A Context-Responsive LSTM based IoT Enabled E- Healthcare Monitoring System for Arrhythmia Detection

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Published: Oct 27, 2023
DOI: https://doi.org/10.17762/ijritcc.v11i9.8330
Keywords:
IoT, healthcare monitoring, context responsiveness, LSTM, Arrythmia

Main Article Content

T. Pandiselvi
Department of Electronics and Communication Engineering, Kamaraj College of Engineering and Technology, SPGC Nagar, K.vellakulam madurai District, Tamil Nadu, India
B. Lakshmi Dhevi
Department of Networking and Communications, SRM Institute of Science and Technology, College of Engineering and Technology, Kattankulathur, Chennai, Tamil Nadu, India
G.M. Karthik
Department of Information Security, School of Computer Science and Engineering (SCOPE), Vellore Institute of Technology, Vellore Campus, Tiruvalam Rd, Katpadi, Vellore, Tamil Nadu, India
Vinoth T
Department of Mechatronics, Chennai Institute of technology, Chennai, Tamil Nadu, India
S. Shibu
Department of Electronics and Communication Engineering, Panimalar Engineering College, Chennai, Tamil Nadu, India
C.Ramesh Kumar
Department of Electronics and Communication Engineering, Panimalar Engineering College, Chennai, Tamil Nadu, India

Abstract

Detecting Arrhythmia, a life-threatening cardiac condition, in real-time is crucial for timely intervention and improved healthcare outcomes. Traditional manual methods for Arrhythmia detection using Electrocardiogram (ECG) signals are error-prone and resource-intensive. To address these limitations, this paper presents an automated system based on the Context Responsive Long Short-Term Memory (CR-LSTM) model for real-time Arrhythmia classification. The system leverages IoT technology to continuously monitor vital signs and effectively combines contextual information with temporal sensor data to accurately discern different types of Arrhythmias. The CR-LSTM model achieves an impressive accuracy of 99.72% in multiclass classification of Arrhythmias, making it a promising solution for dynamic healthcare settings and proactive personalized care.

Article Details

How to Cite
Pandiselvi, T., Dhevi, B. L. ., Karthik, G., T, V., Shibu, S. ., & Kumar, C. . (2023). A Context-Responsive LSTM based IoT Enabled E- Healthcare Monitoring System for Arrhythmia Detection. International Journal on Recent and Innovation Trends in Computing and Communication, 11(9), 157–163. https://doi.org/10.17762/ijritcc.v11i9.8330
Issue
Vol. 11 No. 9 (2023)
Section
Articles

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