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ECG信号自动诊断中回归建模法特征提取的研究

葛丁飞

葛丁飞. ECG信号自动诊断中回归建模法特征提取的研究. 自动化学报, 2007, 33(5): 462-466. doi: 10.1360/aas-007-0462
引用本文: 葛丁飞. ECG信号自动诊断中回归建模法特征提取的研究. 自动化学报, 2007, 33(5): 462-466. doi: 10.1360/aas-007-0462
GE Ding-Fei. Study of Feature Extraction Based on Autoregressive Modeling in ECG Automatic Diagnosis. ACTA AUTOMATICA SINICA, 2007, 33(5): 462-466. doi: 10.1360/aas-007-0462
Citation: GE Ding-Fei. Study of Feature Extraction Based on Autoregressive Modeling in ECG Automatic Diagnosis. ACTA AUTOMATICA SINICA, 2007, 33(5): 462-466. doi: 10.1360/aas-007-0462

ECG信号自动诊断中回归建模法特征提取的研究

doi: 10.1360/aas-007-0462
详细信息
    通讯作者:

    葛丁飞

Study of Feature Extraction Based on Autoregressive Modeling in ECG Automatic Diagnosis

More Information
    Corresponding author: GE Ding-Fei
  • 摘要: This article explores the ability of multivariate autoregressive model (MAR) and scalar AR model to extract the features from two-lead electrocardiogram signals in order to classify certain cardiac arrhythmias. The classification performance of four different ECG feature sets based on the model coefficients are shown. The data in the analysis including normal sinus rhythm, atria premature contraction, premature ventricular contraction, ventricular tachycardia, ventricular fibrillation and superventricular tachycardia is obtained from the MIT-BIH database. The classification is performed using a quadratic discriminant function. The results show the MAR coefficients produce the best results among the four ECG representations and the MAR modeling is a useful classification and diagnosis tool.
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出版历程
  • 收稿日期:  2006-01-16
  • 修回日期:  2006-05-24
  • 刊出日期:  2007-05-20

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