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Barroso-García V, Fernández-Poyatos M, Sahelices B, Álvarez D, Gozal D, Hornero R, Gutiérrez-Tobal GC. Prediction of the Sleep Apnea Severity Using 2D-Convolutional Neural Networks and Respiratory Effort Signals. Diagnostics (Basel) 2023; 13:3187. [PMID: 37892008 PMCID: PMC10605440 DOI: 10.3390/diagnostics13203187] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2023] [Revised: 09/30/2023] [Accepted: 10/10/2023] [Indexed: 10/29/2023] Open
Abstract
The high prevalence of sleep apnea and the limitations of polysomnography have prompted the investigation of strategies aimed at automated diagnosis using a restricted number of physiological measures. This study aimed to demonstrate that thoracic (THO) and abdominal (ABD) movement signals are useful for accurately estimating the severity of sleep apnea, even if central respiratory events are present. Thus, we developed 2D-convolutional neural networks (CNNs) jointly using THO and ABD to automatically estimate sleep apnea severity and evaluate the central event contribution. Our proposal achieved an intraclass correlation coefficient (ICC) = 0.75 and a root mean square error (RMSE) = 10.33 events/h when estimating the apnea-hypopnea index, and ICC = 0.83 and RMSE = 0.95 events/h when estimating the central apnea index. The CNN obtained accuracies of 94.98%, 79.82%, and 81.60% for 5, 15, and 30 events/h when evaluating the complete apnea hypopnea index. The model improved when the nature of the events was central: 98.72% and 99.74% accuracy for 5 and 15 events/h. Hence, the information extracted from these signals using CNNs could be a powerful tool to diagnose sleep apnea, especially in subjects with a high density of central apnea events.
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Affiliation(s)
- Verónica Barroso-García
- Biomedical Engineering Group, University of Valladolid, 47011 Valladolid, Spain; (M.F.-P.); (D.Á.); (R.H.); (G.C.G.-T.)
- Centro de Investigación Biomédica en Red en Bioingeniería, Biomateriales y Nanomedicina (CIBER-BBN), 47011 Valladolid, Spain
| | - Marta Fernández-Poyatos
- Biomedical Engineering Group, University of Valladolid, 47011 Valladolid, Spain; (M.F.-P.); (D.Á.); (R.H.); (G.C.G.-T.)
| | - Benjamín Sahelices
- Electronic Devices and Materials Characterization Group, Department of Computer Science, University of Valladolid, 47011 Valladolid, Spain;
| | - Daniel Álvarez
- Biomedical Engineering Group, University of Valladolid, 47011 Valladolid, Spain; (M.F.-P.); (D.Á.); (R.H.); (G.C.G.-T.)
- Centro de Investigación Biomédica en Red en Bioingeniería, Biomateriales y Nanomedicina (CIBER-BBN), 47011 Valladolid, Spain
| | - David Gozal
- Office of The Dean, Joan C. Edwards School of Medicine, Marshall University, 1600 Medical Center Drive, Huntington, WV 25701, USA;
| | - Roberto Hornero
- Biomedical Engineering Group, University of Valladolid, 47011 Valladolid, Spain; (M.F.-P.); (D.Á.); (R.H.); (G.C.G.-T.)
- Centro de Investigación Biomédica en Red en Bioingeniería, Biomateriales y Nanomedicina (CIBER-BBN), 47011 Valladolid, Spain
| | - Gonzalo C. Gutiérrez-Tobal
- Biomedical Engineering Group, University of Valladolid, 47011 Valladolid, Spain; (M.F.-P.); (D.Á.); (R.H.); (G.C.G.-T.)
