An ANN Ensemble Based ECG Signal Classification Approach for Accurate Arrhythmia Detection

Abstract

Artificial Neutral Networks (ANN) based classification of the electrocardiogram signal to identify various diseases of the heart is a popular method. However, as the classification error can lead to fatal consequences, so the new approaches to improve the accuracy of ANN based methods has been an active area of research during the past decade. An ANN ensemble based approach to detect the arrhythmia from ECG signal by classifying into normal and abnormal classes has been proposed in this presented work. The high accuracy of classification has been observed from the results which makes the proposed approach very effective. Another advantage of the proposed approach is its straightforward design which makes it very easy for implementation.

Prediction of Arrhythmias and Acute Myocardial Infarctions using Machine Learning

“…Las técnicas modernas de redes neuronales profundas (DNN, Deep Neural Networks en inglés) se utilizan para resolver el problema de selección y extracción manual de características en sistemas automáticos convencionales para el diagnóstico de imágenes de Infarto al Miocardio (MI, Myocardial Infartaction en inglés) [25]. El algoritmo de retropropagación se utiliza para entrenar redes neuronales profundas [26].…”