arrhythmias are discriminated using a neural network. To advance the accuracy of classification, a new fuzzy Kohonen network, which overcomes the shortcomings of the classical algorithm, is presented. In our paper, the potential of our method for clinical uses and real-time detection was examined using 180 electrocardiogram records [60 atrial fibrillation, 60 ventricular fibrillation, and 60 ventricular tachycardia]. The proposed algorithm has achieved high accuracy (more than 97%) and is computationally fast in detection.
我们提出了短时多重分形的概念，并利用它开发了一种用于心律失常检测的新方法。心脏节律以短时广义维数（STGDs）为特征，并使用神经网络来区分不同类型的心律失常。为了提高分类的准确性，提出了一种新的模糊科霍宁网络，它克服了经典算法的缺点。在我们的论文中，使用180份心电图记录[60份心房颤动、60份心室颤动和60份室性心动过速]检验了我们的方法在临床应用和实时检测方面的潜力。所提出的算法具有很高的准确率（超过97%），并且在检测时计算速度很快。