- Centro de Investigación Biomédica en Red en Bioingeniería, Biomateriales y Nanomedicina (CIBER-BBN), 47011 Valladolid, Spain
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Tyagi PK, Agarwal D. Systematic review of automated sleep apnea detection based on physiological signal data using deep learning algorithm: a meta-analysis approach. Biomed Eng Lett 2023; 13:293-312. [PMID: 37519869 PMCID: PMC10382448 DOI: 10.1007/s13534-023-00297-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2023] [Revised: 06/10/2023] [Accepted: 06/18/2023] [Indexed: 08/01/2023] Open
Abstract
Sleep apnea (SLA) is a respiratory-related sleep disorder that affects a major proportion of the population. The gold standard in sleep testing, polysomnography, is costly, inconvenient, and unpleasant, and it requires a skilled professional to score. Multiple researchers have suggested and developed automated scoring processes with less detectors and automated classification algorithms to resolve these problems. An automatic detection system will allow for a high diagnosis rate and the analysis of additional patients. Deep learning (DL) is achieving high priority due to the availability of databases and recently developed methods. As the most up-and-coming technique for classification and generative tasks, DL has shown its significant potential in 2-dimensional clinical image processing studies. However, physiological information collected as 1-dimensional data has yet to be effectively extracted from this new approach to achieve the needed medical goals. So, in this study, we review the most recent studies in the field of DL applied to physiological data based on pulse oxygen saturation, electrocardiogram, airflow, and sound signal. A total of 47 articles from different journals and publishing houses that were published between 2012 and 2022 were identified. The primary objective of this work is to perform a comprehensive analysis to analyze, classify, and compare the main characteristics of deep-learning algorithms applied in physiological data processing for SLA detection. Overall, our analysis provides comprehensive and detailed information for researchers looking to add to this field. The data input source, objective, DL network, training framework, and database references are the critical factors of the DL approach examined. These are the most critical variables that influence system performance. We categorized the relevant research studies in physiological sensor data analysis using the DL approach based on (1) Physiological sensor data aspects, like signal types, sampling frequency, and window size; and (2) DL model perspectives, such as learning structure and input data types. Supplementary Information The online version contains supplementary material available at 10.1007/s13534-023-00297-5.
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Affiliation(s)
- Praveen Kumar Tyagi
- Department of ECE, Maulana Azad National Institute of Technology, Bhopal, 462003 India
| | - Dheeraj Agarwal
- Department of ECE, Maulana Azad National Institute of Technology, Bhopal, 462003 India
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Valipour F, Esteki A. Pattern Classification of Hand Movement Tremor in MS Patients with DBS ON and OFF. J Biomed Phys Eng 2022; 12:21-30. [PMID: 35155289 PMCID: PMC8819264 DOI: 10.31661/jbpe.v0i0.1028] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2018] [Accepted: 11/14/2018] [Indexed: 12/04/2022]
Abstract
Background: Hand tremor is one of the consequences of MS disease degrading quality of patient’s life. Recently DBS is used as a prominent treatment to reduce this effect.
Evaluation of this approach has significant importance because of the prevalence rate of disease. Objective: The purpose of this study was the nonlinear analysis of tremor signal in order to evaluate the quantitative effect of DBS on reducing MS tremor and differentiating between
them using pattern recognition algorithms. Material and Methods: In this analytical study, nine features were extracted from the tremor signal. Through statistical analysis, the significance level of each feature was examined.
Finally, tremor signals were categorized by SVM, weighted KNN and NN classifiers. The performance of methods was compared with an ROC graph. Results: The results have demonstrated that dominant frequency, maximum amplitude and energy of the first IMF, deviation of the direct path, sample entropy and fuzzy entropy have the
potential to create a significant difference between the tremor signals. The classification accuracy rate of tremor signals in three groups for Weighted KNN, NN and SVM with Gaussian
and Quadratic kernels resulted in 95.1%, 93.2%, 91.3% and 88.3%, respectively. Conclusion: Generally, nonlinear and nonstationary analyses have a high potential for a quantitative and objective measure of MS tremor. Weighted KNN has shown the best performance
of classification with the accuracy of more than 95%. It has been indicated that DBS has a positive influence on reducing the MS tremor. Therefore, DBS can be used in the
objective improvement of tremor in MS patients.
