Smart Healthcare for ECG Telemonitoring System


  • Jwan Najeeb Saeed
  • Siddeeq Y. Ameen Duhok Polytechnic University


Smart healthcare, ECG telemonitoring, IoT cloud, wearable sensor, machine learning, deep learning


Cardiovascular disorders are one of the major causes of sudden death among older and middle-aged people. Over the past two decades, health monitoring services have evolved quickly and had the ability to change the way health care is currently provided. However, the most challenging aspect of the mobile and wearable sensor-based human activity recognition pipeline is the extraction of the related features. Feature extraction decreases both computational complexity and time. Deep learning techniques are used for automatic feature learning in a variety of fields, including health, image classification, and, most recently, for the extraction and classification of simple and complex human activity recognition in smart health care. This paper presents a review on a recent state of the art in the area of electrocardiogram (ECG) smart health monitoring systems based on the Internet of things with the machine and deep learning techniques. Moreover, the paper provided possible lines of research and challenges that can help researchers in advancing the state of art in future work.




How to Cite

Saeed, J. N. ., & Ameen, S. Y. . (2021). Smart Healthcare for ECG Telemonitoring System. Journal of Soft Computing and Data Mining, 2(2), 75–85. Retrieved from