Volume 6, Issue 4 (April 2019), Pages: 101-109

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 Original Research Paper

 Title: Detection of arrhythmia from the analysis of ECG signal using artificial neural networks

 Author(s): Kamarul Zaman Panatik ¹, Kamilia Kamardin ^{2, 3,} *, Nilam Nur Amir Sjarif ¹, Nur Syazarin Natasha Abd Aziz ¹, Nurul Aini Bani ¹, Noor Azurati Ahmad ¹, Suriani Mohd Sam ¹, Azizul Azizan ^{1, 3}

 Affiliation(s):

 ¹Razak Faculty of Technology and Informatics, Universiti Teknologi Malaysia, UTM KL, Kuala Lumpur, Malaysia
 ²Malaysia-Japan International Institute of Technology, Universiti Teknologi Malaysia, UTM KL, Kuala Lumpur, Malaysia
 ³Wireless Communication Center, Universiti Teknologi Malaysia, UTM KL, Kuala Lumpur, Malaysia

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 * Corresponding Author. 

  Corresponding author's ORCID profile: https://orcid.org/0000-0001-5317-714X

 Digital Object Identifier: 

 https://doi.org/10.21833/ijaas.2019.04.012

 Abstract:

Arrhythmia is a heart rhythm problem that could indicate a symptom of heart disease that often contributes to the increase in hospitalization in many developed countries. The patient of heart disease requires continuous monitoring and close attention to their vital sign such as the heart rate. There are many attempts to automate the detection of Arrhythmia from the Electrocardiogram (ECG) readings of patient. Nevertheless, the accuracy of some of these methods is not satisfactory and prone to biased result due to inter-patient variations of ECG dataset. The purpose of this research addresses the arrhythmia classification problem from the ECG signal using Artificial Neural Network (ANN). First, we perform feature extraction on the ECG data which are the four features from RR intervals. The features are then transformed into a feature vector. Then we modelled sixteen different models of ANN where four different algorithms were used such as Bayesian Regularization (BR), Levenberg-Marquardt (LM), Scaled Conjugate Gradient (SCG), and Resilient Backpropagation (RP). The sixteen models are built with a different number of neurons in the hidden layer. We used the dataset from Massachusetts Institutes of Technology- Beth Israel Hospital (MIT-BIH) Arrhythmia Database for evaluating our models which are simulated in MATLAB. The results of the simulation were analyzed and the best model was compared with the previous work. The analysis of our research indicates that the ANN using Bayesian regularization with twenty number of neurons in the hidden layer is the optimal model compared to other models with an overall accuracy of 83.1%. The Normal class Sensitivity was 97.4%, Specificity of 66.7% and Positive Predictive Value of 77.1%. The SVEB Sensitivity was 60% with Specificity of 86.9% and Positive Predictive Value of 42.9%. The VEB Sensitivity was 66.7% with Specificity of 88.7% and Positive Predictive Value of 66.7%. The comparison with other works indicates that our model outperforms the previous work in terms of sensitivity and overall accuracy. 

 © 2019 The Authors. Published by IASE.

 This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

 Keywords: Heart disease, ECG, Artificial neural network, Accuracy

 Article History: Received 22 October 2018, Received in revised form 5 February 2019, Accepted 11 February 2019

 Acknowledgement:

The authors fully acknowledged Ministry of Education (MOE) and Universiti Teknologi Malaysia (UTM) for the approved fund (15H97 and 16H95) which makes this research viable and effective.

 Compliance with ethical standards

 Conflict of interest:  The authors declare that they have no conflict of interest.

 Citation:

  Panatik KZ, Kamardin K, and Sjarif NNA et al. (2019). Detection of arrhythmia from the analysis of ECG signal using artificial neural networks. International Journal of Advanced and Applied Sciences, 6(4): 101-109

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 Figures

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 Tables

 Table 1 Table 2 Table 3 Table 4 Table 5 Table 6 Table 7 Table 8 Table 9 Table 10 

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