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Affiliation(s)
- Fatemeh Valipour
- MSc, Department of Biomedical Engineering and Medical Physics, Faculty of Medicine, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Ali Esteki
- PhD, Department of Biomedical Engineering and Medical Physics, Faculty of Medicine, Shahid Beheshti University of Medical Sciences, Tehran, Iran
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Niroshana SMI, Zhu X, Nakamura K, Chen W. A fused-image-based approach to detect obstructive sleep apnea using a single-lead ECG and a 2D convolutional neural network. PLoS One 2021; 16:e0250618. [PMID: 33901251 PMCID: PMC8075238 DOI: 10.1371/journal.pone.0250618] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2020] [Accepted: 04/09/2021] [Indexed: 11/18/2022] Open
Abstract
Obstructive sleep apnea (OSA) is a common chronic sleep disorder that disrupts breathing during sleep and is associated with many other medical conditions, including hypertension, coronary heart disease, and depression. Clinically, the standard for diagnosing OSA involves nocturnal polysomnography (PSG). However, this requires expert human intervention and considerable time, which limits the availability of OSA diagnosis in public health sectors. Therefore, electrocardiogram (ECG)-based methods for OSA detection have been proposed to automate the polysomnography procedure and reduce its discomfort. So far, most of the proposed approaches rely on feature engineering, which calls for advanced expert knowledge and experience. This paper proposes a novel fused-image-based technique that detects OSA using only a single-lead ECG signal. In the proposed approach, a convolutional neural network extracts features automatically from images created with one-minute ECG segments. The proposed network comprises 37 layers, including four residual blocks, a dense layer, a dropout layer, and a soft-max layer. In this study, three time-frequency representations, namely the scalogram, the spectrogram, and the Wigner-Ville distribution, were used to investigate the effectiveness of the fused-image-based approach. We found that blending scalogram and spectrogram images further improved the system's discriminative characteristics. Seventy ECG recordings from the PhysioNet Apnea-ECG database were used to train and evaluate the proposed model using 10-fold cross validation. The results of this study demonstrated that the proposed classifier can perform OSA detection with an average accuracy, recall, and specificity of 92.4%, 92.3%, and 92.6%, respectively, for the fused spectral images.
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Affiliation(s)
- S. M. Isuru Niroshana
- Biomedical Information Engineering Lab, The University of Aizu, Aizuwakamatsu, Fukushima, Japan
| | - Xin Zhu
- Biomedical Information Engineering Lab, The University of Aizu, Aizuwakamatsu, Fukushima, Japan
| | - Keijiro Nakamura
- Division of Cardiovascular Medicine, Ohashi Medical Center, Toho University, Meguro, Tokyo, Japan
| | - Wenxi Chen
- Biomedical Information Engineering Lab, The University of Aizu, Aizuwakamatsu, Fukushima, Japan
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Li X, Ling SH, Su S. A Hybrid Feature Selection and Extraction Methods for Sleep Apnea Detection Using Bio-Signals. SENSORS 2020; 20:s20154323. [PMID: 32756353 PMCID: PMC7436101 DOI: 10.3390/s20154323] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/10/2020] [Revised: 07/06/2020] [Accepted: 07/31/2020] [Indexed: 11/16/2022]
Abstract
People with sleep apnea (SA) are at increased risk of having stroke and cardiovascular diseases. Polysomnography (PSG) is used to detect SA. This paper conducts feature selection from PSG signals and uses a support vector machine (SVM) to detect SA. To analyze SA, the Physionet Apnea Database was used to obtain various features. Electrocardiography (ECG), oxygen saturation (SaO2), airflow, abdominal, and thoracic signals were used to provide various frequency-, time-domain and non-linear features (n = 87). To analyse the significance of these features, firstly, two evaluation measures, the rank-sum method and the analysis of variance (ANOVA) were used to evaluate the significance of the features. These features were then classified according to their significance. Finally, different class feature sets were presented as inputs for an SVM classifier to detect the onset of SA. The hill-climbing feature selection algorithm and the k-fold cross-validation method were applied to evaluate each classification performance. Through the experiments, we discovered that the best feature set (including the top-five significant features) obtained the best classification performance. Furthermore, we plotted receiver operating characteristic (ROC) curves to examine the performance of the SVM, and the results showed the SVM with Linear kernel (regularization parameter = 1) outperformed other classifiers (area under curve = 95.23%, sensitivity = 94.29%, specificity = 96.17%). The results confirm that feature subsets based on multiple bio-signals have the potential to identify patients with SA. The use of a smaller subset avoids dimensionality problems and reduces the computational load.
