Currently, in order to conduct a cardiac examination, the patient has to move from home to a hospital or doctor’s office. However, it is not simple to people who live in remote locations to travel every time to do the examination. In addition, not all hospitals in inland cities are able to perform such tests due to lack of equipment. In this paper, we propose a tool called Intelligent Detection of Arrhythmic Heartbeats on Electrocardiograms (IDAH-ECG) which collects data from an electrocardiograph and then analyzes and classifies the data, detecting patterns of arrhythmic beats in the ECG signal.
The IDAH-ECG uses a classification mechanism that was trained, using a public database, to classify arrhythmic beats based on the topological characteristics of the normal versus abnormal heartbeats. Finally, IDAH-ECG obtained a true-negative classification rate of approximately 93.24 percent, with the possibility of increasingly better rates as the number of training samples increases.