A new trained ECG signal Classification method using Modified Spline Activated Neural Network

An ECG (Electrocardiogram) records the electrical activity of the heart and assess heart arrhythmia. Cardiac arrhythmia is an irregular heartbeat caused by unbalanced rhythm. In the past, several works were developed to produce automatic ECG-based heartbeat classification methods. In this work, a modified spline activated neural network, a new approach for cardiac arrhythmia classification by presenting the ECG signal preprocessing, the heartbeat segmentation techniques, the feature description methods and the learning algorithms used. The MIT-Bill arrhythmia database was used and experimented for testing and training.