Percorrer por autor "Guerreiro, Nathan"
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- Binary classification of cardiac pathologies using deep learning: a PTB-XL dataset approachPublication . Chaabani, Mohamed; Guerreiro, Nathan; Ribeiro, Luiz; Luiz, Luiz E.; Slim, Mohamed; Teixeira, João PauloCardiovascular diseases, such as myocardial infarction, are among the leading causes of death worldwide. Accuracy and time are crucial for diagnosing these conditions and for effective treatment, usually requiring time-consuming manual analysis of clinical-grade electrocardiogram (ECG). This paper presents a novel deep learning-based method for binary classification of cardiac patholo-gies using the PTB-XL dataset. The model integrates EfficientNetB3 for spatial feature extraction and a Linformer block to capture long-range dependencies be-tween leads. Preprocessing involves converting RGBA ECG images to RGB for-mat and normalizing them to meet the requirements of the inputs of the layers. Initial experiments have shown promising results, achieving an AUC (Area Un-der the Curve) of 86.06%. Further work includes tests to optimize the model's performance based on different key metrics, including accuracy and precision.
- Congestive heart failure detection in ECG using LSTM and CNNPublication . Ribeiro, Luiz; Guerreiro, Nathan; Chaabani, Mohamed Khalil; Luiz, Luiz E.; Lazzaretti, André; Teixeira, João PauloCongestive Heart Failure (CHF) is a chronic condition inm which the heart does not pump blood efficiently. This pathology causes fatigue, dyspnea, oedema, nausea, and memory problems, affecting patients’ quality of life. The causes include coronary artery disease, cardiomyopathy, arterial hypertension, and myocarditis. The diagnosis is usually based on the patient’s medical history, physical exams, echocardiogram, electrocardiogram, and other methods. Aiming to improve diagnostic tools, this study proposes an artificial intelligence model based on deep learning to classify pathological ECG signals indicative of CHF. The selected models were LSTM and CNN. The training was conducted using a personalised dataset created from the public databases BIDMC Congestive Heart Failure and PTB Diagnostic ECG from Physionet. ECG data from 28 individuals aged 22 to 71 were selected, including 14 with severe CHF (NYHA class 3 and 4) and 14 control samples without ECG abnormalities. The database architecture was designed so that the input to the neural networks was raw ECG signals without filtering or feature extraction. The results showed an accuracy of 98.21% for the CNN model and 92.26% for the LSTM model.
- Graphical user interface to acquire ECG using MATLAB and BITalinoPublication . Guerreiro, Nathan; Ribeiro, Luiz; Chaabani, Mohamed Khalil; Luiz, Luiz E.; Dajer, Maria Eugenia; Teixeira, João PauloThis study presents a MATLAB-based graphical user interface (GUI) designed to acquire, process, and visualise electrocardiogram (ECG) signals using the BITalino platform. The interface allows users, including healthcare professionals and researchers, to interactively monitor ECG signals in real-time, providing an accessible and effective tool for cardiac assessment. The system ensures noise reduction and signal clarity using a three-electrode configuration, Butterworth, and notch filters. Results highlight the GUI’s ability to display detailed and comprehensive ECG visualizations, aiding in identifying key ECG characteristics. This work demonstrates the potential for extending the system with machine learning algorithms for automated ECG pattern recognition.