Key result
A fuzzy inference system achieved 100% accuracy in classifying ECG signals into normal and 9 types of cardiovascular diseases, compared to 90.4% accuracy using a backpropagation neural network.
Why the study?
Accurate diagnosis of arrhythmias is crucial for saving lives, and more efficient methods for ECG disease diagnosis are needed.
Does a fuzzy inference system improve the classification accuracy of ECG signals for cardiac arrhythmias compared to a Backpropagation neural network?
Does a fuzzy inference system improve the classification accuracy of ECG signals for cardiac arrhythmias compared to a Backpropagation neural network?
Absolute Event Rate: 100% vs 90.4%
A fuzzy logic inference system demonstrated superior accuracy (100%) compared to a Backpropagation neural network (90.4%) for the automatic classification of ECG signals in detecting cardiac arrhythmias.
Accurate diagnosis of arrhythmias plays a crucial role in saving the lives of many heart patients. The aim of this research is to find the more efficient method to diagnosis electrocardiogram (ECG) diseases. This work presents the use of Backpropagation neural network (BPNN) and fuzzy logic for automatic detection of cardiac arrhythmias based on analysis of the ECG. These a more valuable tool used to classify ECG signals in cardiac patients. Data collected from physioBank ATM. The analysis of the ECG signal is performed in MATLAB environment. In BPNN the results appear that the only two misclassifications happened to result in an accuracy of 90.4%. while in fuzzy inference system the results appear that the classification accuracy is 100%.
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Hassan et al. (2018) studied Cardiac arrhythmias (n=101). Fuzzy inference system and Backpropagation neural network (BPNN) was evaluated on Classification accuracy. A fuzzy inference system achieved 100% accuracy in classifying ECG signals into normal and 9 types of cardiovascular diseases, compared to 90.4% accuracy using a backpropagation neural network.
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