Classification of ECG Signals for IoT-based Smart Healthcare Applications using WBAN

From the last few decades, the diagnoses of diseases are based on a medical test. Electrocardiogram (ECG) signal is one of the techniques used for diagnosing heart diseases. The Electrocardiograph or ECG machine allows removing many electrical and mechanical defects of the heart by measuring ECG's which have some potential on the body surface. With the help of it, doctors are able to determine heart rate and other cardiac parameters. Early and accurate detection of arrhythmia types is important in detecting heart diseases and finding treatment for a patient. This paper deals with the analysis of the signal for the classification of critical and non-critical data using different learning-based algorithms for smart Internet of Things (IoT) based health-care monitoring application using Wireless Body Area Network (WBAN) and how to minimize the misclassified critical data.