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Yazdi M, Samaee M, Massicotte D. A Review on Automated Sleep Study. Ann Biomed Eng 2024; 52:1463-1491. [PMID: 38493234 DOI: 10.1007/s10439-024-03486-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2023] [Accepted: 02/25/2024] [Indexed: 03/18/2024]
Abstract
In recent years, research on automated sleep analysis has witnessed significant growth, reflecting advancements in understanding sleep patterns and their impact on overall health. This review synthesizes findings from an exhaustive analysis of 87 papers, systematically retrieved from prominent databases such as Google Scholar, PubMed, IEEE Xplore, and ScienceDirect. The selection criteria prioritized studies focusing on methods employed, signal modalities utilized, and machine learning algorithms applied in automated sleep analysis. The overarching goal was to critically evaluate the strengths and weaknesses of the proposed methods, shedding light on the current landscape and future directions in sleep research. An in-depth exploration of the reviewed literature revealed a diverse range of methodologies and machine learning approaches employed in automated sleep studies. Notably, K-Nearest Neighbors (KNN), Ensemble Learning Methods, and Support Vector Machine (SVM) emerged as versatile and potent classifiers, exhibiting high accuracies in various applications. However, challenges such as performance variability and computational demands were observed, necessitating judicious classifier selection based on dataset intricacies. In addition, the integration of traditional feature extraction methods with deep structures and the combination of different deep neural networks were identified as promising strategies to enhance diagnostic accuracy in sleep-related studies. The reviewed literature emphasized the need for adaptive classifiers, cross-modality integration, and collaborative efforts to drive the field toward more accurate, robust, and accessible sleep-related diagnostic solutions. This comprehensive review serves as a solid foundation for researchers and practitioners, providing an organized synthesis of the current state of knowledge in automated sleep analysis. By highlighting the strengths and challenges of various methodologies, this review aims to guide future research toward more effective and nuanced approaches to sleep diagnostics.
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Affiliation(s)
- Mehran Yazdi
- Laboratory of Signal and System Integration, Department of Electrical and Computer Engineering, Université du Québec à Trois-Rivières, Trois-Rivières, Canada.
- Signal and Image Processing Laboratory, School of Electrical and Computer Engineering, Shiraz University, Shiraz, Iran.
| | - Mahdi Samaee
- Signal and Image Processing Laboratory, School of Electrical and Computer Engineering, Shiraz University, Shiraz, Iran
| | - Daniel Massicotte
- Laboratory of Signal and System Integration, Department of Electrical and Computer Engineering, Université du Québec à Trois-Rivières, Trois-Rivières, Canada
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2
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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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Martín-González S, Ravelo-García AG, Navarro-Mesa JL, Hernández-Pérez E. Combining Heart Rate Variability and Oximetry to Improve Apneic Event Screening in Non-Desaturating Patients. SENSORS (BASEL, SWITZERLAND) 2023; 23:s23094267. [PMID: 37177472 PMCID: PMC10181515 DOI: 10.3390/s23094267] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/19/2022] [Revised: 04/17/2023] [Accepted: 04/18/2023] [Indexed: 05/15/2023]
Abstract
In this paper, we thoroughly analyze the detection of sleep apnea events in the context of Obstructive Sleep Apnea (OSA), which is considered a public health problem because of its high prevalence and serious health implications. We especially evaluate patients who do not always show desaturations during apneic episodes (non-desaturating patients). For this purpose, we use a database (HuGCDN2014-OXI) that includes desaturating and non-desaturating patients, and we use the widely used Physionet Apnea Dataset for a meaningful comparison with prior work. Our system combines features extracted from the Heart-Rate Variability (HRV) and SpO2, and it explores their potential to characterize desaturating and non-desaturating events. The HRV-based features include spectral, cepstral, and nonlinear information (Detrended Fluctuation Analysis (DFA) and Recurrence Quantification Analysis (RQA)). SpO2-based features include temporal (variance) and spectral information. The features feed a Linear Discriminant Analysis (LDA) classifier. The goal is to evaluate the effect of using these features either individually or in combination, especially in non-desaturating patients. The main results for the detection of apneic events are: (a) Physionet success rate of 96.19%, sensitivity of 95.74% and specificity of 95.25% (Area Under Curve (AUC): 0.99); (b) HuGCDN2014-OXI of 87.32%, 83.81% and 88.55% (AUC: 0.934), respectively. The best results for the global diagnosis of OSA patients (HuGCDN2014-OXI) are: success rate of 95.74%, sensitivity of 100%, and specificity of 89.47%. We conclude that combining both features is the most accurate option, especially when there are non-desaturating patterns among the recordings under study.
