F
Fábio Mendonça
Researcher at Madeira Interactive Technologies Institute
Publications - 35
Citations - 618
Fábio Mendonça is an academic researcher from Madeira Interactive Technologies Institute. The author has contributed to research in topics: Polysomnography & Computer science. The author has an hindex of 9, co-authored 26 publications receiving 299 citations. Previous affiliations of Fábio Mendonça include University of Madeira & Instituto Superior Técnico.
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Journal ArticleDOI
A Review of Obstructive Sleep Apnea Detection Approaches
Fábio Mendonça,Sheikh Shanawaz Mostafa,Antonio G. Ravelo-García,Fernando Morgado-Dias,Thomas Penzel +4 more
TL;DR: The objective of this review is to analyze already existing algorithms that have not been implemented on hardware but have had their performance verified by at least one experiment that aims to detect obstructive sleep apnea to predict trends.
Journal ArticleDOI
A systematic review of detecting sleep apnea using deep learning
TL;DR: The goal of this work is to analyze the published research in the last decade, providing an answer to the research questions such as how to implement the different deep networks, what kind of pre-processing or feature extraction is needed, and the advantages and disadvantages of different kinds of networks.
Journal ArticleDOI
Devices for home detection of obstructive sleep apnea: A review
Fábio Mendonça,Sheikh Shanawaz Mostafa,Sheikh Shanawaz Mostafa,Antonio G. Ravelo-García,Fernando Morgado-Dias,Thomas Penzel +5 more
TL;DR: The objective of this research is to review publications that show the performance of different devices for ambulatory diagnosis of sleep apnea, and to determine the sensors that provided the best results.
Journal ArticleDOI
A Review of Approaches for Sleep Quality Analysis
Fábio Mendonça,Sheikh Shanawaz Mostafa,Fernando Morgado-Dias,Antonio G. Ravelo-García,Thomas Penzel +4 more
TL;DR: It was verified that despite the convenience and considerable popularity among the consumers of home health monitoring of devices, such as actigraphs, the validity of these tools regarding the estimation of sleep quality still needs to be systematically examined.
Proceedings ArticleDOI
SpO2 based sleep apnea detection using deep learning
TL;DR: A Deep Belief Network is used for feature extraction, without using domain-specific knowledge, and then the same network isused for classification of sleep apnea, and the optimum number of hidden neurons of this problem is found using a search technique.