A cost-effective ECG monitoring in rural areas: leveraging artificial neural networks for efficient healthcare solutions

Abstract

Cardiovascular diseases engender serious public health concerns in developing nations since access to specialized medical equipment is often limited and standard treatment expenses can be prohibitive. This study proposes an efficient and relatively affordable electrocardiogram (ECG) monitoring system that reads and analyzes a person's electrocardiogram data to provide affordable and quality healthcare solutions. The device initially extracts features from electrocardiogram records by reading electrical signals in the heart. Extracted data are then analyzed by a trained deep learning model to determine precisely if the heart is in a healthy state or undergoing complexities. Experimental results showed that the fine-tuned ANN architecture outperformed the state-of-the-art architectures in this field with an accuracy of 98.95%. The data can also be sent to specialists through an MQTT server if necessary, allowing for remote diagnosis and treatment. The system is intended to be deployed in countries where rural regions lack access to specialized healthcare equipment and professionals. Additionally, the device is inexpensive and, hence can be made accessible to people with limited affordability.