RHYTHMI: A Deep Learning-Based Mobile ECG Device for Heart Disease Prediction

Author:

Eleyan Alaa1ORCID,AlBoghbaish Ebrahim1ORCID,AlShatti Abdulwahab1,AlSultan Ahmad1,AlDarbi Darbi1

Affiliation:

1. College of Engineering and Technology, American University of the Middle East, Egaila 54200, Kuwait

Abstract

Heart disease, a global killer with many variations like arrhythmia and heart failure, remains a major health concern. Traditional risk factors include age, cholesterol, diabetes, and blood pressure. Fortunately, artificial intelligence (AI) offers a promising solution. We have harnessed the power of AI, specifically deep learning and convolutional neural networks (CNNs), to develop Rhythmi, an innovative mobile ECG diagnosis device for heart disease detection. Rhythmi leverages extensive medical data from databases like MIT-BIH and BIDMC. These data empower the training and testing of the developed deep learning model to analyze ECG signals with accuracy, precision, sensitivity, specificity, and F1-score in identifying arrhythmias and other heart conditions, with performances reaching 98.52%, 98.55%, 98.52%, 99.26%, and 98.52%, respectively. Moreover, we tested Rhythmi in real time using a mobile device with a single-lead ECG sensor. This user-friendly prototype captures the ECG signal, transmits it to Rhythmi’s dedicated website, and provides instant diagnosis and feedback on the patient’s heart health. The developed mobile ECG diagnosis device addresses the main problems of traditional ECG diagnostic devices such as accessibility, cost, mobility, complexity, and data integration. However, we believe that despite the promising results, our system will still need intensive clinical validation in the future.

Publisher

MDPI AG

Reference42 articles.

1. Russell, S., and Norvig, P. (2021). Artificial Intelligence: A Modern Approach, Pearson. [4th ed.].

2. Machine Learning from Theory to Algorithms: An Overview;Alzubi;J. Phys. Conf. Ser.,2018

3. Santosh, K.C., Das, N., and Ghosh, S. (2022). Chapter 1—Introduction. Primers in Biomedical Imaging Devices and Systems: Deep Learning Models for Medical Imaging, Academic Press.

4. AlphaGo: Mastering the game of Go with deep neural networks and tree search;Silver;Nature,2016

5. Zhou, S.K., Greenspan, H., and Shen, D. (2017). Chapter 2—An Introduction to Deep Convolutional Neural Nets for Computer Vision. Deep Learning for Medical Image Analysis, Academic Press.

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