Classification techniques on computerized systems to predict and/or to detect Apnea: A systematic review. (March 2017)

• Record Type: Journal Article
• Authors: Pombo, Nuno; Garcia, Nuno; Bousson, Kouamana
• Is Part Of: Computer methods and programs in biomedicine. Volume 140(2017)
• Page Start: 265
• Page End: 274
• Publication Date: 2017-03
• Subjects: Sleep apnea -- Machine learning -- Classification -- Threshold-based classification -- Systematic review; AI Apnea Index -- AHI Apnea and Hypopnea Index -- AIRS Artificial Immune Recognition System -- ANN Artificial Neural Network -- ANFIS Adaptive Neuro-Fuzzy Inference System -- AUC Area Under receiver operating characteristic Curve -- BHC Binary Hierarchical Classification -- BNN Bayesian Neural Network -- CAS Central Sleep Apnea -- ECOC Error Correcting Output Code -- ECG Electrocardiogram -- EEG Electroencephalogram -- EMG Electromyography -- EOG electrooculography -- FP False Positive -- FN False Negative -- HI Hypopnea Index -- HMM Hidden Markov Model -- KNN K-Nearest Neighbor -- LDA Linear Discriminant Analysis -- LS-SVM Least Squares Support Vector Machine -- LR Logistic Regression -- LVQ Learning Vector Quantization -- ML Machine Learning -- MLR Multi-Linear Regression -- MSA Mixed Sleep Apnea -- NARX Nonlinear AutoRegressive network with eXogenous -- OSA Obstructive Sleep Apnea -- PNN Probabilistic Neural Network -- NPV Negative Predictive Value -- PPG Photoplethysmogram -- PPV Positive Predictive Value -- PSG Polysomnogram -- RBFNN Radial Basis Function Neural Network -- RCT Randomized Controlled Trial -- RDI Respiratory Disturbance Index -- ROC receiver operating characteristic -- SAS Sleep Apnea Syndrome -- Sp02 Oxygen Saturation -- SRN Simple Recurrent Network -- SVM Support Vector Machine -- TP True Positive -- TN True Negative -- VDA Voice Activity Detection; Medicine -- Computer programs -- Periodicals; Biology -- Computer programs -- Periodicals; Computers -- Periodicals; Medicine -- Periodicals; Médecine -- Logiciels -- Périodiques; Biologie -- Logiciels -- Périodiques; Biology -- Computer programs; Medicine -- Computer programs; Periodicals; Electronic journals; 610.28
• DOI: 10.1016/j.cmpb.2017.01.001
• Languages: English
• ISSNs: 0169-2607

Abstract

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.