Percorrer por autor "Luiz, Luiz E."
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- The artificial intelligence act: insights regarding its application and implicationsPublication . Cabrera, Beatriz M.; Luiz, Luiz E.; Teixeira, João PauloThis paper deals with Europe's Artificial Intelligence Act, the first regulation on the subject and is considered an international milestone. Therefore, the introduction provides a historical overview of legislative developments on Artificial Intelligence in Europe until the current milestone, namely the approval of the AI Act text, which is currently being amended and translated into its official publication. Afterwards, the regulation is dealt with in detail; its nuances are presented, along with the conceptualisation of artificial intelligence in the regulation and the classification of artificial intelligence systems, which is based on risks to users, together with the mechanisms created to make the regulation more efficient, specifically the Artificial Intelligence Office. Ultimately, considering the great innovation on the subject, this work presents different opinions regarding the application of the regulation, its risk-based analysis and classification, expectations and views on the possible impacts of the act on the market, thereby seeking to expose society's receptiveness to the regulation created. Therefore, based on the discussion points, it can be concluded that the regulation, which will soon be in effect, brings different feelings to citizens and members of the European market, who are still insecure about the risk-based approach used, harbouring feelings of fear about the limitation of innovation. However, at the same time, there is hope, given that regulation is necessary to guarantee safe innovation in line with the fundamental rights set out by the European Union. It can also be concluded that the approval and forthcoming publication of the act is a small step towards the challenges that will arise. It is certain that, regardless of the different opinions that exist, it is necessary to start implementing the act to analyse its effects on the market, society, politics, the economy and, above all, on innovations in artificial intelligence systems.
- 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.
- High-resolution portable bluetooth module for ECG and EMG acquisitionPublication . Luiz, Luiz E.; Soares, Salviano; Valente, Antonio; Barroso, João; Leitão, Paulo; Teixeira, João PauloPortable ECG/sEMG acquisition systems for telemedicine often lack application flexibility (e.g., limited configurability, signal validation) and efficient wireless data handling. A modular biosignal acquisition system with up to 8 channels, 24-bit resolution and configurable sampling (1–4 kHz) is proposed, featuring per-channel gain/source adjustments, internal MUX-based reference drive, and visual electrode integrity monitoring; Bluetooth® transmits data via a bit-wise packet structure (83.92% smaller than JSON, 7.28 times faster decoding with linear complexity based on input size). Results: maximum 6.7 μVrms input-referred noise; harmonic signal correlations >99.99%, worst-case THD of -53.03 dBc, and pulse wave correlation >99.68% in frequency-domain with maximum NMSE% of 6e-6%; and 22.3-hour operation (3.3 Ah battery @ 150 mA). The system enables high-fidelity, power-efficient acquisition with validated signal integrity and adaptable multi-channel acquisition, addressing gaps in portable biosensing.
- Portable system and user interface for ECG and EMG acquisition, conditioning, and parameters extractionPublication . Luiz, Luiz E.; Silva, Wilson J. da; Soares, Salviano; Leitão, Paulo; Teixeira, João PauloElectrical signals from the human body are constantly the focus of research, searching for a better understanding of physiological events, discovering early signs of disease, and improving life quality. Developing devices that allow research beyond the usual requirements and allow easy adaptation regarding the system's acquisition, conditioning, and processing parts could be a step toward better understanding some behaviours. Therefore, this work focused on designing a system that goes from the discrete components to a graphical interface, allowing user control of parameters to set the acquisition in a way that favours the specific signal of interest. The resulting system allows the user to change parameters regarding the analogue and digital filtering, sampling frequency and events detection, namely R-peaks, in the electrocardiogram, to estimate heart rate and its frequency fluctuation, and muscle contraction in electromyogram, to analyse contraction strength and muscle fatigue. The system also allows the data to be stored, aiming to generate datasets to further process the data in AI-based algorithms.