Telehealth

Revolutionizing healthcare through secure AI collaboration

Project Overview

The integration of AI and telehealth revolutionizes healthcare by enhancing diagnostic accuracy, treatment planning, and patient outcomes. Our solution leverages federated learning with ECG signal analysis to enable secure collaboration between health centers while maintaining patient data privacy.

Advanced AI Models for ECG Analysis
Enhanced Cardiac Diagnostic Accuracy
Secure ECG Data Protection
Real-time ECG Monitoring

Project Goals

  • Enhance ECG data security and privacy
  • Improve cardiac diagnostic accuracy through AI
  • Reduce cardiac healthcare disparities
  • Enable real-time ECG monitoring and analysis

Our Team

Tuy Nguyen

Project Sponsor

Professor Tuy Nguyen is currently an Assistant Professor at the School of Informatics, Computing, and Cyber Systems at Northern Arizona University. Previously, he held positions at Inha University and worked as a Senior Research Engineer at Conextt Inc.

Jiawen Zhao

Project Manager

Computer engineering student at Northern Arizona University focusing on machine learning and image processing. Experienced in Python, R, C, Javascript, Verilog, and Assembly.

Xiwei Wang

Team Treasurer

Computer engineering student specializing in FPGA projects and machine learning applications. Proficient in Python, Golang, C, Java, and Verilog.

Rudra Amin

Project Secretary

Senior at Northern Arizona University pursuing a bachelor's degree in computer engineering. Passionate about sports, particularly soccer, and brings diverse cultural perspective to the team.

Loren Larrieu

Consultant

Experienced consultant providing valuable insights and guidance to the project team. Specializes in healthcare technology integration and security protocols.

Our Technology Stack

Homomorphic Encryption

Advanced encryption technology that allows computations on encrypted data, ensuring privacy while maintaining functionality.

AI Models Based on ECG Signals

State-of-the-art machine learning models specialized in ECG signal analysis, enabling precise cardiac diagnosis and monitoring.

Target Customers

Hospitals

Large healthcare facilities seeking secure data collaboration

Medical Practices

Private practices looking to enhance diagnostic capabilities

Research Institutions

Organizations conducting healthcare research and development

Project Progress

Current Phase

Model Development

Progress 33%

Current Focus

Developing and training AI models for secure healthcare data analysis