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Affiliation(s)
- Sofía Martín-González
- Institute for Technological Development and Innovation in Communications, Universidad de Las Palmas de Gran Canaria, 35017 Las Palmas de Gran Canaria, Spain
| | - Antonio G Ravelo-García
- Institute for Technological Development and Innovation in Communications, Universidad de Las Palmas de Gran Canaria, 35017 Las Palmas de Gran Canaria, Spain
- Interactive Technologies Institute (ITI/LARSyS and ARDITI), 9020-105 Funchal, Portugal
| | - Juan L Navarro-Mesa
- Institute for Technological Development and Innovation in Communications, Universidad de Las Palmas de Gran Canaria, 35017 Las Palmas de Gran Canaria, Spain
| | - Eduardo Hernández-Pérez
- Institute for Technological Development and Innovation in Communications, Universidad de Las Palmas de Gran Canaria, 35017 Las Palmas de Gran Canaria, Spain
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Liang Z. Novel method combining multiscale attention entropy of overnight blood oxygen level and machine learning for easy sleep apnea screening. Digit Health 2023; 9:20552076231211550. [PMID: 37936958 PMCID: PMC10627021 DOI: 10.1177/20552076231211550] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2023] [Accepted: 10/16/2023] [Indexed: 11/09/2023] Open
Abstract
Objective Sleep apnea is a common sleep disorder affecting a significant portion of the population, but many apnea patients remain undiagnosed because existing clinical tests are invasive and expensive. This study aimed to develop a method for easy sleep apnea screening. Methods Three supervised machine learning algorithms, including logistic regression, support vector machine, and light gradient boosting machine, were applied to develop apnea screening models at two apnea-hypopnea index cutoff thresholds: ≥ 5 and ≥ 30 events/hours. The SpO2 recordings of the Sleep Heart Health Study database (N = 5786) were used for model training, validation, and test. Multiscale entropy analysis was performed to derive a set of multiscale attention entropy features from the SpO2 recordings. Demographic features including age, sex, body mass index, and blood pressure were also used. The dependency among the multiscale attention entropy features were handled with the independent component analysis. Results For cutoff ≥ 5/hours, logistic regression model achieved the highest Matthew's correlation coefficient (0.402) and area under the curve (0.747), and reasonably good sensitivity (75.38%), specificity (74.02%), and positive predictive value (92.94%). For cutoff ≥ 30/hours, support vector machine model achieved the highest Matthew's correlation coefficient (0.545) and area under the curve (0.823), and good sensitivity (82.00%), specificity (82.69%), and negative predictive value (95.53%). Conclusions Our models achieved better performance than existing methods and have the potential to be integrated with home-use pulse oximeters.
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Affiliation(s)
- Zilu Liang
- Kyoto University of Advanced Science (KUAS), Japan
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Ayatollahi A, Afrakhteh S, Soltani F, Saleh E. Sleep apnea detection from ECG signal using deep CNN-based structures. EVOLVING SYSTEMS 2022. [DOI: 10.1007/s12530-022-09445-1] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
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JeyaJothi ES, Anitha J, Rani S, Tiwari B. A Comprehensive Review: Computational Models for Obstructive Sleep Apnea Detection in Biomedical Applications. BIOMED RESEARCH INTERNATIONAL 2022; 2022:7242667. [PMID: 35224099 PMCID: PMC8866013 DOI: 10.1155/2022/7242667] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/22/2021] [Accepted: 12/22/2021] [Indexed: 02/06/2023]
Abstract
Obstructive sleep apnea (OSA) is a sleep disorder characterized by periodic episodes of partial or complete upper airway obstruction caused by narrowing or collapse of the pharyngeal airway despite ongoing breathing efforts during sleep. Fall in the blood oxygen saturation and cortical arousals are prompted by this reduction in the airflow which lasts for at least 10 seconds. Impaired labor performance, debilitated quality of life, excessive daytime sleepiness, high snoring, and tiredness even after a whole night's sleep are the primary symptoms of OSA. In due course, the long-standing contributions of OSA culminate in hypertension, arrhythmia, cerebrovascular disease, and heart failure. The traditional diagnostic approach of OSA is the laboratory-based polysomnography (PSG) overnight sleep study, which is a tedious and labor-intensive process that exaggerates the discomfort to the patient. With the advent of computer-aided diagnosis (CAD), automatic detection of OSA has gained increasing interest among researchers in the area of sleep disorders as it influences both diagnostic and therapeutic decisions. The research literature on sleep apnea published during the last decade has been surveyed, focusing on the varied screening approaches accustomed to identifying OSA events and the developmental knowledge offered by multiple contributors from the software perspective. The current study presents an overview of the pathophysiology of OSA, the detection methods, physiological signals related to OSA, the different preprocessing, feature extraction, feature selection, and classification techniques employed for the detection and classification of OSA. Consequently, the research challenges and research gaps in the diagnosis of OSA are identified, critically analyzed, and presented in the best possible light.
