ECG Monitoring Using Deep Learning – A Review

Abstract

Electrocardiogram (ECG) is used to detect heart's rhythm and electrical activity. Heart rhythm problems (heart arrhythmias) occur when the electrical signals that coordinate the heart's beats don't work properly and faulty signaling causes the heart to beat too fast, too slow or irregularly. Patients facing arrhythmia have no indications of having an arrhythmia and only a doctor will be able to diagnose arrhythmias in a routine test. Therefore, low cost portable monitoring system plays a important role. The main objective of this project is to design and develop a model for predicting arrhythmia (atrial fibrillation) along with monitoring the ECG signals. To create an arrhythmia prediction model and an ECG monitoring system, Deep Learning algorithms, TensorFlow and Keras library are applied here. The system is being designed with Raspberry pi 3, Arduino UNO, AD8232 single lead ECG sensor, biomedical sensor pad and battery. This system will be helpful for remote ECG monitoring and make easier for doctors to monitor the ECG of their patients outside the hospital. Key Words: ECG, Deep Learning, arrhythmia.