Affiliations: [a]
Department of ECE, Chaitanya Bharathi Institute of Technology, Hyderabad, Telangana, India
| [b]
Department of ECE, Anurag University, Hyderabad, Telangana, India
| [c]
Department of CSE, Vasavi College of Engineering, Hyderabad, Telangana, India
| [d]
Department of ECE, SR University, Warangal, Telangana, India
Correspondence:
[*]
Corresponding author. P. Anuradha, Department of ECE, Chaitanya Bharathi Institute of Technology, Hyderabad, Telangana, India. E-mail: [email protected].
Abstract: Nowadays, WSN-IoT may be used to remotely and in real-time monitor patients’ vital signs, enabling medical practitioners to follow their status and deliver prompt treatments. This equipment can evaluate the gathered data on-site thanks to the integration of edge computing, enabling quicker diagnostic and medical options with the need for massive data transmission to a centralized server. Making the most of the resources accessible without sacrificing monitoring efficiency is critical due to the constrained lifespan and resource availability that these intelligent devices still encounter. To make the most of the assets at hand and achieve excellent categorization performance, intelligence must be applied through a learning model. Making the most of the resources that are available without sacrificing performance monitoring is essential given the restricted lifespan and resource availability that these intelligent devices still suffer. A learning model must incorporate intelligence in order to maximize the utilization of resources while maintaining excellent classification performance. In this study, a unique Harris Hawks Optimized Long Short-Term Memory (HHO-LSTM) that categorizes Electrocardiogram (ECG) data without compromising optimum utilization of resources is proposed for Edge enabled WSN devices. We will train the model to correctly categorize various kinds of ECG readings by employing cutting-edge techniques and neural networks. Significant testing is carried out on fifty individuals utilizing real-time test chips with integrated controllers coupled to ECG sensors and NVIDIA Jetson Nano Boards as edge computing devices. To show the benefits of the suggested model, performance comparisons with various deep-learning techniques for peripheral equipment are conducted. Experiments show that in terms of classification results (98% accuracy) and processing expenses, the suggested model, which is based on Edge-enabled WSN devices, beat existing state-of-the-art learning algorithms. The ability of this technology to help medical personnel diagnose a range of heart issues would eventually enhance customer management.