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Affiliation(s)
- E. Smily JeyaJothi
- Department of Biomedical Instrumentation Engineering, Avinashilingam Institute for Home Science and Higher Education for Women, Coimbatore 641108, India
| | - J. Anitha
- Department of Electronics and Communication Engineering, Karunya Institute of Technology and Sciences, Coimbatore 641114, India
| | - Shalli Rani
- Chitkara University Institute of Engineering and Technology, Chitkara University, Rajpura Punjab-140401, India
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Simegn GL, Nemomssa HD, Ayalew MP. Machine learning-based automatic sleep apnoea and severity level classification using ECG and SpO2 signals. J Med Eng Technol 2022; 46:148-157. [DOI: 10.1080/03091902.2022.2026503] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/11/2023]
Affiliation(s)
| | - Hundessa Daba Nemomssa
- School of Biomedical Engineering, Jimma Institute of Technology, Jimma University, Jimma, Ethiopia
| | - Mikiyas Petros Ayalew
- School of Biomedical Engineering, Jimma Institute of Technology, Jimma University, Jimma, Ethiopia
- Ethiopian Food and Drug Authority, Addis Ababa, Ethiopia
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Afrakhteh S, Ayatollahi A, Soltani F. Classification of sleep apnea using EMD-based features and PSO-trained neural networks. BIOMED ENG-BIOMED TE 2021; 66:459-472. [PMID: 33930264 DOI: 10.1515/bmt-2021-0025] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2021] [Accepted: 04/12/2021] [Indexed: 11/15/2022]
Abstract
In this study, we propose a method for detecting obstructive sleep apnea (OSA) based on the features extracted from empirical mode decomposition (EMD) and the neural networks trained by particle swarm optimization (PSO) in the classification phase. After extracting the features from the intrinsic mode functions (IMF) of each heart rate variability (HRV) signal of each segment, these features were applied to the input of popular classifiers such as multi-layer perceptron neural networks (MLPNN), Naïve Bayes, linear discriminant analysis (LDA), k-nearest neighborhood (KNN), and support vector machines (SVM) were applied. The results show that the MLPNN learned with back propagation (BP) algorithm has a diagnostic accuracy of less than 90%, and this may be due to being derivative based property of the BP algorithm, which causes trapping in the local minima. For Improving MLPNN's performance, we used the PSO algorithm instead of the BP method in training part. Therefore, the MLPNN's accuracy improved from 89.36 to 97.66% after the application of the PSO algorithm. The proposed method has also reached to 97.78 and 97.96% in sensitivity and specificity, respectively. So, it can be concluded that the proposed method achieves better or comparable results when compared with the previous works in this field.
