Real-time ECG Analysis and Classification using Neural Networks in IoT devices

This paper develops a simple, cost-efficient, and easy-to-access ECG analyzer. The proposed prototype can perform the acquisition of ECG Signals lively from the subject, and display ECG Signals in real-time through IoT devices such as mobiles. The work diagnoses such as classifying the subject to having Arrhythmia, Congestive Heart Failure and Normal Sinus Rhythm. The prototype is to be equipped with a pre-trained Deep Learning model, which helps in the classification of ECG signals obtained from the subject, thus helping in providing a quick and basic insight into the would-be medical care needed by the subject. The prototype is to be equipped with a pre-trained Deep Learning model, which helps in the classification of ECG signals obtained from the subject, thus helping in providing a quick and basic insight into the would-be medical care needed by the subject.