Automatic ECG Arrhythmia Classification Using Ensemble Learning
Publish place: The First Conferecne on Novel Approaches of Biomedical Engineering in Cardivascular Diseases
Publish Year: 1393
نوع سند: مقاله کنفرانسی
زبان: English
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شناسه ملی سند علمی:
NABICAD01_008
تاریخ نمایه سازی: 19 اردیبهشت 1395
Abstract:
In this paper, the classification of eight common arrhythmias existing in the MIT-BIH database is taken into account. For this purpose, denoising and baseline wander removal accomplished using Wavelet Transform and Two-Pass Split-Window (TPSW) algorithm, respectively. Then, the performance of three classifiers (SVM, MLP, and PNN) evaluated using morphological features. Afterwards, using PCA, FPCA and LDA, morphological features are mapped into a subspace with lower dimensional and the efficiency of classifiers is assessed. Next, by combining the output of three aforementioned classifiers for morphological features, the effectiveness of different ensemble learnings methods, e. g. Majority Vote, Naïve Bayes, Continuous Nontrainable Combiner and Trainable Weighted Average Combiner, is evaluated. The same procedure is done for the features extracted from the Discrete Wavelet Transform (DWT). The proposed algorithm uses a combined feature vector including morphological and DWT features, and Naïve Bayes ensemble learning method for classification of eight arrhythmias (provided the accuracy of 97.66%). The results demonstrate the advantages of using ensemble learning methods against single classifiers.
Authors
S Ghorbanpour
Mechanical Engineering Department, Iran University of Science and Technology, Tehran
M Nazarahari
Mechanical Engineering Department, Iran University of Science and Technology
A. H Davaie Markazi
Mechanical Engineering Department, Iran University of Science and Technology
A Kabir
Engineering Department, Iran University of Science and Technology
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