Automated learning of ecg streaming data through machine learning internet of things

Abstract

Applying machine learning techniques on Internet of Things (IoT) data streams will help achieve better understanding, predict future perceptions, and make crucial decisions based on those analytics. The collaboration between IoT, Big Data and machine learning can be found in different domains such as Health care, Smart cities, and Telecommunications. The aim of this paper is to develop a method for automated learning of electrocardiogram (ECG) streaming data to detect any heart beat anomalies. A promising solution is to use medical sensors that transfer vital signs to medical care computer systems, combined with machine learning, such that clinicians can get alerted about patient’s critical condition and act accordingly. Since the probability of false alarms pose serious impact to the accuracy of cardiac arrhythmia detection, it is the most important factor to keep false alarms to the lowest level. The proposed method in this paper demonstrates an example of how machine learning can contribute to health technologies with in detecting heart disease through minimizing negative false alarms. Stages of heartbeat learning model are proposed and explained besides the stages heartbeat anomalies detection stages.