Software developed to carry out the End-of-Degree Project PRAFAI (Prediction of Recurrence of Atrial Fibrillation using Artificial Intelligence).

A deep learning-based system for automatic detection of sleep apnea from ECG signals using a hybrid 1D CNN-BiLSTM architecture with an attention mechanism. Achieves high accuracy with minimal preprocessing, making it suitable for real-time, portable diagnostic applications.

Arrhythmia detector based on machine learning algorithms

Comparison between classical and enanched genetic programming in signals classification (ECG). Project for Machine Learning, developed in 2025.

One of the firsts dataset level explanability libraries for 1d signal using GRAD-CAM++

This project classifies ECG Signal as AF(Atrial Fibrillation) or Non-AF(All other rhythms).This project consist of 2 different models. A custom cnn and a transfer learning model. These model are doing the same thing with different approaches.

Scripts and modules for training and testing neural network for ECG automatic classification. Companion code to the paper "Automatic diagnosis of the 12-lead ECG using a deep neural network".

Clasificación de señales de Electrocardiogramas (ECG) mediante Deep Learning. Implementación basada y entrenada con el dataset MIT-BIH. Incluye una aplicación web interactiva con Flask.

Evaluation of Deep Learning models for detecting irregular heartbeat rhythms (arrhythmias) on electrocardiogram (ECG) measurements.

Oloche's AI Cardiologist is a deep learning web app for real-time automated classification of cardiac arrhythmias from raw ECG signals. Uses a custom 1D CNN trained on MIT-BIH database to classify heartbeats into five categories with confidence scores and visualizations for diagnostic support.

ML-based solution for ECG signal processing and diagnosis classification. Paper in SSPP 2025

Official Repo for, GRACE: Privacy-centric Multimodal Seizure Detection

Code repository of the paper: Electrocardiogram-Based Mental Stress Detection Amid Everyday Activities Using Machine Learning: Model Development and Validation Study. Published in the Journal of Medical Internet Research.

Determine the gender from a 12 Lead ECG signal with different machine learning algorithms.

Code repository of the paper: Beyond Supervision: Evaluating Contrastive Self-Supervised Learning Techniques for Electrocardiogram-Based Mental Stress Detection

Dual-channel CNN-LSTM for ECG arrhythmia classification (NSR, PAC, PVC) using raw ECG and derivative signals with Docker support

MIT-BIH ECG classification using 1D CNN with TensorFlow3

Python port of ecgScorer https://github.com/ecgScorer/ecgScorer