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Automated sleep apnea detection from cardio-pulmonary signal using bivariate fast and adaptive EMD coupled with cross time-frequency analysis. Comput Biol Med 2020; 120:103769. [PMID: 32421659 DOI: 10.1016/j.compbiomed.2020.103769] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2020] [Revised: 04/17/2020] [Accepted: 04/17/2020] [Indexed: 12/14/2022]
Abstract
Sleep apnea is a sleep related pathology in which breathing or respiratory activity of an individual is obstructed, resulting in variations in the cardio-pulmonary (CP) activity. The monitoring of both cardiac (heart rate (HR)) and pulmonary (respiration rate (RR)) activities are important for the automated detection of this ailment. In this paper, we propose a novel automated approach for sleep apnea detection using the bivariate CP signal. The bivariate CP signal is formulated using both HR and RR signals extracted from the electrocardiogram (ECG) signal. The approach consists of three stages. First, the bivariate CP signal is decomposed into intrinsic mode functions (IMFs) and residuals for both HR and RR channels using bivariate fast and adaptive empirical mode decomposition (FAEMD). Second, the features are extracted using time-domain analysis, spectral analysis, and time-frequency domain analysis of IMFs from CP signal. The time-frequency domain features are computed from the cross time-frequency matrices of IMFs of CP signal. The cross time-frequency matrix of each IMF is evaluated using the Stockwell (S)-transform. Third, the support vector machine (SVM) and the random forest (RF) classifiers are used for automated detection of sleep apnea with the features from the bivariate CP signal. Our proposed approach has demonstrated an average sensitivity and specificity of 82.27% and 78.67%, respectively for sleep apnea detection using the 10-fold cross-validation method. The approach has yielded an average sensitivity and specificity of 73.19% and 73.13%, respectively for the subject-specific cross-validation. The performance of the approach was compared with other CPC features used for the detection of sleep apnea.
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Prucnal MA, Polak AG. Analysis of Features Extracted from EEG Epochs by Discrete Wavelet Decomposition and Hilbert Transform for Sleep Apnea Detection. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2019; 2018:287-290. [PMID: 30440394 DOI: 10.1109/embc.2018.8512201] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
Sleep apnea (SA) is one of the most common disorders manifesting during sleep and the electroencephalo-gram (EEG) belongs to these biomedical signals that change during apnea and hypopnea episodes. In recent years, a few publications reported approaches to the automatic classification of sleep apnea episodes based only on the EEG. The purpose of this work was to analyze statistical features extracted from the EEG epochs by combined discrete wavelet transform (DWT) and Hilbert transform (HT). Additionally, the selected most discriminative 30 features were then used in the automatic classification of normal breathing and obstructive (OSA) and central (CSA) apnea by a feedforward neural network with 17+7 neurons in two hidden layers. This classifier returned the accuracy of 73.9% for the training and 77.3% for the testing set. The analysis of features extracted from EEG epochs revealed the importance of theta, beta and gamma brain waves.
