Aayush Singh
Pronouns: he/him
Research Mentor(s): Kayvan Najarian, Professor
Research Mentor School/College/Department: Department of Computational Medicine & Bioinformatics, Michigan Medicine
Presentation Date: Thursday, April 22, 2021
Session: Session 1 (10am-10:50am)
Breakout Room: Room 17
Presenter: 2
Abstract
Cardiac issues are serious medical issues that affect a large part of the population. Many studies have looked into using programs to automate the identification process and help discover symptoms early without the need for a doctor’s visit. The project focuses on developing machine-learning algorithms that can rapidly detect cardiac issues, specifically arrhythmias, to be implemented directly into motor vehicles. This project involves conducting training and testing of non-parametric machine learning algorithms on the publicly available MIT-BIH Arrhythmia Database. Models are created on MATLAB and tested based on statistics such as accuracy, specificity, and sensitivity through a confusion matrix. So far, decision tree based models have shown accuracies around 90% on smaller datasets after tuning various hyperparameters. Testing is to be continued with different models such as Support Vector Machines (SVM) and Convoluted Neural Networks (CNN) on larger datasets that will ideally result in significant improvements to the final model. These models will be better suited to accurately predict cardiac issues even with the extra signal noise when built into embedded systems within motor vehicles. These systems will add an extra layer of security to vehicles and help identify symptoms earlier than traditionally possible.
Authors: Aayush Singh, Zhi Li
Research Method: Computer Programming
