| Paper: | MLSP-P2.9 | ||
| Session: | Bioinformatics and Biomedical Applications | ||
| Time: | Wednesday, May 19, 13:00 - 15:00 | ||
| Presentation: | Poster | ||
| Topic: | Machine Learning for Signal Processing: Signal detection, Pattern Recognition and Classification | ||
| Title: | PATTERN RECOGNITION OF CARDIAC ARRHYTHMIAS BASED ON MULTIVARIATE AUTOREGRESSIVE MODELING | ||
| Authors: | Dingfei Ge; Zhejiang University of Science and Technology | ||
| Zhegen Zhang; Zhejiang University of Science and Technology | |||
| Abstract: | Abstract-Computer-assisted automatic diagnosis will play an important role in diagnosis and treatment of critical ill patients.Multivariate autoregressive modeling (MAR) has been performed on two-lead ECG signals in this research. MAR coefficients and K-L transformation of MAR coefficients have been used as ECG features for classification. Five types of ECG signals were obtained from MIT-BIH database, namely normal sinus rhythm, atria premature contraction, premature ventricular contraction, ventricular tachycardia, and ventricular fibrillation. A quadratic discriminant function (QDF) based classification algorithm was employed in this study. The results show MAR coefficients produced slightly better results than K-L transformation of MAR coefficients. The accuracy of classification based on MAR coefficients was 96.6% to 99.3%.Key words: ECG signals, Multivariate Autoregressive Modeling, Quadratic discriminant function, Classification | ||
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