ECG signal classification using Machine Learning

EKG Analysis code for the MI3 intern group at CHOC Children's

In the realm of EKG/ECG analysis, deep learning models have made significant strides. However, the pursuit of efficiency and accuracy persists. The proposed Mamba Biometric EKG Analysis Technology (MambaBEAT) project aims to utilize the Mamba model, a promising sequence modeling architecture, to further advance EKG analysis.

Research project developing a novel image-based method for neural network heart attack diagnosis.

Filter raw electrocardiograms(EKG), build a characteristic array based on their harmonics (decompose signal with FFT, extract amplitude/phase) and match the input heartbeat with database entries by Euclidean norm.

Practicum Project : EKG Anomaly Identification utilizing Machine learning. Collaborated with 3 other Classmates under the supervision of an industry mentor and Professor for the project. Part of the project entailed Literature review of the relevant industry published Material. We utilized Python;

MATLAB app made in school subject two years ago

ploting electrocardiogram from MOD_EKG device

This project uses Python to process electrocardiogram (ECG or EKG) signals and calculate heart rate (HR) through biomedical signal processing techniques. It includes noise filtering and R-peak detection for accurate HR analysis. The project features a user-friendly graphical interface to visualize ECG data and heart rate results.

An application, developed in Angular 4, Nodejs and Python, to detect Arrhythmia from Live ECG Signal using Machine Learning techniques

HeartSense is an interactive LED ring that visually represents your heartbeats in real-time. The LED ring blinks to your heartbeats and displays the current BPM (beats per minute) using both numerical values and color-coded illumination.

Repository contains my MATLAB files for the hand-coded Myocardial-Infarction detection model trained on EKG data whose features were carefully engineered for the EEL5813 - Neural Networks: Algorithms and Applications course, PROJECT03