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Affiliation(s)
- Sajjad Afrakhteh
- Department of Electrical Engineering, Iran University of Science and Technology, Narmak, Tehran, 16846-13114, Iran
| | - Ahmad Ayatollahi
- Department of Electrical Engineering, Iran University of Science and Technology, Narmak, Tehran, 16846-13114, Iran
| | - Fatemeh Soltani
- Department of Electrical Engineering, Iran University of Science and Technology, Narmak, Tehran, 16846-13114, Iran
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Bernardini A, Brunello A, Gigli GL, Montanari A, Saccomanno N. AIOSA: An approach to the automatic identification of obstructive sleep apnea events based on deep learning. Artif Intell Med 2021; 118:102133. [PMID: 34412849 DOI: 10.1016/j.artmed.2021.102133] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2021] [Revised: 06/23/2021] [Accepted: 06/24/2021] [Indexed: 12/31/2022]
Abstract
Obstructive Sleep Apnea Syndrome (OSAS) is the most common sleep-related breathing disorder. It is caused by an increased upper airway resistance during sleep, which determines episodes of partial or complete interruption of airflow. The detection and treatment of OSAS is particularly important in patients who suffered a stroke, because the presence of severe OSAS is associated with higher mortality, worse neurological deficits, worse functional outcome after rehabilitation, and a higher likelihood of uncontrolled hypertension. The gold standard test for diagnosing OSAS is polysomnography (PSG). Unfortunately, performing a PSG in an electrically hostile environment, like a stroke unit, on neurologically impaired patients is a difficult task; moreover, the number of strokes per day vastly outnumbers the availability of polysomnographs and dedicated healthcare professionals. Hence, a simple and automated recognition system to identify OSAS cases among acute stroke patients, relying on routinely recorded vital signs, is highly desirable. The vast majority of the work done so far focuses on data recorded in ideal conditions and highly selected patients, and thus it is hardly exploitable in real-life circumstances, where it would be of actual use. In this paper, we propose a novel convolutional deep learning architecture able to effectively reduce the temporal resolution of raw waveform data, like physiological signals, extracting key features that can be used for further processing. We exploit models based on such an architecture to detect OSAS events in stroke unit recordings obtained from the monitoring of unselected patients. Unlike existing approaches, annotations are performed at one-second granularity, allowing physicians to better interpret the model outcome. Results are considered to be satisfactory by the domain experts. Moreover, through tests run on a widely-used public OSAS dataset, we show that the proposed approach outperforms current state-of-the-art solutions.
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Affiliation(s)
- Andrea Bernardini
- Clinical Neurology Unit, Udine University Hospital, Piazzale Santa Maria della Misericordia, 15, 33100 Udine, Italy.
| | - Andrea Brunello
- Department of Mathematics, Computer Science, and Physics, University of Udine, Via delle Scienze 206, 33100 Udine, Italy.
| | - Gian Luigi Gigli
- Clinical Neurology Unit, Udine University Hospital, Piazzale Santa Maria della Misericordia, 15, 33100 Udine, Italy.
| | - Angelo Montanari
- Department of Mathematics, Computer Science, and Physics, University of Udine, Via delle Scienze 206, 33100 Udine, Italy.
| | - Nicola Saccomanno
- Department of Mathematics, Computer Science, and Physics, University of Udine, Via delle Scienze 206, 33100 Udine, Italy.
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Bricout A, Fontecave-Jallon J, Pépin JL, Guméry PY. Accelerometry-derived respiratory index estimating apnea-hypopnea index for sleep apnea screening. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2021; 207:106209. [PMID: 34118579 DOI: 10.1016/j.cmpb.2021.106209] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/14/2021] [Accepted: 05/24/2021] [Indexed: 06/12/2023]
Abstract
BACKGROUND AND OBJECTIVE Sleep Apnea Syndrome (SAS) is a multimorbid chronic disease with individual and societal deleterious consequences. Polysomnography (PSG) is the multi-parametric reference diagnostic tool that allows a manual quantification of the apnea-hypopnea index (AHI) to assess SAS severity. The burden of SAS is affecting nearly one billion people worldwide explaining that SAS remains largely under-diagnosed and undertreated. The development of an easy to use and automatic solution for early detection and screening of SAS is highly desirable. METHODS We proposed an Accelerometry-Derived Respiratory index (ADR) solution based on a dual accelerometry system for airflow estimation included in a machine learning process. It calculated the AHI thanks to a RUSBoosted Tree model and used physiological and explanatory specifically developed features. The performances of this method were evaluated against a configuration using gold-standard PSG signals on a database of 28 subjects. RESULTS The AHI estimation accuracy, specificity and sensitivity of the ADR index were 89%, 100% and 80% respectively. The added value of the specifically developed features was also demonstrated. CONCLUSION Overnight physiological monitoring with the proposed ADR solution using a machine learning approach provided a clinically relevant estimate of AHI for SAS screening. The physiological component of the solution has a real interest for improving performance and facilitating physician's adhesion to an automatic AHI estimation.
