Analysis of CNN and Feed-Forward ANN Model for the Evaluation of ECG Signal

Abstract

Aims: Heart disease is considered as one of the complex diseases that has affected a large number of people around world. Background: Therefore, it is important to detect and identify cardiac diseases at early stages Objective: A large number of methods are already present that detect various heart diseases, however, there are some limitations in these methods that degraded their overall performance. Methods: In this paper, an effective and efficient method based on convolutional neural network (CNN) and feed forward artificial neural network (FFANN) is proposed that can effectively detect cardiac diseases after analysing the Electrocardiogram (ECG) signals. In this ongoing study, the transformed signals are used to extract the information from the processed data. The extracted features are then passed to the proposed CNN-FFANN classifiers for training and testing purpose. Results: The performance of the proposed CNN-FFANN model is evaluated in the MATLAB software in terms of performance matrices. Conclusion: The simulated outcomes proved that the proposed CNN-FFANN model is more accurate and efficient in detecting heart diseases from ECG signals and can be adopted for future biomedical applications.