Title: Wearable IoT enabled smart heart disease monitoring on WSN

Authors: Xiaofei Wang; Xiaodong Li; Bing Zhang; Yanghua Liu

Addresses: College of P.E and Sports, Beijing Normal University, Beijing, 100091, China ' Smart Health Promote Laboratory, Huaihua University, Huaihua, 418000, China ' Research Center of Physical Education and Health Science, Tsinghua University, Beijing, 100080, China ' School of Electrical and Information Engineering, Huaihua University, Huaihua, 418000, China

Abstract: The age profiles of many countries are increasing day by day with increasing population of individuals affected by the chronic diseases such as diabetes, cardiovascular disease, obesity and so on. In order to maintain the individual living, remote health monitoring with daily activity by recognising people is a promising solution. Cardiovascular disease (CVD) is the major cause of mortality globally. Most of the deaths due to CVD are sudden and without any chance of medical help. In order to avoid this accidental death, precautions are required with continuous monitoring of body parameters such as heart rate, pulse rate and electrocardiogram (ECG) to show the current status of the health. Internet of Things (IoT) is rapidly growing industry in many disciplines including healthcare. In current research, heart disease is monitored with processing of electrocardiogram signals. The existing monitoring system lacks in prediction accuracy and remote monitoring. In this proposed work, the gathered data from the wearable devices are preprocessed to remove the noise. The relevant features for better recognition are selected using the proposed LBPNet with particle swarm optimisation (PSO). Then sequential minimal optimisation based SVM classifier recognises the abnormalities of heart disease from normal patients for diagnosis. These data are available in remote servers for doctors and care takers with IoT application. The care takers are notified about the patient health using smart phones. This proposed system is useful for cardiac patient monitoring and updation with high accuracy.

Keywords: cardiac disease; health monitoring; IoT; Internet of Things; WSN; wireless sensor network; deep learning; PSO; particle swarm optimisation; LBPNet; ECG; electrocardiogram; SMO-SVM.

DOI: 10.1504/IJNT.2023.131114

International Journal of Nanotechnology, 2023 Vol.20 No.1/2/3/4, pp.199 - 213

Received: 20 May 2021
Accepted: 09 Aug 2021
Published online: 31 May 2023 *