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首页> 外文期刊>Computational Biology and Bioinformatics >Detection of Abnormality in Electrocardiogram (ECG) Signals Based on Katz's and Higuchi's Method Under Fractal Dimensions
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Detection of Abnormality in Electrocardiogram (ECG) Signals Based on Katz's and Higuchi's Method Under Fractal Dimensions

机译：分形维数下基于Katz和Higuchi方法的心电图信号异常检测

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Analysis process of electrocardiogram (ECG) is a major research interests in bio-medical signal processing. The reasons of this interest is the growth of cardiac health care activities all over the world and the rapid progress in digital computer technology which play an essential role to the detection of diseases at various stages from bio medical signals. The assessment process of diagnostic results for these bio medical signals heavily depends upon quantity, accuracy and speed. Computer based analysis is very useful in clinical therapy. In this Paper a method of analysis (ECG) signals using fractal features have been proposed and practical experiments have done to show that this method provides a good electronic diagnosis pattern for cardiac abnormality because it has been used by some specialist doctors to diagnose various types of diseases with accuracy. By the fact that ECG signals show a fractal patterns, it has been tried to find out a comparison between Katz's and Higuchi's method under fractal dimension (FD) of the ECG time series in a feature extraction phase. All ECG signals have been acquired from the Massachusetts Institute of Technology (MITBIH) arrhythmia database. The obtained results confirm the superiority of the Katz's and Higuchi's method to identify cardiac abnormality as compared to traditional one which is analyses of ECG signals based on morphology features and three ECG temporal features.(i.e. the QRS complex duration, the RR interval and the RR interval averaged over the ten last beats).
机译：心电图（ECG）的分析过程是生物医学信号处理中的主要研究兴趣。引起这种兴趣的原因是全世界心脏保健活动的增长以及数字计算机技术的飞速发展，这对从生物医学信号检测疾病的各个阶段起着至关重要的作用。这些生物医学信号的诊断结果的评估过程在很大程度上取决于数量，准确性和速度。基于计算机的分析在临床治疗中非常有用。本文提出了一种使用分形特征分析信号的方法，并进行了实际实验，表明该方法为心脏异常提供了一种良好的电子诊断模式，因为它已被一些专科医生用来诊断各种类型的心电图。疾病的准确性。通过ECG信号显示为分形模式这一事实，已尝试在特征提取阶段在ECG时间序列的分形维数（FD）下找到Katz和Higuchi方法之间的比较。所有心电图信号均已从麻省理工学院（MITBIH）心律失常数据库中获取。与传统的基于形态学特征和三个ECG时间特征的ECG信号分析方法（即QRS复杂度，RR间隔和RR）进行分析相比，所得结果证实了Katz和Higuchi的方法识别心脏异常的方法的优越性。最后十个节拍的平均间隔）。

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