Automated ECG Arrhytmia Detection (Neural Network Classification Project)

This project focuses on building an automated system for detecting ECG arrhythmias using neural network–based classification.

The model processes ECG signals, learns relevant patterns, and classifies different heart rhythm conditions.

Key work in this project included data preprocessing, model training, performance evaluation, and visualization of classification results.

The project was implemented using Python and deep learning tools, with an emphasis on building a complete and organized machine learning pipeline.

Technologies used:

Python, Neural Networks, PyTorch, PyTorch Lightning, NumPy, Pandas, Matplotlib