Abstract
In medical practices, the detection of diseases highly depends on different medical tests. Electrocardiogram (ECG) technique is commonly used for heart disease diagnosis. Doctors can measure pulse and other heart boundaries with the aid of it. Fast and precise detection of forms of arrhythmia is critical while identifying heart disease. In this work, we proposed an intelligent ECG device (called iKardo) with the built-in automatic capability to classify into critical and non-critical data from an imbalanced ECG dataset for the smart IoT or Internet of Things based smart healthcare device. Particular emphasis is given to the reduction of data misclassification by converting imbalanced data into a balanced dataset using necessary techniques. This proposed iKardo helps in the accurate detection of critical ECG beats with an accuracy of 99.58% and result in a smart healthcare monitoring device that would make the disease detection fast and precise.
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