Key result
A novel automated ECG classification system utilizing 15 features from Tetrolet transforms and an SVM classifier achieved an overall accuracy of 99.35% across 16 arrhythmia classes.
Why the study?
Proper analysis of ECG signals can detect heart diseases, but better methods are needed to detect and classify arrhythmia.
The addition of Tetrolet transform-derived energy and entropy features to standard ECG features enables highly accurate (99.35%) automated classification of 16 arrhythmia classes using an RBF-SVM classifier.
Should not yet change clinical ECG workflows; leaves open prospective validation of Tetrolet-SVM arrhythmia classifiers.
Heart diseases had been molded as potential threats to human lives, especially to elderly people in recent days due to the dynamically varying food habits among the people. However, these diseases could be easily caught by proper analysis of Electrocardiogram (ECG) signals acquired from individuals. This paper proposes a better method to detect and classify the arrhythmia using 15 features which include 4 R-R interval features, 3 statistical and 6 chaotic features estimated from ECG signals. Additionally, Entropy and Energy features had been gained after converting one dimensional ECG signals to two dimensional data and applied Tetrolet transforms on that. Total numbers of 15 features had been utilized to classify the heart beats from the benchmark MIT-Arrhythmia database using Support Vector Machines (SVM). The classification performance was analyzed under various kernel functions and different Tetrolet decomposition levels. It is found that Radial Basis Function (RBF) kernel could perform better than linear and polynomial kernels. This research attempt yielded an accuracy of 99.35 % against the existing works. Moreover, addition of two more features had introduced a negligible overhead of time. Hence, this method is better suitable to detect and classify the Arrhythmia in both online and offline.
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Jayagopi et al. (2019) studied Arrhythmia (n=47). Tetrolet transforms and SVM classifier vs. Other classification methods was evaluated on Classification accuracy. A novel automated ECG classification system utilizing 15 features from Tetrolet transforms and an SVM classifier achieved an overall accuracy of 99.35% across 16 arrhythmia classes.
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