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New University of Lisbon, Portugal

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44 Papers

Assessing Electrocardiogram Quality: A Deep Learning Framework For Noise Detection And Classification

Topics: Medical Signal Acquisition, Analysis and Processing; Pattern Recognition & Machine Learning for Biosignal Data

Authors: Márcia Monteiro , Mariana Dias and Hugo Gamboa

Affiliation: LIBPhys (Laboratory for Instrumentation, Biomedical Engineering and Radiation Physics), NOVA School of Science and Technology, Campus de Caparica, 2829-516, Portugal

Digital Object Identifier: 10.5220/0013313800003911

Proceedings of BIOSIGNALS 2025, ISBN: 978-989-758-731-3

Quick Abstract

The electrocardiogram (ECG) is an essential tool in the diagnosis of cardiovascular conditions. A common obstacle to readability and reliability is the vulnerability of ECG signals to noise, especially in wearable devices and long-term monitoring. Traditional methods have limited accuracy in noise detection, and, while deep learning (DL) shows promise, current models primarily focus on binary classification, lacking detailed quality analysis. This study proposes a DL model that assesses ECG signal quality, detecting and classifying specific noise types, with random-length noise segments added to clean 10-second signals to simulate real-world scenarios. The model, using gated recurrent units (GRUs), identifies three common noise types: baseline wander (BW), muscle artifacts (MA), and electrode motion (EM), achieving 98.09 % accuracy for BW, 92.62 % for MA, and 90.71 % for EM with F1 scores of 88.89 % for BW, 82.19 % for EM and 64.62 % for MA. It also surpasses existing DL methods, reaching 99.86 % accuracy for binary classification, with high recall and precision.

Intraoperative Electrocorticography Signal Synthesis to Improve the Classification of Epileptiform Tissue

Topic: Generative AI models Model Auditing

Authors: Leonor Almeida ¹ , Sem Hoogteijling ^{2

;

3} , Inês Silveira ¹ , Dania Furk ¹ , Irene Heijink ^{2

;

3} , Maryse Klooster ² , Hugo Gamboa ¹ , Luís Silva ¹ and Maeike Zijlmans ^{2

;

3}

Affiliations: ¹ Laboratório de Instrumentação, Engenharia Biomédica e Física da Radiação (LIBPhys-UNL), Departamento de Física, Faculdade de Ciências e Tecnologia, FCT, Universidade Nova de Lisboa, 2829-516 Caparica, Portugal , ² Department of Neurology and Neurosurgery, Brain Center, University Medical Center Utrecht, Part of ERN EpiCARE, P.O. box 85500, 3508 GA Utrecht, The Netherlands , ³ Stichting Epilepsie Instellingen Nederland (SEIN), The Netherlands

Digital Object Identifier: 10.5220/0013398500003911

Proceedings of SyntBioGen 2025, ISBN: 978-989-758-731-3

Quick Abstract

Epilepsy surgery is a viable option for treating drug-resistant cases where anti-seizure medications fail, but accurately localizing epileptic tissue remains challenging. This process can be guided by the visual assessment of intraoperative electrocorticography (ioECoG). Data scarcity limits developing machine learning (ML) models for automatic epileptic tissue classification. To address this, we propose a generative model based on Generative Adversarial Networks (GANs) to synthesize realistic ioECoG signals. Our approach identified three distinct ioECoG patterns using Agglomerative Clustering, which guided training individual Deep Convolutional Wasserstein GANs with Gradient Penalty (DCwGAN-GP). Synthetic data (SD) was evaluated across multiple dimensions: fidelity using temporal (e.g., Wasserstein distance (WD)), frequency and time-frequency metrics; diversity through dimensionality reduction; and utility by comparing ML performance with and without SD. It replicated temporal and frequency characteristics of real signals (fidelity), though lacked variability (diversity) due to potential data misclassifications. Specifically, the WD between real and synthetic signals outperformed literature benchmarks (i.e., 0.043 ± 0.025 vs. 0.078). Classifiers trained on a combination of real and SD achieved 88% accuracy, compared to 85% with real data alone. These results demonstrate the potential of SD to replicate real signals, address data scarcity, augment ioECoG datasets, and advance ML-based epilepsy surgery research.

