An Approach Towards Real-Time Arrhythmia Detection using Deep Learning Techniques

Arrhythmia refers to any abnormality in the heart’s electrical activity that disrupts its regular rhythm. In the absence of prompt detection and treatment, arrhythmias can cause serious health complications. An Arrhythmia Detection System has been developed as a real-time healthcare application designed to detect and classify arrhythmias in patients. The proposed method uses Kafka for real-time data streaming to get the data from devices directly and applies deep learning techniques like LSTM for analysing electrocardiograms and classifying the readings as “normal” or “abnormal” near to real time. The developed model is also compared with various existing works including Arrhythmia detection using CNN, SVM. Data Streaming for near to real time prediction with high accuracy is the novelty achieved for the current work.