IoT Empowerment in Healthcare: Detailed ECG Analysis and Prediction by using 2D Gaussian Filter

Asif  Raza; Khaliq  Ahmed; Maheen  Danish; Kashif  Sheikh; Fozia  Noor

doi:10.32350/icr.41.04

Asif Raza Sir Syed University of Engineering and Technology, Pakistan
Khaliq Ahmed Iqra University, Pakistan
Maheen Danish Sir Syed University of Engineering and Technology, Pakistan
Kashif Sheikh Sir Syed University of Engineering and Technology, Pakistan
Fozia Noor Combined Military Hospital, Pakistan

Keywords: computer vision, Gaussian filter, healthcare, health management systems, IOT, machine learning, patients, vital signs

Abstract

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Currently, healthcare sector is the most dynamic sector in terms of introducing new technologies and services. An innovative advancement in this sector is the remote or portable monitoring of patients, which is proving to be very beneficial in a world with a rapidly expanding population, rising health issues, and limited access to medical facilities. A patient monitoring equipment is often used to quantitatively measure a patient's vital signs, including blood pressure, temperature, ECG, heart rate, and SpO2. This study attempts to enhance the future of healthcare by developing such a patient monitoring system. The ECG dataset was downloaded from CPEIC. The device created for this study can measure four parameters, namely ECG, SpO2, heart rate, and body temperature. The values are displayed on an LCD screen and the device is IoT-based, allowing data transmission to a web application for easy and universal access. To address the issues encountered, the device is designed to be cost-effective, dependable, and portable. The ECG output of this IoT-based model was thoroughly examined and tested using a deep learning model known as Inception V3 to determine the accuracy and dependability of the network. The model obtained phenomenal training loss of 0.1315 and a training accuracy of 96.66%. On the validation set, it achieved a validation loss of 0.1146 and a validation accuracy of 96.90%. Two-dimensional Gaussian elimination was used to remove noise from ECG images.

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