Machine Learning on ECG to predict heart-beat classification.

Arrhythmia detection using topological data analysis in combination with a convolutional neural network.

An investigation into tabular classification with deep NNs for ETHZ Machine Learning for Healthcare on the MIT-BIH arrythmia dataset .

Newton–Puiseux for CVNNs: complete toolkit for uncertainty mining, confidence calibration and local symbolic-numeric analysis on ECG (MIT-BIH) and wireless IQ data (RadioML 2016.10A).

MIT-BIH Arrhythmia Classification

Deep learning model for automated classification of cardiac arrhythmias using ECG signals from the MIT-BIH database. The project combines signal preprocessing via wavelet transform and a multi-layer CNN architecture, achieving over 98% test accuracy across 15 heartbeat classes. Designed for real-time and clinical applications.