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Identifying Obstructive, Central and Mixed Apnea Syndrome Using Discrete Wavelet Transform. ACTA ACUST UNITED AC 2019. [DOI: 10.1007/978-3-030-24322-7_3] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/20/2023]
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9
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Automated detection of sleep apnea using sparse residual entropy features with various dictionaries extracted from heart rate and EDR signals. Comput Biol Med 2019; 108:20-30. [DOI: 10.1016/j.compbiomed.2019.03.016] [Citation(s) in RCA: 33] [Impact Index Per Article: 6.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2019] [Revised: 03/15/2019] [Accepted: 03/16/2019] [Indexed: 11/22/2022]
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10
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Uddin MB, Su SW, Chen W, Chow CM. Dynamic changes in electroencephalogram spectral power with varying apnea duration in older adults. J Sleep Res 2019; 28:e12850. [PMID: 30931548 DOI: 10.1111/jsr.12850] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2018] [Revised: 02/21/2019] [Accepted: 02/24/2019] [Indexed: 11/28/2022]
Abstract
Sleep apnea elicits brain and physiological changes and its duration varies across the night. This study investigates the changes in the relative powers in electroencephalogram (EEG) frequency bands before and at apnea termination and as a function of apnea duration. The analysis was performed on 30 sleep records (375 apnea events) of older adults diagnosed with sleep apnea. Power spectral analysis centered on two 10-s EEG epochs, before apnea termination (BAT) and after apnea termination (AAT), for each apnea event. The relative power changes in EEG frequency bands were compared with changes in apnea duration, defined as Short (between 10 and 20 s), Moderate (between 20 and 30 s) and Long (between 30 and 40 s). A significant reduction in EEG relative powers for lower frequency bands of alpha and sigma were observed for the Long compared to the Moderate and Short apnea duration groups at BAT, and reduction in relative theta, alpha and sigma powers for the Long compared to the Moderate and Short groups at AAT. The proportion of apnea events showed a significantly decreased trend with increased apnea duration for non-rapid eye movement sleep but not rapid eye movement sleep. The proportion of central apnea events decreased with increased apnea duration, but not obstructive episodes. The findings suggest EEG arousal occurred both before and at apnea termination and these transient arousals were associated with a reduction in relative EEG powers of the low-frequency bands: theta, alpha and sigma. The clinical implication is that these transient EEG arousals, without awakenings, are protective of sleep. Further studies with large datasets and different age groups are recommended.
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Affiliation(s)
- Md B Uddin
- School of Biomedical Engineering, Faculty of Engineering and Information Technology, University of Technology Sydney, Sydney, Australia
| | - Steven W Su
- School of Biomedical Engineering, Faculty of Engineering and Information Technology, University of Technology Sydney, Sydney, Australia
| | - Wenhui Chen
- School of Biomedical Engineering, Faculty of Engineering and Information Technology, University of Technology Sydney, Sydney, Australia
| | - Chin Moi Chow
- Charles Perkins Centre, University of Sydney, Sydney, Australia.,Exercise, Heath & Performance Research Group, Faculty of Health Sciences, University of Sydney, Sydney, Australia
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Li X, Al-Ani A, Ling SH. Feature Selection for the Detection of Sleep Apnea using Multi-Bio Signals from Overnight Polysomnography. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2018; 2018:1444-1447. [PMID: 30440664 DOI: 10.1109/embc.2018.8512585] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Abstract
Patients with sleep apnea (SA) are at increased risk of stroke and cardiovascular disease. Diagnosis of sleep apnea depends on the standard overnight polysomnography (PSG). In this study, the DREAM Apnea Database was used to evaluate the importance of the various features proposed in the literature for the analysis of sleep apnea. Various timeand frequency- domain features that include wavelet and power spectral density were extracted from ECG, EMG, EEG, airflow, SaO2, abdominal and thoracic recordings. Evaluation measures of one-way analysis of variance (ANOVA) and Rank-Sum test were used to test the performance of different features. The selected feature subset indicated that frequency-domain features outperform time-domain ones. This study will help in enhancing the detection accuracy of sleep apnea for the various polysomnography signals.
