Internet of Things Enabled Based Arrhythmia Classification using Dandelion Optimization Algorithm with Ensemble Learning

 

T. Vivekanandan1, J. Jegan1,*, D. Jagadeesan1, Y. Sreeraman1, N. Ch. S. N. Iyengar2, E. Purushotham3

 

1Department of Computer Science and Engineering, School of Technology, The Apollo University, Chittoor, Andhra Pradesh, India

2Department of Information Technology, Sreenidhi Institute of Science and Technology, Yamnampet, Ghatkesar, Hyderabad, Telangana, India

3Department of Computer Science and Engineering, Sreenivasa Institute of Technology and Management Studies, Chittoor, Andhra Pradesh, India

Emails: stvanand@gmail.com;  jegan.deepa@gmail.com; djagadeesanphd@gmail.com; sramany@gmail.com; srimannarayanach@sreenidhi.edu.in; e.purushotham@gmail.com

*Corresponding Author: jegan.deepa@gmail.com

 

Text Box: Abstract Internet of Things (IoT) based Arrhythmia Classification is a cutting-edge algorithm that amalgamates the abilities of the IoT and advanced medical diagnosis to revolutionize the detection and classification of arrhythmias—irregular heartbeats that may indicate fundamental cardiovascular issues. This technique leverages IoT devices, namely connected health monitors and wearable sensors, to continuously gather electrocardiogram (ECG) information from individuals. This information, streamed in real-time, provides a great opportunity for timely and remote monitoring of cardiac health. Leveraging the abilities of deep learning and IoT, this technique provides an automated and more sophisticated means of classifying and detecting arrhythmias, improving the efficiency and accuracy of diagnoses. This article presents an Internet of Things Enabled Based Arrhythmia Classification using the Dandelion Optimization Algorithm with Ensemble Learning (AC-DOAEL) method. The presented AC-DOAEL technique utilizes IoT-based data collection with an ensemble learning-based classification process. For the arrhythmia detection and classification process, the AC-DOAEL technique follows an ensemble learning algorithm such as long short-term memory (LSTM), autoencoder (AE), and bidirectional LSTM (BiLSTM) models. To improve the recognition rate of the ensemble models, the AC-DOAEL technique uses DOA as a hyperparameter optimizer. The simulation outcomes of the AC-DOAEL method are well-studied on benchmark ECG data. The experimental result analysis inferred the greater performance of the AC-DOAEL algorithm with other techniques. Received: September 15, 2022 Revised: December 19, 2022 Accepted: January 18, 2024

 

 

Keywords: Internet of Things; healthcare; Arrhythmia classification; ECG signals; Artificial intelligence