Smart ECG using deep learning

Project Title

Project Area of Specialization

Artificial Intelligence

Project Summary

In this project, we investigate the most recent automatic detecting algorithms on abnormal electrocardiogram (ECG) in a variety of cardiac arrhythmias. We present typical examples of a medical case study and technical applications related to diagnosing ECG, which include (i) a recently patented data classifier on the basis of deep learning model, (ii) a deep neural network scheme to diagnose variable types of arrhythmia ,Our work establishes a cross-area study, which relates artificial intelligence (AI), deep learning. Experimental results display the technical advantages such as saving cost, better reliability, and higher accuracy of deep learning-based models in contrast to conventional schemes on cardiac diagnosis.

Project Objectives

In our project, we have established a study on deep learning theory related to automatic diagnosis on abnormal electrocardiogram (ECG). We briefly introduced the most recent automatic detecting schemes such as convolutional neural networks (CNN) which aims on analyzing different types of cardiac arrhythmias. We presented an investigation of practical examples and applications of deep learning on automatic ECG diagnosis. We combined the theoretical concepts of artificial intelligence. Technical advantages such as low-power consumption, higher accuracy, better reliability, and cost saving on the links of feasible software/hardware implementations to automatic cardiac arrhythmia diagnosis prospects broader applications of deep learning on ECG and other data analytics on medical imaging.

Project Implementation Method

heart patents

Benefits of the Project

Helpful for heart patients

easy for layman person to understand ECG

Technical Details of Final Deliverable

The technical advantages such as saving cost, better reliability, and higher accuracy of deep learning-based models in contrast to conventional schemes on cardiac diagnosis.