Nuno Pombo1, Nuno Garcia2, Kouamana Bousson3. 1. Research Units: Instituto de Telecomunicações and ALLab Assisted Living Computing and Telecommunications Laboratory, Department of Informatics, Universidade da Beira Interior, Covilhã, Portugal and Universidade Lusófona de Humanidades e Tecnologias, Lisbon, Portugal. Electronic address: ngpombo@ubi.pt. 2. Research Units: Instituto de Telecomunicações and ALLab Assisted Living Computing and Telecommunications Laboratory, Department of Informatics, Universidade da Beira Interior, Covilhã, Portugal and Universidade Lusófona de Humanidades e Tecnologias, Lisbon, Portugal. Electronic address: ngarcia@di.ubi.pt. 3. Research Unit: LAETA/UBI-AEROG, Department of Aerospace Sciences, Universidade da Beira Interior, Covilhã, Portugal. Electronic address: bousson@ubi.pt.
Abstract
BACKGROUND AND OBJECTIVE: Sleep apnea syndrome (SAS), which can significantly decrease the quality of life is associated with a major risk factor of health implications such as increased cardiovascular disease, sudden death, depression, irritability, hypertension, and learning difficulties. Thus, it is relevant and timely to present a systematic review describing significant applications in the framework of computational intelligence-based SAS, including its performance, beneficial and challenging effects, and modeling for the decision-making on multiple scenarios. METHODS: This study aims to systematically review the literature on systems for the detection and/or prediction of apnea events using a classification model. RESULTS: Forty-five included studies revealed a combination of classification techniques for the diagnosis of apnea, such as threshold-based (14.75%) and machine learning (ML) models (85.25%). In addition, the ML models, were clustered in a mind map, include neural networks (44.26%), regression (4.91%), instance-based (11.47%), Bayesian algorithms (1.63%), reinforcement learning (4.91%), dimensionality reduction (8.19%), ensemble learning (6.55%), and decision trees (3.27%). CONCLUSIONS: A classification model should provide an auto-adaptive and no external-human action dependency. In addition, the accuracy of the classification models is related with the effective features selection. New high-quality studies based on randomized controlled trials and validation of models using a large and multiple sample of data are recommended.
BACKGROUND AND OBJECTIVE:Sleep apnea syndrome (SAS), which can significantly decrease the quality of life is associated with a major risk factor of health implications such as increased cardiovascular disease, sudden death, depression, irritability, hypertension, and learning difficulties. Thus, it is relevant and timely to present a systematic review describing significant applications in the framework of computational intelligence-based SAS, including its performance, beneficial and challenging effects, and modeling for the decision-making on multiple scenarios. METHODS: This study aims to systematically review the literature on systems for the detection and/or prediction of apnea events using a classification model. RESULTS: Forty-five included studies revealed a combination of classification techniques for the diagnosis of apnea, such as threshold-based (14.75%) and machine learning (ML) models (85.25%). In addition, the ML models, were clustered in a mind map, include neural networks (44.26%), regression (4.91%), instance-based (11.47%), Bayesian algorithms (1.63%), reinforcement learning (4.91%), dimensionality reduction (8.19%), ensemble learning (6.55%), and decision trees (3.27%). CONCLUSIONS: A classification model should provide an auto-adaptive and no external-human action dependency. In addition, the accuracy of the classification models is related with the effective features selection. New high-quality studies based on randomized controlled trials and validation of models using a large and multiple sample of data are recommended.
Authors: W M Faizal; N N N Ghazali; C Y Khor; Irfan Anjum Badruddin; M Z Zainon; Aznijar Ahmad Yazid; Norliza Binti Ibrahim; Roziana Mohd Razi Journal: Comput Methods Programs Biomed Date: 2020-06-26 Impact factor: 5.428
Authors: Merel M van Gilst; Johannes P van Dijk; Roy Krijn; Bertram Hoondert; Pedro Fonseca; Ruud J G van Sloun; Bruno Arsenali; Nele Vandenbussche; Sigrid Pillen; Henning Maass; Leonie van den Heuvel; Reinder Haakma; Tim R Leufkens; Coen Lauwerijssen; Jan W M Bergmans; Dirk Pevernagie; Sebastiaan Overeem Journal: BMJ Open Date: 2019-11-25 Impact factor: 2.692