Routine Pattern Learning and Anomaly Detection Applied to Lone Workers Through Topic Modeling

Topics: Pattern Recognition & Machine Learning for Biosignal Data; Biometrics, Detection and Identification; Motion and Activity Analysis

Authors: Ana Pereira ¹ , Marília Barandas ¹ and Hugo Gamboa ^{1

;

2}

Affiliations: ¹ Fraunhofer Portugal AICOS, Rua Alfredo Allen 455/461, 4200-135 Porto, Portugal , ² Laboratório de Instrumentação, Engenharia Biomédica e Física da Radiação (LIBPhys-UNL), Departamento de Física, Faculdade de Ciências e Tecnologia da Universidade Nova de Lisboa, Monte da Caparica, 2829-516 Caparica, Portugal

Digital Object Identifier: 10.5220/0013236600003911

Proceedings of BIOSIGNALS 2025, ISBN: 978-989-758-731-3

Quick Abstract

Learning routines and identifying anomalous behaviors play a critical role in worker safety. Identifying deviations from normal patterns helps prevent accidents, ensuring enhanced safety in complex environments. Topic modeling is frequently used to discover hidden semantics patterns and is well-suited to the complexity of routines in human behavior. However, its utility in complex time-series analysis and as a baseline for anomaly detection has not been widely explored. This work proposes a novel solution to accurately model complex routines using topic modeling, enabling the identification of anomalies through a statistical approach. A dataset of human movement recordings was collected over up to seven consecutive months, capturing the routines of three lone workers, with each accumulating between 414 and 955 hours of recording time. This dataset served as the basis for a comprehensive analysis of the results, showing strong alignment between visually observed patterns in routines and the outcomes of the proposed method. Additionally, detecting anomalies across models with varying training days confirms that online learning enhances the accuracy and adaptability of routine modeling. Topic modeling allows for in-depth learning of routines, capturing latent patterns undetectable to humans. This capability prevents abnormal events, leading to a proactive approach to predictive risk assessment.

Multimodal Stress Classification Based on Biosignals Extracted from Smart Devices and Electromyography

Topics: Medical Signal Acquisition, Analysis and Processing; Wearable Sensors and Systems; Detection and Identification; Pattern Recognition & Machine Learning for Biosignal Data

Authors: Maria Justino , Phillip Probst , Daniel Zagalo , Cátia Cepeda and Hugo Gamboa

Affiliation: LIBPhys (Laboratory for Instrumentation, Biomedical Engineering and Radiation Physics), Faculdade de Ciencias e Tecnologia, Universidade Nova de Lisboa, Caparica, Portugal

Digital Object Identifier: 10.5220/0011687200003414

Proceedings of BIOSIGNALS 2023/BIOSTEC, ISBN: 978-989-758-631-6

Quick Abstract

Work-Related Stress is the second most impactful occupational health problem in Europe, behind musculoskeletal diseases. When mental health is adequately handled, a worker’s well-being, performance, and productivity can be considerably improved. This paper presents machine learning models to classify mental stress experienced by office workers using physiological signals including heart rate, acquired using a smartwatch; respiration, derived from a smartphone’s acc placed on the chest; and trapezius electromyography, using proprietary electromyography sensors. Two interactive protocols were implemented to collect data from 12 individuals. Time features were extracted from heart rate and electromyography signals, with frequency features also being extracted from the latter. Statistical and temporal features were extracted from the derived respiration signal. Different input modalities were tested for the machine learning models: one for each physiological signal and a multimodal one, combining all of them. Three algorithms: Support Vector Machine, Random Forest, and K-Nearest-Neighbor were employed for mental stress classification. Random Forest obtained the best results (67.7%) for the heart rate model whereas K-Nearest-Neighbor attained higher accuracies for the respiration (89.1%) and electromyography (95.4%) models. Both algorithms achieved 100% accuracy for the multimodal model. A possible future approach would be to validate these models in real time.

Prediction of Sleep Stages Based on Wearable Signals Using Machine Learning Techniques

Topics: Wearable Sensors and Systems; Cardiovascular Signals; Neural Networks for Biosignal Data; Pattern Recognition & Machine Learning for Biosignal Data; Physiological Processes and Biosignal Modeling, Non-Linear Dynamics

Authors: Rodrigo Braga ¹ , Daniel Osório ^{1

;

2} and Hugo Gamboa ^{1

;

2}

Affiliations: ¹ Department of Physics, Faculdade de Ciências e Tecnologia da Universidade Nova de Lisboa, Monte da Caparica, 2892-516, Caparica, Portugal , ² Plux-Wireless Biosignals S.A, Avenida 5 de Outubro 70, 1050-59, Lisboa, Portugal

