Key result
Random forest classifier using multiple features fusion achieves ~99% accuracy for intra-patient arrhythmia classification.
Why the study?
Accurate detection of arrhythmia is critical for clinical treatment, prompting the development of a novel automatic classification method to improve classification accuracy.
Does a random forest classifier with multiple features fusion improve arrhythmia classification accuracy compared to SVM and BP neural networks in ECG signals?
Does a random forest classifier with multiple features fusion improve arrhythmia classification accuracy compared to SVM and BP neural networks in ECG signals?
A novel random forest-based classification method using multiple features fusion achieved high accuracy for arrhythmia detection on intra-patient ECG beats, though sensitivity dropped significantly for inter-patient beats.
May support automated ECG arrhythmia detection; leaves open clinical utility pending prospective validation.
Cardiovascular diseases have become more and more prominent in recent years, which have proven to be a major threat to people's health. Accurate detection of arrhythmia in patients has important implications for clinical treatment. The aim of this study was to propose a novel automatic classification method for arrhythmia in order to improve classification accuracy. The electrocardiogram (ECG) signal was subjected preprocessing for denoising purposes using a wavelet transform. Then, the local and global characteristics of the beat, which contained RR interval features according with the clinical diagnosis criterion, morphology features based on wavelet packet decomposition and statistical features along with kurtosis coefficient, skewness coefficient and variance are exploited and fused. Meanwhile, the dimensionality of wavelet packet coefficients were reduced via principal component analysis (PCA). Finally, these features were used as the input of the random forest classifier to train the model and were then compared with the support vector machine (SVM) and back propagation (BP) neural networks. Based on 100,647 beats from the MIT-BIH database, the proposed method achieved an average accuracy, specificity and sensitivity of 99.08%, 99.00% and 89.31%, respectively, using the intra-patient beats, and 92.31%, 89.98% and 37.47%, respectively, using the inter-patient beats. Moreover, two classification schemes, namely, inter-patient and intra-patient scheme, were validated. Compared with the other methods referred to in this paper, the performance of the novel method yielded better results.
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Wang et al. (2019) studied Arrhythmia (n=100,647). Random forest classifier with multiple features fusion vs. Support vector machine (SVM) and back propagation (BP) neural networks was evaluated on Classification accuracy, specificity, and sensitivity. A random forest classifier using multiple features fusion achieved an average accuracy of 99.08%, specificity of 99.00%, and sensitivity of 89.31% for intra-patient arrhythmia classification.
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