ECG Classification of Normal and Abnormal with GB-DBN Model (pytorch)

Implement an intelligent diagnostic system capable of accurately classifying cardiac activity. By analyzing ECG images or electronic readings, the system aims to detect various abnormalities, including distinguishing normal vs. abnormal heartbeats, identifying myocardial infarction (MI) and its history, and assessing the impact of COVID-19.

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

Authentication is an important factor to manage security. The traditional authentication methods are not secure enough to protect the user’s data. So, Using ECG signals as a biometric authentication method is a good solution to solve this problem.

Machine learning-based detection of cardiovascular disease using ECG signals: performance vs. complexity

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".

A transformer custom tailored for arrhythmia detection and based on ECG (Electrocardiogram) signals.

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

Classifying ecg signal to various types

Neural networks trained to categorize heartbeat ECG's using mitbit and ptbdb datasets

Применение машинного обучения для для предварительной диагностики патологий сердечного ритма

Archive for an AAI1001 project on Arrhythmia classification with a Temporal Convolutional Network with Grad-CAM Explainability

This repository contains the implementation of a novel approach to identify the subjects using PQRST fragments of the electrocardiogram (ECG) signal.

1D-CNN that is able to predict, in an inter-patient fashion, if a beat is Normal, Premature Ventricular Complex or Premature Atrial Complex relying on only a portion of the ECG.

• Developed and implemented ensemble-based machine learning models for ECG signal classification, enhancing accuracy and reliability. • Addressed challenges in data preprocessing, feature engineering, and class imbalance in ECG datasets. • Demonstrated the clinical implications of accurate ECG classification for enhanced patient care and diagnosis

Vit ECG image classification model

Classificação de séries temporais de sinais ECG com redes neurais convolucionais (CNN).

Classification of ecg signal using mitbih dataset