Digital Object Identifier: 10.5220/0011655200003414

Proceedings of BIOSIGNALS 2023/BIOSTEC, ISBN: 978-989-758-631-6

Quick Abstract

Sleep’s impact on mood and health is widely recognized by medical researchers with such understanding disseminating among average people in recent years. The main objective of this work was the development of machine learning algorithms for automatic sleep cycles detection. The features were selected based on the AASM manual, which is considered the gold standard for human technicians. For training the models we used MESA, a database containing 2056 full overnight unattended polysomnographies. With the goal of developing an algorithm that would only require a photoplethysmography (PPG) device to be able to accurately predict sleep stages and quality, the main channels used from this dataset were peripheral oxygen saturation and PPG. Testing the performance of Random forest, Gradient Boosting, Gaussian Naive-bayes, K-Nearest Neighbours, Support Vector Machine and Multilayer Perceptron classifiers, and using features extracted from the dataset, we achieved 80.50 % accuracy, 0.7586 Cohen’s kappa, and 77.38% F1-score, for five sleep stages, using a Multilayer Perceptron. To assess its performance in a real-world scenario we acquired sleep data and compared the classifications attributed by a popular sleep stage classification android app and our algorithm, resulting in a strong level of agreement (90.96% agreement, 0.8663 Cohen’s kappa), for four sleep stages.

Biomedical Text Mining: Applicability of Machine Learning-based Natural Language Processing in Medical Database

Topic: Detection and Identification

Authors: Nafiseh Mollaei , Catia Cepeda , Joao Rodrigues and Hugo Gamboa

Affiliation: Department of Physics, Faculdade de Ciencias e Tecnologia da Universidade Nova de Lisboa, Monte de Caparica, 2892-516, Caparica, Portugal

Digital Object Identifier: 10.5220/0010819500003123

Proceedings of BIOSIGNALS 2022/BIOSTEC, ISBN: 978-989-758-552-4

Quick Abstract

Machine learning has demonstrated superior performance in solving many problems in various fields of medicine compared to non-machine learning approaches. The aim of this review is to understand how Machine Learning-based Natural Language Processing (ML-NLP) has been applied to the clinical notes databases. Optimization algorithms are listed as examples to demonstrate the simplicity and effectiveness of their applications for clinical notes database. We reviewed the literature in clinical applications of ML-NLP, particularly techniques of deep learning such as mainly in pathology reports of diabetes, schizophrenia, cancer and cardiology, where NLP either on a classical algorithm or with deep learning has been actively adopted. We covered 60 different studies in this domain, focusing on a wide range of medical perspective based algorithms. Machine learning-based approaches combine the benefits of health systems with the expertise and experience of human well-being. From this review, it is clear that these techniques can improve the quantification of diagnosis and prognosis of cases and may create tools to assist patients during diagnosis and treatment. We complete this work by providing guidelines on the applicability of ML-NLP by describing the most relevant libraries to extract medical expressions from clinical reports text that can support clinical decision-making.

Classification of Volatile Compounds with Morphological Analysis of e-nose Response

Topics: Medical Signal Acquisition, Analysis and Processing; Pattern Recognition & Machine Learning for Biosignal Data

Authors: Rita Alves ^{1

;

2

;

3} , João Rodrigues ¹ , Efthymia Ramou ^{2

;

3} , Susana I. C. J. Palma ^{2

;

3} , Ana C. A. Roque ^{2

;

3} and Hugo Gamboa ¹

Affiliations: ¹ LIBPhys (Laboratory for Instrumentation, Biomedical Engineering and Radiation Physics), Faculdade de Ciências e Tecnologia, Universidade Nova de Lisboa, Caparica, Portugal , ² Associate Laboratory i4HB- Institute for Health and Bioeconomy, School of Science and Technology, NOVA University Lisbon, 2819-516 Caparica, Portugal , ³ UCIBIO – Applied Molecular Biosciences Unit, Department of Chemistry, School of Science and Technology, NOVA University Lisbon, 2819-516 Caparica, Portugal

Digital Object Identifier: 10.5220/0010827200003123

Proceedings of BIOSIGNALS 2022/BIOSTEC, ISBN: 978-989-758-552-4

Quick Abstract

Electronic noses (e-noses) mimic human olfaction, by identifying Volatile Organic Compounds (VOCs). This work presents a novel approach that successfully classifies 11 known VOCs using the signals generated by sensing gels in an in-house developed e-nose. The proposed signals’ analysis methodology is based on the generated signals’ morphology for each VOC since different sensing gels produce signals with different shapes when exposed to the same VOC. For this study, two different gel formulations were considered, and an average f1-score of 84% and 71% was obtained, respectively. Moreover, a standard method in time series classification was used to compare the performances. Even though this comparison reveals that the morphological approach is not as good as the 1-nearest neighbour with euclidean distance, it shows the possibility of using descriptive sentences with text mining techniques to perform VOC classification.

