Artificial Intelligence for Cardiac Diseases Diagnosis and Prediction Using ECG Images on Embedded Systems

Lotfi Mhamdi; Oussama Dammak; François Cottin; Imed Ben Dhaou

doi:10.3390/biomedicines10082013

Artificial Intelligence for Cardiac Diseases Diagnosis and Prediction Using ECG Images on Embedded Systems

Biomedicines. 2022 Aug 19;10(8):2013. doi: 10.3390/biomedicines10082013.

Authors

Lotfi Mhamdi¹, Oussama Dammak², François Cottin^{3

4}, Imed Ben Dhaou^{5

6}

Affiliations

¹ Biotechnology Institute of Monastir, Environment Street, Monastir 5000, Tunisia.
² Department of Mathematics, College of First Common Year, Um Al Qura University, Mecca 21955, Saudi Arabia.
³ CIAMS EA 4532, Paris-Saclay University, 91405 Orsay, France.
⁴ CIAMS EA 4532, Orleans University, 45067 Orleans, France.
⁵ Department of Computer Science, Hekma School of Engineering, Computing and Informatics, Dar Al-Hekma University, Jeddah 22246, Saudi Arabia.
⁶ Department of Technology, Higher Institute of Computer Sciences and Mathematics, University of Monastir, Monastir 5000, Tunisia.

Abstract

The electrocardiogram (ECG) provides essential information about various human cardiac conditions. Several studies have investigated this topic in order to detect cardiac abnormalities for prevention purposes. Nowadays, there is an expansion of new smart signal processing methods, such as machine learning and its sub-branches, such as deep learning. These popular techniques help analyze and classify the ECG signal in an efficient way. Our study aims to develop algorithmic models to analyze ECG tracings to predict cardiovascular diseases. The direct impact of this work is to save lives and improve medical care with less expense. As health care and health insurance costs increase in the world, the direct impact of this work is saving lives and improving medical care. We conducted numerous experiments to optimize deep-learning parameters. We found the same validation accuracy value of about 0.95 for both MobileNetV2 and VGG16 algorithms. After implementation on Raspberry Pi, our results showed a small decrease in accuracy (0.94 and 0.90 for MobileNetV2 and VGG16 algorithms, respectively). Therefore, the main purpose of the present research work is to improve, in an easy and cheaper way, real-time monitoring using smart mobile tools (mobile phones, smart watches, connected T-shirts, etc.).

Keywords: ECG images; Raspberry; cardiac arrhythmia classification; deep learning; healthcare.

Grants and funding

This research was funded by the Deanship of Scientific Research at Umm Al Qura University, grant number [22UQU4340610DSR01] And the APC was funded by Dar Al-Hekma University and CIAMS Laboratory, Paris-Saclay University.