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Affiliation(s)
- Aurélien Bricout
- Grenoble Alpes University, CNRS, CHU Grenoble Alpes, Grenoble INP, TIMC, Grenoble, France.
| | - Julie Fontecave-Jallon
- Grenoble Alpes University, CNRS, CHU Grenoble Alpes, Grenoble INP, TIMC, Grenoble, France
| | - Jean-Louis Pépin
- HP2 Laboratory, INSERM U1042, Grenoble Alpes University, Grenoble, France
| | - Pierre-Yves Guméry
- Grenoble Alpes University, CNRS, CHU Grenoble Alpes, Grenoble INP, TIMC, Grenoble, France.
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Automated Sleep apnea detection using optimal duration-frequency concentrated wavelet-based features of pulse oximetry signals. APPL INTELL 2021. [DOI: 10.1007/s10489-021-02422-2] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/15/2023]
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12
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Elgart M, Redline S, Sofer T. Machine and Deep Learning in Molecular and Genetic Aspects of Sleep Research. Neurotherapeutics 2021; 18:228-243. [PMID: 33829409 PMCID: PMC8116376 DOI: 10.1007/s13311-021-01014-9] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 01/18/2021] [Indexed: 12/11/2022] Open
Abstract
Epidemiological sleep research strives to identify the interactions and causal mechanisms by which sleep affects human health, and to design intervention strategies for improving sleep throughout the lifespan. These goals can be advanced by further focusing on the environmental and genetic etiology of sleep disorders, and by development of risk stratification algorithms, to identify people who are at risk or are affected by, sleep disorders. These studies rely on comprehensive sleep-related data which often contains complex multi-dimensional physiological and molecular measurements across multiple timepoints. Thus, sleep research is well-suited for the application of computational approaches that can handle high-dimensional data. Here, we survey recent advances in machine and deep learning together with the availability of large human cohort studies with sleep data that can jointly drive the next breakthroughs in the sleep-research field. We describe sleep-related data types and datasets, and present some of the tasks in the field that can be targets for algorithmic approaches, as well as the challenges and opportunities in pursuing them.
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Affiliation(s)
- Michael Elgart
- Division of Sleep and Circadian Disorders, Brigham and Women’s Hospital, Boston, MA USA
- Department of Medicine, Harvard Medical School, Boston, MA USA
| | - Susan Redline
- Division of Sleep and Circadian Disorders, Brigham and Women’s Hospital, Boston, MA USA
- Department of Medicine, Harvard Medical School, Boston, MA USA
| | - Tamar Sofer
- Division of Sleep and Circadian Disorders, Brigham and Women’s Hospital, Boston, MA USA
- Department of Medicine, Harvard Medical School, Boston, MA USA
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA USA
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13
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Mostafa SS, Baptista D, Ravelo-García AG, Juliá-Serdá G, Morgado-Dias F. Greedy based convolutional neural network optimization for detecting apnea. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2020; 197:105640. [PMID: 32673899 DOI: 10.1016/j.cmpb.2020.105640] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/20/2020] [Accepted: 06/27/2020] [Indexed: 06/11/2023]
Abstract
BACKGROUND AND OBJECTIVE Sleep apnea is a common sleep disorder, usually diagnosed using an expensive, highly specialized, and inconvenient test called polysomnography. A single SpO2 sensor based on an automated classification system can be developed to simplify the apnea detection. The main objective of this work is to develop a classifier based on a convolution neural network with the capability of detecting apnea events from one dimensional SpO2 signal. However, to find an optimum convolution neural network structure is a daunting task is usually done by a trial-and-error method. To solve this problem, a method is proposed to save time and simplify the process of searching for an optimum convolution neural network structure. METHODS Greedy based optimization is proposed to search for an optimized convolution neural network structure. Three different variants of greedy based optimization are proposed: the topology transfer, the weighted-topology transfer with rough estimation, and the weighted-topology transfer with fine tuning. The subject independent and the cross-database test are performed for the analysis. RESULTS Considering the balance between the execution time and the performance, the weighted-topology transfer with rough estimation is the best. An accuracy of 88.49% for the HuGCDN2008 database and 95.14% for the Apnea-ECG database are obtained for apnea events detection per minute. Regarding the apnea patient detection, also referred to as global classification, an accuracy of 95.71% is achieved for the HuGCDN2008 database, and 100% is achieved for the AED database without removing any subjects from both databases. CONCLUSIONS The proposed one-dimensional convolution neural network performs better in a similar situation than those presented in the literature. The greedy based methods, mainly the weighted-topology transfer with rough estimation, is an alternative method to extensive trial and error method.
