지원사업
학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
초록· 키워드
오류제보하기
This paper presented a method for random forest based the arrhythmia classification using both heart rate (HR) and heart rate variability (HRV) features. We analyzed the MIT-BIH arrhythmia database which contains halfhour ECG recorded from 48 subjects. This study included not only the linear features but also non-linear features for the improvement of classification performance. We classified abnormal ECG using mean_NN (mean of heart rate), SD1/SD2 (geometrical feature of poincare HRV plot), SE (spectral entropy), pNN100 (percentage of a heart rate longer than 100 ms) affecting accurate classification among combined of linear and nonlinear features. We compared our proposed method with Neural Networks to evaluate the accuracy of the algorithm. When we used the features extracted from the HRV as an input variable for classifier, random forest used only the most contributed variable for classification unlike the neural networks. The characteristics of random forest enable the dimensionality reduction of the input variables, increase a efficiency of classifier and can be obtained faster, 11.1% higher accuracy than the neural networks.
목차
등록된 정보가 없습니다.
참고문헌 (22)
참고문헌 신청
U.R. ACHARYA / 2006 / Heart rate variability: a review / Medical and Biological Engineering and Computing 44(12):1031~1051
E. ZELLMER / 2009 / Highly accurate ECG beat classification based on continuous wavelet transformation and multiple support vector machine classifiers / Proceeding of the 2009 2th International Conference on Biomedical Engineering and Informatics (BMEI 2009) :1~5
M. ENGIN / 2004 / ECG beat classification using neuro-fuzzy network / Pattern Recognition Letters 25(15):1715~1722
Y. -C. YEH / 2009 / Cardiac arrhythmia diagnosis method using linear discriminant analysis on ECG signals / Measurement 42(5):778~789
I. CHRISTOV / 2006 / Comparative study of morphological and time-frequency ECG descriptors for heartbeat classification / Medical Engineering & Physics 28(9):876~887
E. ZELLMER / 2009 / Highly accurate ECG beat classification based on continuous wavelet transformation and multiple support vector machine classifiers / Proceeding of the 2009 2th International Conference on Biomedical Engineering and Informatics (BMEI 2009) :1~5
M. ENGIN / 2004 / ECG beat classification using neuro-fuzzy network / Pattern Recognition Letters 25(15):1715~1722
Y. -C. YEH / 2009 / Cardiac arrhythmia diagnosis method using linear discriminant analysis on ECG signals / Measurement 42(5):778~789
I. CHRISTOV / 2006 / Comparative study of morphological and time-frequency ECG descriptors for heartbeat classification / Medical Engineering & Physics 28(9):876~887
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