IHMS

IHMS is the repository for the Intelligent Heart Monitoring System. A Capstone project for the Queen's University Electrical Engineering program in Kingston, ON, CA.

What is the IHMS

The Intelligent Heart Monitoring System is a project that, using an ECG electrode sensor, will classify abnormal heartbeats in real-time.

Repository breakdown

IHMS

The IHMS folder is the final implementation of the IHMS system. This sections contains the code and scripts to run the IHMS system in full.

• Predict Arryhthmia's in Real-Time using complete system
• Real-time plotting and control using custom user interface
• Detailed visualization of saved data and arrhythmias

Dataset-Proc

Dataset-proc is the dataset processing component of the IHMS project. It uses signal processing techniques on raw ECG recordings to create datasets suitable for machine learning. The current raw data being used is from the PTB Diagnostic ECG Database and the MIT-BIH Arrhythmia Database. Purposes include but are not limited to:

• R-Peak Identification
• Heartbeat Extraction
• Signal Frequency Domain Filtering
• Signal Downsampling
• Appendage of Labels

Machine Learning

MachineLearning is the classifier creation and testing component of the IHMS project. In this section, machine learning suitable datasets are used to train machine learning models that can classify different types of arrhythmias. Purposes include but are not limited to:

• Training Deep Learning Models
• Model Validation
• Model Testing
• Model Performance Visualization

GoDirectSensor

GoDirectSensor is the sensor testing and implementation component of the IHMS project. In this section, the Vernier GoDirect ECG sensor is used to develop scripts for recording data in real-time and testing code using multiprocessing.

• Sensor I/O
• Multiprocessing and Threading
• Real-time data acquisitiion and processing

GUI

GUI is the component of the IHMS project where the user interface is developped and iterated upon.

• User Interface Layout Design
• GUI testing and development

Contributors

Shawn Carere

Queen's Electrical Engineering - Biomedical Option
Class of 2020

Specialties:

• Machine Learning
• Signal Processing
• Software Development

Ethan Fiset

Queen's Electrical Engineering
Class of 2020

Specialties:

• Sensors and Systems

Brad Kitzul

Queen's Electrical Engineering
Class of 2020

Specialties:

• Electronics

Archana Agashe

Queen's Electrical Engineering
Class of 2019 (Internship Year)

Specialties:

• Signal Processing