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Affiliation(s)
- Sheikh Shanawaz Mostafa
- ITI/Larsys/Madeira Interactive Technologies Institute, Portugal; Universidade de Lisboa, Instituto Superior Técnico, Portugal.
| | - Darío Baptista
- ITI/Larsys/Madeira Interactive Technologies Institute, Portugal; Universidade de Lisboa, Instituto Superior Técnico, Portugal.
| | - Antonio G Ravelo-García
- Universidad de Las Palmas de Gran Canaria, Institute for Technological Development and Innovation in Communications, Spain; ITI/Larsys/Madeira Interactive Technologies Institute, Portugal.
| | - Gabriel Juliá-Serdá
- Pulmonary Medicine Department, Hospital Universitario de Gran Canaria Dr. Negrın, Las Palmas de Gran Canaria 35010, Spain.
| | - Fernando Morgado-Dias
- ITI/Larsys/Madeira Interactive Technologies Institute, Portugal; Universidade da Madeira, Portugal.
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15
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Orjuela-Cañón AD, Cerquera A, Freund JA, Juliá-Serdá G, Ravelo-García AG. Sleep apnea: Tracking effects of a first session of CPAP therapy by means of Granger causality. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2020; 187:105235. [PMID: 31812116 DOI: 10.1016/j.cmpb.2019.105235] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/27/2019] [Revised: 11/04/2019] [Accepted: 11/18/2019] [Indexed: 06/10/2023]
Abstract
Connectivity between physiological networks is an issue of particular importance for understanding the complex interaction brain-heart. In the present study, this interaction was analyzed in polysomnography recordings of 28 patients diagnosed with obstructive sleep apnea (OSA) and compared with a group of 10 control subjects. Electroencephalography and electrocardiography signals from these polysomnography time series were characterized employing Granger causality computation to measure the directed connectivity among five brain waves and three spectral subbands of heart rate variability. Polysomnography data from OSA patients were recorded before and during a first session of continuous positive air pressure (CPAP) therapy in a split-night study. Results showed that CPAP therapy allowed the recovery of inner brain connectivities, mainly in subsystems involving the theta wave. In addition, differences between control and OSA patients were established in connections that involve lower frequency ranges of heart rate variability. This information can be potentially useful in the initial diagnosis of OSA, and determine the role of cardiac activity in sleep dynamics based on the use of three subbands of heart rate variability.
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Affiliation(s)
- Alvaro D Orjuela-Cañón
- Facultad de Ingeniería Mecánica, Electrónica y Biomédica, Universidad Antonio Nariño, Bogotá D.C., Colombia; Biomedical Engineering Program, School of Medicine and Health Sciences, Universidad del Rosario, Bogotá D.C., Colombia.
| | - Alexander Cerquera
- Brain Dynamics Program, Wilder Center for Epilepsy Research. Department of Neurology-College of Medicine. University of Florida, Gainesville, FL, United States.
| | - Jan A Freund
- Carl von Ossietzky Universität Oldenburg. ICBM & Research Center Neurosensory Science. D-26111, Oldenburg, Germany.
| | - Gabriel Juliá-Serdá
- Pulmonary Medicine Department, Hospital Universitario de Gran Canaria Dr. Negrín, Las Palmas de Gran Canaria 35010, Spain.
| | - Antonio G Ravelo-García
- Institute for Technological Development and Innovation in Communications, Universidad de Las Palmas de Gran Canaria, Las Palmas de Gran Canaria 35017, Spain.