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Uddin MB, Chow CM, Su SW. Classification methods to detect sleep apnea in adults based on respiratory and oximetry signals: a systematic review. Physiol Meas 2018; 39:03TR01. [DOI: 10.1088/1361-6579/aaafb8] [Citation(s) in RCA: 40] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/11/2023]
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13
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Empirical Wavelet Transform Based Features for Classification of Parkinson’s Disease Severity. J Med Syst 2017; 42:29. [DOI: 10.1007/s10916-017-0877-2] [Citation(s) in RCA: 25] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2017] [Accepted: 12/13/2017] [Indexed: 10/18/2022]
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Pombo N, Garcia N, Bousson K. Classification techniques on computerized systems to predict and/or to detect Apnea: A systematic review. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2017; 140:265-274. [PMID: 28254083 DOI: 10.1016/j.cmpb.2017.01.001] [Citation(s) in RCA: 21] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/13/2016] [Revised: 12/28/2016] [Accepted: 01/03/2017] [Indexed: 05/05/2023]
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.
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Affiliation(s)
- Nuno Pombo
- 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.
| | - Nuno Garcia
- 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.
| | - Kouamana Bousson
- Research Unit: LAETA/UBI-AEROG, Department of Aerospace Sciences, Universidade da Beira Interior, Covilhã, Portugal.
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Contact-Free Detection of Obstructive Sleep Apnea Based on Wavelet Information Entropy Spectrum Using Bio-Radar. ENTROPY 2016. [DOI: 10.3390/e18080306] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
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16
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Alvarez-Estevez D, Moret-Bonillo V. Computer-Assisted Diagnosis of the Sleep Apnea-Hypopnea Syndrome: A Review. SLEEP DISORDERS 2015; 2015:237878. [PMID: 26266052 PMCID: PMC4523666 DOI: 10.1155/2015/237878] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Received: 05/01/2015] [Revised: 06/15/2015] [Accepted: 06/21/2015] [Indexed: 02/07/2023]
Abstract
Automatic diagnosis of the Sleep Apnea-Hypopnea Syndrome (SAHS) has become an important area of research due to the growing interest in the field of sleep medicine and the costs associated with its manual diagnosis. The increment and heterogeneity of the different techniques, however, make it somewhat difficult to adequately follow the recent developments. A literature review within the area of computer-assisted diagnosis of SAHS has been performed comprising the last 15 years of research in the field. Screening approaches, methods for the detection and classification of respiratory events, comprehensive diagnostic systems, and an outline of current commercial approaches are reviewed. An overview of the different methods is presented together with validation analysis and critical discussion of the current state of the art.
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Affiliation(s)
| | - Vicente Moret-Bonillo
- Laboratory for Research and Development in Artificial Intelligence (LIDIA), Department of Computer Science, University of A Coruña, 15071 A Coruña, Spain
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Cabrerizo M, Ayala M, Goryawala M, Jayakar P, Adjouadi M. A new parametric feature descriptor for the classification of epileptic and control EEG records in pediatric population. Int J Neural Syst 2013; 22:1250001. [PMID: 23627587 DOI: 10.1142/s0129065712500013] [Citation(s) in RCA: 37] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
This study evaluates the sensitivity, specificity and accuracy in associating scalp EEG to either control or epileptic patients by means of artificial neural networks (ANNs) and support vector machines (SVMs). A confluence of frequency and temporal parameters are extracted from the EEG to serve as input features to well-configured ANN and SVM networks. Through these classification results, we thus can infer the occurrence of high-risk (epileptic) as well as low risk (control) patients for potential follow up procedures.
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Affiliation(s)
- Mercedes Cabrerizo
- Center for Advanced Technology and Education, College of Engineering and Computing, Florida International University, Miami, FL 33174, USA.
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