Prediction of Personal Characteristics and Emotional State based on Voice Signals using Machine Learning Techniques

Topics: Detection and Identification; Multimodal Speech & Audio Processing; Pattern Recognition & Machine Learning for Biosignal Data

Authors: Marta Babel Guerreiro ¹ , Catia Cepeda ¹ , Joana Sousa ^{2

;

3} , Carolina Maio ² , João Ferreira ² and Hugo Gamboa ¹

Digital Object Identifier: 10.5220/0010802700003123

Proceedings of BIOSIGNALS 2022/BIOSTEC, ISBN: 978-989-758-552-4

Quick Abstract

Voice signals are a rich source of personal information, leading to the main objective of the present work: study the possibility of predicting gender, age, and emotional valence through short voice interactions with a mobile device (a smartphone or remote control), using machine learning algorithms. For that, data acquisition was carried out to create a Portuguese dataset (consisting in 156 samples). Testing Support Vector Machine (SVM), K-Nearest Neighbors (KNN), and Random Forest (RF) classifiers and using features extracted from the audio, the gender recognition model achieved an accuracy of 87.8%, the age group recognition model achieved an accuracy of 67.6%, and an accuracy of 94.6% was reached for the emotion model. The SVM algorithm produced the best results for all models. The results show that it is possible to predict not only someone’s specific personal characteristics but also its emotional state from voice signals. Future work should be done in order to improve these models by increasing the dataset.

Eye-pointer Coordination in a Decision-making Task Under Uncertainty

Topics: Biometrics; Physiological Processes and Biosignal Modeling, Non-Linear Dynamics

Authors: Catia Cepeda ^{1

;

2} , Maria Camila Dias ¹ , Dina Rindlisbacher ^{2

;

3} , Marcus Cheetham ^{2

;

3} and Hugo Gamboa ¹

Affiliations: ¹ LIBPhys (Laboratory for Instrumentation, Biomedical Engineering and Radiation Physics), Faculdade de Ciências e Tecnologia, Universidade Nova de Lisboa, Caparica, Portugal , ² Department of Internal Medicine, University Hospital Zurich, Zurich, Switzerland , ³ University Research Priority Program "Dynamics of Healthy Aging", University Zurich, Zurich, Switzerland

Digital Object Identifier: 10.5220/0008867500300037

Proceedings of BIOSIGNALS 2020/BIOSTEC, ISBN: 978-989-758-398-8

Quick Abstract

Eye-tracking (ET) systems, which capture eye movements, are often used to measure human behavior while interacting with a user interface. Given the high costs and challenges of acquiring, installing and ensuring good calibration of ET systems, the use of pointer (or mouse) tracking is gaining interest as a viable alternative in research on human-computer interaction. In this study, we measured and evaluated temporal and spatial relationships between eye and pointer movements in a standardized task that allowed us to examine the relationship between eye and pointer movements while participants made decisions under conditions of high and low uncertainty. We collected data from N=81 participants and applied a range of metrics to a total of 5205 decision trials. The overall findings show that the convergence between eye and pointer movements is consistently high. Importantly, there are differences in levels of convergence depending on the temporal, spatial and combined temporo-spatial metrics used. There are also differences in eye-pointer convergence depending on the relative level of decision uncertainty in the task. In conclusion, the present findings favour the use of pointer tracking to analyse human-computer interaction in more complex tasks.

Predicting Response Uncertainty in Online Surveys: A Proof of Concept

Topics: Detection and Identification; Pattern Recognition & Machine Learning for Biosignal Data

Authors: Maria Camila Dias ¹ , Catia Cepeda ^{1

;

2} , Dina Rindlisbacher ^{2

;

3} , Edouard Battegay ^{2

;

3} , Marcus Cheetham ^{2

;

3} and Hugo Gamboa ¹

Affiliations: ¹ LIBPhys (Laboratory for Instrumentation, Biomedical Engineering and Radiation Physics), Faculdade de Ciências e Tecnologia, Universidade Nova de Lisboa, Caparica, Portugal , ² Department of Internal Medicine, University Hospital Zurich, Zurich, Switzerland , ³ Center of Competence Multimorbidity, University of Zurich, Zurich, Switzerland

Digital Object Identifier: 10.5220/0007381801550162

Proceedings of BIOSIGNALS 2019/BIOSTEC, ISBN: 978-989-758-353-7

Quick Abstract

Online questionnaire-based research is growing at a fast pace. Mouse-tracking methods provide a potentially important data source for this research by enabling the capture of respondents’ online behaviour while answering questionnaire items. This behaviour can give insight into respondents’ perceptual, cognitive and affective processes. The present work focused on the potential use of mouse movements to indicate uncertainty when answering questionnaire items and used machine learning methods as a basis to model these. N=79 participants completed an online questionnaire while mouse data was tracked. Mouse movement features were extracted and selected for model training and testing. Using logistic regression and k-fold cross-validation, the model achieved an estimated performance accuracy of 89%. The findings show that uncertainty is indicated by an increase in the number of horizontal direction inversions and the distance covered by the mouse and by longer interaction times with and a higher number of revisits to questionnaire items that evoked uncertainty. Future work should validate these methods further.