Classification techniques on computerized systems to predict and/or to detect Apnea: A systematic review. (March 2017) - Pombo, Nuno; Garcia, Nuno; Bousson, Kouamana
Highlights: We provide a comprehensive review of the existing state of the art of classification models for apnea. We identify several limitations and open problems in the computerized decision for apnea. A classification model should provide an auto-adaptive and no external-human action dependency. The accuracy of the classification models is related with the effective features selection. RCTs and validation of models using a large and multiple sample of data are recommended. 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.