Key result
An approach combining wavelet transform feature extraction, PCA, ICA, and ID3 decision tree classification achieved high reliability and accuracy for recognizing heartbeat arrhythmias.
Why the study?
Does a combination of Wavelet transform and Decision tree classification accurately classify ECG signals for cardiac arrhythmias?
Does a combination of Wavelet transform and Decision tree classification accurately classify ECG signals for cardiac arrhythmias?
A novel machine learning approach combining wavelet transform and decision tree classification provides high accuracy and reliability for automated ECG arrhythmia detection.
Automatic analysis of cardiac arrhythmias is very important for diagnosis of cardiac abnormities. This paper presents a novel approach that classifies ECG signals with the combination of Wavelet transform and Decision tree classification. This approach has two aspects. In the first aspect, we utilize the wavelet transform to extract the ECG signals wavelet coefficients as the first features and utilize the combination of principal component analysis (PCA) and independent component analysis (ICA) to remove the first features relativity and search this features independence as the new features, then we add the RR interval as the final features. In the second aspect, we utilize the ID3 algorithm which is one of analysis decision tree methods as the classifier to recognize the different heartbeat arrhythmias. We utilize the MIT-BIH Arrhythmia Database to create the classification and test the classification. The results confirm its high reliability and high accuracy is very well.
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Zhang et al. (2010) studied Cardiac arrhythmias. Wavelet transform and Decision tree classification (ID3 algorithm) was evaluated on Classification accuracy and reliability. An approach combining wavelet transform feature extraction, PCA, ICA, and ID3 decision tree classification achieved high reliability and accuracy for recognizing heartbeat arrhythmias.
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