Development of an Efficient Monitoring System Using Fog Computing and Machine Learning Algorithms on Healthcare 4.0

Abstract

Disruptive innovations in data management and analytics have led to the development of patient-centric Healthcare 4.0 from the hospital-centric Healthcare 3.0. This work presents an IoT-based monitoring systems for patients with cardiovascular abnormalities. IoT-enabled wearable ECG sensor module transmits the readings in real-time to the fog nodes/mobile app for continuous analysis. Deep learning/machine learning model automatically detect and makes prediction on the rhythmic anomalies in the data. The application alerts and notifies the physician and the patient of the rhythmic variations. Real-time detection aids in the early diagnosis of the impending heart condition in the patient and helps physicians clinically to make quick therapeutic decisions. The system is evaluated on the MIT-BIH arrhythmia dataset of ECG data and achieves an overall accuracy of 95.12% in classifying cardiac arrhythmia.