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Mendonça F, Mostafa SS, Morgado-Dias F, Ravelo-García AG. An Oximetry Based Wireless Device for Sleep Apnea Detection. SENSORS 2020; 20:s20030888. [PMID: 32046102 PMCID: PMC7039040 DOI: 10.3390/s20030888] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/15/2020] [Revised: 01/29/2020] [Accepted: 02/03/2020] [Indexed: 11/16/2022]
Abstract
Sleep related disorders can severely disturb the quality of sleep. Among these disorders, obstructive sleep apnea (OSA) is highly prevalent and commonly undiagnosed. Polysomnography is considered to be the gold standard exam for OSA diagnosis. Even though this multi-parametric test provides highly accurate results, it is time consuming, labor-intensive, and expensive. A non-invasive and easy to self-assemble home monitoring device was developed to address these issues. The device can perform the OSA diagnosis at the patient’s home and a specialized technician is not required to supervise the process. An automatic scoring algorithm was developed to examine the blood oxygen saturation signal for a minute-by-minute OSA assessment. It was performed by analyzing statistical and frequency-based features that were fed to a classifier. Afterward, the ratio of the number of minutes classified as OSA to the time in bed in minutes was compared with a threshold for the global (subject-based) OSA diagnosis. The average accuracy, sensitivity, specificity, and area under the receiver operating characteristic curve for the minute-by-minute assessment were, respectively, 88%, 80%, 91%, and 0.86. The subject-based accuracy was 95%. The performance is in the same range as the best state of the art methods for the models based only on the blood oxygen saturation analysis. Therefore, the developed model has the potential to be employed in clinical analysis.
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Affiliation(s)
- Fábio Mendonça
- Instituto Superior Técnico, University of Lisbon, 1049-001 Lisbon, Portugal;
- ITI/Larsys/Madeira Interactive Technologies Institute, 9020-105 Funchal, Portugal;
- Correspondence: (F.M.); (F.M.-D.); Tel.: +351-291-721-006 (F.M.)
| | - Sheikh Shanawaz Mostafa
- Instituto Superior Técnico, University of Lisbon, 1049-001 Lisbon, Portugal;
- ITI/Larsys/Madeira Interactive Technologies Institute, 9020-105 Funchal, Portugal;
| | - Fernando Morgado-Dias
- ITI/Larsys/Madeira Interactive Technologies Institute, 9020-105 Funchal, Portugal;
- Faculty of Exact Sciences and Engineering, University of Madeira, 9000-082 Funchal, Portugal
- Correspondence: (F.M.); (F.M.-D.); Tel.: +351-291-721-006 (F.M.)
| | - Antonio G. Ravelo-García
- ITI/Larsys/Madeira Interactive Technologies Institute, 9020-105 Funchal, Portugal;
- Institute for Technological Development and Innovation in Communications, Universidad de Las Palmas de Gran Canaria, 35001 Las Palmas de Gran Canaria, Spain
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Byun S, Kim AY, Jang EH, Kim S, Choi KW, Yu HY, Jeon HJ. Entropy analysis of heart rate variability and its application to recognize major depressive disorder: A pilot study. Technol Health Care 2020; 27:407-424. [PMID: 31045557 PMCID: PMC6597986 DOI: 10.3233/thc-199037] [Citation(s) in RCA: 24] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/22/2022]
Abstract
BACKGROUND The current method to evaluate major depressive disorder (MDD) relies on subjective clinical interviews and self-questionnaires. OBJECTIVE Autonomic imbalance in MDD patients is characterized using entropy measures of heart rate variability (HRV). A machine learning approach for screening depression based on the entropy is demonstrated. METHODS The participants experience five experimental phases: baseline (BASE), stress task (MAT), stress task recovery (REC1), relaxation task (RLX), and relaxation task recovery (REC2). The four entropy indices, approximate entropy, sample entropy, fuzzy entropy, and Shannon entropy, are extracted for each phase, and a total of 20 features are used. A support vector machine classifier and recursive feature elimination are employed for classification. RESULTS The entropy features are lower in the MDD group; however, the disease does not have a significant effect. Experimental tasks significantly affect the features. The entropy did not recover during REC1. The differences in the entropy features between the two groups increased after MAT and showed the largest gap in REC2. We achieved 70% accuracy, 64% sensitivity, and 76% specificity with three optimal features during RLX and REC2. CONCLUSION Monitoring of HRV complexity changes when a subject experiences autonomic arousal and recovery can potentially facilitate objective depression recognition.
