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Detalhes

Publicações

2019

Optical fiber-based sensing method for nanoparticle detection through supervised back-scattering analysis: a potential contributor for biomedicine

Autores
Paiva, JS; Jorge, PAS; Ribeiro, RSR; Sampaio, P; Rosa, CC; Cunha, JPS;

Publicação
International Journal of Nanomedicine

Abstract

2018

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

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

Publicação
Biochimica et Biophysica Acta (BBA) - General Subjects

Abstract

2018

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

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

Publicação
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

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

Publicação
Sensors

Abstract

2018

Cognitive impact and psychophysiological effects of stress using a biomonitoring platform

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

Publicação
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.