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I obtained my integrated master's degree in Biomedical Engineering in 2018 at the University of Minho. The last two years of my degree were focused on medical informatics. Then, I joined HASLab in 2018, alongside the development of my master thesis named "Cloud-based Analytics for Monitoring and Classification of Arrhythmia".  Currently, I am a PhD student at HASLab/INESC TEC and I am enrolled in the doctoral program in Informatics (PDInf) in University of Minho. My main research interests fall into distributed and privacy-preserving machine learning. 



  • Name

    Cláudia Vanessa Brito
  • Cluster

    Computer Science
  • Role

    Research Assistant
  • Since

    01st October 2018


The Case for Storage Optimization Decoupling in Deep Learning Frameworks

Macedo, R; Correia, C; Dantas, M; Brito, C; Xu, W; Tanimura, Y; Haga, J; Paulo, J;

2021 IEEE International Conference on Cluster Computing (CLUSTER)



Electrocardiogram beat-classification based on a ResNet network

Brito, C; Machado, A; Sousa, A;

Studies in Health Technology and Informatics

When dealing with electrocardiography (ECG) the main focus relies on the classification of the heart's electric activity and deep learning has been proving its value over the years classifying the heartbeats, exhibiting great performance when doing so. Following these assumptions, we propose a deep learning model based on a ResNet architecture with convolutional 1D layers to classify the beats into one of the 4 classes: normal, atrial premature contraction, premature ventricular contraction and others. Experimental results with MIT-BIH Arrhythmia Database confirmed that the model is able to perform well, obtaining an accuracy of 96% when using stochastic gradient descent (SGD) and 83% when using adaptive moment estimation (Adam), SGD also obtained F1-scores over 90% for the four classes proposed. A larger dataset was created and tested as unforeseen data for the trained model, proving that new tests should be done to improve the accuracy of it. © 2019 International Medical Informatics Association (IMIA) and IOS Press. This article is published online with Open Access by IOS Press and distributed under the terms of the Creative Commons Attribution Non-Commercial License 4.0 (CC BY-NC 4.0).


Assessment of an IoT platform for data collection and analysis for medical sensors

Rei, J; Brito, C; Sousa, A;

Proceedings - 4th IEEE International Conference on Collaboration and Internet Computing, CIC 2018

Health facilities produce an increasing and vast amount of data that must be efficiently analyzed. New approaches for healthcare monitoring are being developed every day and the Internet of Things (IoT) came to fill the still existing void on real-time monitoring. A new generation of mechanisms and techniques are being used to facilitate the practice of medicine, promoting faster diagnosis and prevention of diseases. We proposed a system that relies on IoT for storing and monitoring medical sensors data with analytic capabilities. To this end, we chose two approaches for storing this data which were thoroughly evaluated. Apache HBase presents a higher rate of data ingestion, when collaborating with the Kaa IoT platform, than Apache Cassandra, exhibiting good performance storing unstructured data, as presented in a healthcare environment. The outcome of this system has shown the possibility of a large number of medical sensors being simultaneously connected to the same platform (6000 records sent by the second or 48 ECG sensors with a frequency of 125Hz). The results presented in this paper are promising and should be further investigated as a comprehensive system would benefit the patient's diagnosis but also the physicians. © 2018 IEEE.