Arrhythmia Identification from ECG Signals with a Neural Network Classifier Based on a Bayesian Framework
نویسندگان
چکیده
This paper presents a diagnostic system for cardiac arrhythmias from ECG data, using an Artificial Neural Network (ANN) classifier based on a Bayesian framework. The Bayesian ANN Classifier is built by the use of a logistic regression model and the back propagation algorithm. A dual threshold method is applied to determine the diagnosis strategy and suppress false alarm signals. The experimental results presented in this paper show that more than 90% prediction accuracy may be obtained using the improved methods in the study. It is hoped that the system can be further developed and fine-tuned for practical application.
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Cardiovascular diseases are the most dangerous diseases and one of the biggest causes of fatality all over the world. One of the most common cardiac arrhythmias which has been considered by physicians is premature ventricular contraction (PVC) arrhythmia. Detecting this type of arrhythmia due to its abundance of all ages, is particularly important. ECG signal recording is a non-invasive, popula...
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