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About

About

During my master's degree I acquired knowledge in the field of Neuroscience, Applied Physics, Signal Processing, Brain imaging Techniques and machine learning. In my master thesis, I used signal processing methods and machine learning techniques and contributed by creating knowledge to the future development of systems for human attention training. I have conducted a study in order to identify the most reliable parameters in brain electrical (EEG) signals and ocular activity for predicting fluctuations in attention, and developed a classification platform based on those features. I collected EEG signals and eye measures from 21 healthy subjects. I concluded that phase coherence (synchrony) of EEG signals and pupil diameter can be used for predicting fluctuations in attention levels. I was also enrolled, in 2015, in a FCT Research Grant, in which I developed biometric ECG-based algorithms in a partnership with an industrial partner. 

Since January 2016 I have been developing a PhD research project within the scope of near-field optical probes for optogenetic applications in neurological diseases, such Parkinson, and other psychiatric diseases as Depression or Bipolar Disorders. I am currently studying applied photonics to biomedical applications. I am therefore extremely interested in and enthusiastic about pursuing further study in the field of Medical Applied Physics. I believe that my research experience in Applied Neuroscience’s field and Optics is a particular strength given my interest in following this research line. During my studies and my current research project, I have managed to integrate myself in several research teams, interacting with different persons and different working methods thus demonstrating important skills necessary to pursue the proposed research project.

During my studies at UC, I have attended to several conferences and courses of my research area. I have enrolled in an English Language Course of 120 hours at Faculty of Letters of the University of Coimbra (two semesters). In addition, I wrote some articles for the Physiscs Department Journal. 
I have also received two awards for being one of the top 3% students of the Integrated Master in Biomedical Engineering in 2013 and 2014. Recently, I also won a Full Travel Grant in order to attend to the International School on Light Sciences and Technologies, in Santander, Spain, 2016.

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Details

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Publications

2018

Optical Fiber Tips for Biological Applications: from Light Confinement, Biosensing to Bioparticles Manipulation

Authors
Paiva, JS; Jorge, PAS; Rosa, CC; Cunha, JPS;

Publication
Biochimica et Biophysica Acta (BBA) - General Subjects

Abstract

2018

Supervised learning methods for pathological arterial pulse wave differentiation: A SVM and neural networks approach

Authors
Paiva, JS; Cardoso, J; Pereira, T;

Publication
INTERNATIONAL JOURNAL OF MEDICAL INFORMATICS

Abstract
Objective: The main goal of this study was to develop an automatic method based on supervised learning methods, able to distinguish healthy from pathologic arterial pulse wave (APW), and those two from noisy waveforms (non-relevant segments of the signal), from the data acquired during a clinical examination with a novel optical system. Materials and methods: The APW dataset analysed was composed by signals acquired in a clinical environment from a total of 213 subjects, including healthy volunteers and non-healthy patients. The signals were parameterised by means of 39 pulse features: morphologic, time domain statistics, cross-correlation features, wavelet features. Multiclass Support Vector Machine Recursive Feature Elimination (SVM RFE) method was used to select the most relevant features. A comparative study was performed in order to evaluate the performance of the two classifiers: Support Vector Machine (SVM) and Artificial Neural Network (ANN). Results and discussion: SVM achieved a statistically significant better performance for this problem with an average accuracy of 0.9917 +/- 0.0024 and a F-Measure of 0.9925 +/- 0.0019, in comparison with ANN, which reached the values of 0.9847 +/- 0.0032 and 0.9852 +/- 0.0031 for Accuracy and F-Measure, respectively. A significant difference was observed between the performances obtained with SVM classifier using a different number of features from the original set available. Conclusion: The comparison between SVM and NN allowed reassert the higher performance of SVM. The results obtained in this study showed the potential of the proposed method to differentiate those three important signal outcomes (healthy, pathologic and noise) and to reduce bias associated with clinical diagnosis of cardiovascular disease using APW.

2018

Single Particle Differentiation through 2D Optical Fiber Trapping and Back-Scattered Signal Statistical Analysis: An Exploratory Approach

Authors
Paiva, JS; Ribeiro, RSR; Cunha, JPS; Rosa, CC; Jorge, PAS;

Publication
Sensors

Abstract

2018

Cognitive impact and psychophysiological effects of stress using a biomonitoring platform

Authors
Rodrigues, S; Paiva, JS; Dias, D; Aleixo, M; Filipe, RM; Cunha, JPS;

Publication
International Journal of Environmental Research and Public Health

Abstract
Stress can impact multiple psychological and physiological human domains. In order to better understand the effect of stress on cognitive performance, and whether this effect is related to an autonomic response to stress, the Trier Social Stress Test (TSST) was used as a testing platform along with a 2-Choice Reaction Time Task. When considering the nature and importance of Air Traffic Controllers (ATCs) work and the fact that they are subjected to high levels of stress, this study was conducted with a sample of ATCs (n = 11). Linear Heart Rate Variability (HRV) features were extracted from ATCs electrocardiogram (ECG) acquired using a medical-grade wearable ECG device (Vital Jacket® (1-Lead, Biodevices S.A, Matosinhos, Portugal)). Visual Analogue Scales (VAS) were also used to measure perceived stress. TSST produced statistically significant changes in some HRV parameters (Average of normal-to-normal intervals (AVNN), Standard Deviation of all NN (SDNN), root mean square of differences between successive rhythm-to-rhythm (RR) intervals (RMSSD), pNN20, and LF/HF) and subjective measures of stress, which recovered after the stress task. Although these short-term changes in HRV showed a tendency to normalize, an impairment on cognitive performance was evident. Despite that participant’s reaction times were lower, the accuracy significantly decreased, presenting more errors after performing the acute stress event. Results can also point to the importance of the development of quantified occupational health (qOHealth) devices to allow for the monitoring of stress responses. © 2018 by the authors. Licensee MDPI, Basel, Switzerland.

2018

Experimental and theoretical evaluation of the trapping performance of polymeric lensed optical fibers: single biological cells versus synthetic structures

Authors
Paiva, JI; Ribeiro, RSR; Jorge, PAS; Rosa, CC; Azevedo, MM; Sampaio, P; Cunha, JPS;

Publication
Biophotonics: Photonic Solutions for Better Health Care VI

Abstract