Predicting Arrhythmia from ECG Signal Using Artificial Intelligence

Ziwen Jia

working interface of my research.

Working interface of Ziwen's research.

Undergraduate Student Project

Introduction

Did you wear a bracelet heartbeat monitor in hospital when the doctor need to monitor your heartbeat condition?

My name is Ziwen Jia and I am a senior electrical engineering student major at UB. I am very interested in the integrated circuit design. In summer 2019, I started working with Dr. Arindam Sanyal. With Dr. Sanyal as my mentor, I designed a machine learning model in MATLAB using for prediction of arrhythmia based on the given ECG signal.

The prediction of arrhythmia is important for medical treatment. Compared with the traditional heartbeat monitor, monitor with this technology could predict if the patient's heart beat is normal or abnormal, which can help doctors save time on manually diagnosing. I believe that this technology could release lots of pressure on doctors.

Abstract

This research concentrates on the prediction of arrhythmia from ECG signal using artificial intelligence in MATLAB.

See the Full Poster

Click on the file below to see the full poster in your browser. 

Digital Accessibility

The University at Buffalo is committed to ensuring digital accessibility for people with disabilities. We are continually improving the user experience for everyone, and applying the relevant accessibility standards to ensure we provide equal access to all users. If you experience any difficulty in accessing the content or services on this website, or if you have suggestions about improving the user experience, please contact the Experiential Learning Network via email (ubeln@buffalo.edu) or phone (716-645-8177).