Investigation of ecg signal on cardiovascular disease using artificial intelligence of medical things

Abstract

Due to recent advancements in healthcare industry stands to gain enormous benefits from artificial intelligence (AI) and IoT (Internet of Things). The Internet of things is the fast-growing technology in the present days due to its larger network connectivity and numerous significant applications in various fields. IoT has also bring advancement in the bio-medical field by developing IoMT (Internet of Medical Things) network. ECG (Electrocardiogram) signals are being measured to detect the abnormal heart functions and for effectively classification is an essential step in the detection of Myocardial Infraction (MI)-affected patients and they are major challenges like network traffic, data loss and security issues in the current IoMT system. newlineOur proposed work, is designed with an deep learning based adaptive recurrent neural network (ARNN) classification algorithm to provide exact prediction of the heart disease. An advanced autoencoder based signal compression technique with a newly designed MS notch filter with Priority based Auto-encoder approach is also developed to attain the enhanced signal for compression. The proposed model has been integrated with the IoMT network, called Artificial Intelligence of Medical Things (AIoMT). Currently, network congestion in IoMT is a major problem is also rectified newline