Fractal feature based ECG arrhythmia classification

S. Raghav, A.K. Mishra
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引用次数: 17

Abstract

We propose a method for the classification of ECG arrhythmia using local fractal dimensions of ECG signal as the features to classify the arrhythmic beats. The heart beat waveforms were extracted within a fixed length window around the R-peak of the signal and local fractal dimension is calculated at each sample point of the ECG waveform. The method is based on matching these fractal dimension series of the test ECG waveform to that of the representative ECG waveforms of different types of arrhythmia, by calculating Euclidean distances or by calculating the correlation coefficients. The performance of the classifier was tested on independent MIT-BIH arrhythmia database. The achieved performance is represented in terms of the percentage of correct classification (found to be 99.49% on an average). The performance was found to be competitive to other published results. The current classification algorithm proved to be a computationally efficient and hence a potential technique for automatic recognition of arrhythmic beats in ECG monitors or Holter ECG recorders.
基于分形特征的心电心律失常分类
提出了一种利用心电信号局部分形维数作为特征对心律失常进行分类的方法。在信号r峰周围的固定长度窗口内提取心跳波形,并在心电波形的每个采样点计算局部分形维数。该方法通过计算欧氏距离或计算相关系数,将测试心电波形的分形维数序列与不同类型心律失常的代表性心电波形的分形维数序列进行匹配。在独立的MIT-BIH心律失常数据库上测试了分类器的性能。实现的性能用正确分类的百分比表示(发现平均为99.49%)。与其他已发表的结果相比,该性能具有竞争力。目前的分类算法被证明是一种计算效率高的算法,因此是一种潜在的自动识别心电监护仪或动态心电图记录仪中的心律失常的技术。
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