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Current Signal Transduction Therapy

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ISSN (Print): 1574-3624
ISSN (Online): 2212-389X

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Research Article

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

Author(s): Prerak Mathur, Tanu Sharma, and Karan Veer*

Volume 18, Issue 1, 2023

Published on: 19 August, 2022

Article ID: e280322202704 Pages: 8

DOI: 10.2174/1574362417666220328144453

Price: $65

Abstract

Background: Heart disease is considered one of the complex diseases that has affected a large number of people around the world. 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 to these methods, that have degraded their overall performance.

Methods: In this paper, an effective and efficient method based on a convolutional neural network (CNN) and a 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 purposes.

Results: The performance of the proposed CNN-FFANN model is evaluated in the MATLAB software in terms of performance matrices.

Conclusion: The simulated outcomes have proved the proposed CNN-FFANN model to be more accurate and efficient in detecting heart diseases from ECG signals and can be adopted for future biomedical applications.

Keywords: ECG signal, artificial neural network, health monitoring system, biomedical applications, artificial intelligence, cardiac diseases.

Graphical Abstract

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