Wearable Sensor-Based Edge Computing Framework for Cardiac Arrhythmia Detection and Acute Stroke Prediction

Internet of Things-based smart healthcare systems have gained attention in recent years for improving healthcare services and reducing data management costs. However, there is a requirement for improving the smart healthcare system in terms of speed, accuracy, and cost. An intelligent and secure edge-computing framework with wearable devices and sensors is proposed for cardiac arrhythmia detection and acute stroke prediction. Latency reduction is highly essential in real-time continuous assessment, and classification accuracy has to be improved for acute stroke prediction. In this paper, preprocessing and deep learning-based assessment is performed in the edge-computing layer, and decisions are communicated instantly to the individuals. In this work, acute stroke prediction is performed by a deep learning model using heart rate variability features and physiological data. Classification accuracy is improved in this approach when compared to other machine learning approaches. Cloud servers are utilized for storing the healthcare data of individuals for further analysis. Analyzed data from these servers are shared with hospitals, healthcare centers, family members, and physicians. The proposed edge computing with wearable sensors approach outperforms existing smart healthcare-based approaches in terms of execution speed, latency time, and power consumption. The deep learning method combined with DWT performs better than other similar approaches in the assessment of cardiac arrhythmia and acute stroke prediction. The proposed classifier achieves a sensitivity of 99.4%, specificity of 99.1%, and accuracy of 99.3% when compared with other similar approaches.