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Affiliation(s)
- Sangwon Byun
- Department of Electronics Engineering, Incheon National University, Incheon 22012, Korea
| | - Ah Young Kim
- Bio-Medical IT Convergence Research Division, Electronics and Telecommunications Research Institute, Daejeon 34129, Korea
| | - Eun Hye Jang
- Bio-Medical IT Convergence Research Division, Electronics and Telecommunications Research Institute, Daejeon 34129, Korea
| | - Seunghwan Kim
- Bio-Medical IT Convergence Research Division, Electronics and Telecommunications Research Institute, Daejeon 34129, Korea
| | - Kwan Woo Choi
- Department of Psychiatry, Depression Center, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul 06351, Korea.,Department of Psychiatry, Korea University Anam Hospital, Korea University College of Medicine, Seoul 02841, Korea
| | - Han Young Yu
- Bio-Medical IT Convergence Research Division, Electronics and Telecommunications Research Institute, Daejeon 34129, Korea
| | - Hong Jin Jeon
- Department of Psychiatry, Depression Center, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul 06351, Korea
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Mostafa SS, Mendonça F, G. Ravelo-García A, Morgado-Dias F. A Systematic Review of Detecting Sleep Apnea Using Deep Learning. SENSORS (BASEL, SWITZERLAND) 2019; 19:E4934. [PMID: 31726771 PMCID: PMC6891618 DOI: 10.3390/s19224934] [Citation(s) in RCA: 50] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/13/2019] [Revised: 10/29/2019] [Accepted: 11/04/2019] [Indexed: 02/02/2023]
Abstract
Sleep apnea is a sleep related disorder that significantly affects the population. Polysomnography, the gold standard, is expensive, inaccessible, uncomfortable and an expert technician is needed to score. Numerous researchers have proposed and implemented automatic scoring processes to address these issues, based on fewer sensors and automatic classification algorithms. Deep learning is gaining higher interest due to database availability, newly developed techniques, the possibility of producing machine created features and higher computing power that allows the algorithms to achieve better performance than the shallow classifiers. Therefore, the sleep apnea research has currently gained significant interest in deep learning. 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. The employed signals, sensors, databases and implementation challenges were also considered. A systematic search was conducted on five indexing services from 2008-2018. A total of 255 papers were found and 21 were selected by considering the inclusion and exclusion criteria, using the preferred reporting items for systematic reviews and meta-analyses (PRISMA) approach.
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Affiliation(s)
- Sheikh Shanawaz Mostafa
- Instituto Superior Técnico, Universidade de Lisboa, 1049-001 Lisboa, Portugal;
- Madeira Interactive Technologies Institute, 9020-105 Funchal, Portugal
| | - Fábio Mendonça
- Instituto Superior Técnico, Universidade de Lisboa, 1049-001 Lisboa, Portugal;
- Madeira Interactive Technologies Institute, 9020-105 Funchal, Portugal
| | - Antonio G. Ravelo-García
- Institute for Technological Development and Innovation in Communications, Universidad de Las Palmas de Gran Canaria, 35001 Las Palmas, Spain;
| | - Fernando Morgado-Dias
- Faculdade de Ciências Exatas e da Engenharia, Universidade da Madeira, 9000-082 Funchal, Portugal
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Yang W, Fan J, Wang X, Liao Q. Sleep Apnea and Hypopnea Events Detection Based on Airflow Signals Using LSTM Network. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2019; 2019:2576-2579. [PMID: 31946423 DOI: 10.1109/embc.2019.8857558] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/10/2023]
Abstract
Sleep Apnea-Hypopnea Syndrome (SAHS) is a sleep-related breathing disorder which involves the reduction in breathing airway when patiens sleep. However, a large proportion of patients are usually undiagnosed and untreated which may lead to the risk of life. In this paper, we propose an automatic SAHS event detection method based on Long Short-Term Memory (LSTM) network via nasal airway pressure and temperature signals from clinical polysomnography (PSG) dataset. Focusing on time location of the events, we firstly segment the two channels of signals into a series of sequences by feature extraction. Secondly, a LSTM network is established and these sequences are subsequently fed into this LSTM network for SAHS event classification. The experimental results on both our clinical PSG dataset and public MIT-BIH PSG database show that our method is promising in terms of recall.
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Mendonca F, Mostafa SS, Ravelo-Garcia AG, Morgado-Dias F, Penzel T. A Review of Obstructive Sleep Apnea Detection Approaches. IEEE J Biomed Health Inform 2019; 23:825-837. [DOI: 10.1109/jbhi.2018.2823265] [Citation(s) in RCA: 75] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
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Álvarez D, Crespo A, Vaquerizo-Villar F, Gutierrez-Tobal GC, Cerezo-Hernández A, Barroso-García V, Ansermino JM, Dumont GA, Hornero R, Del Campo F, Garde A. Symbolic dynamics to enhance diagnostic ability of portable oximetry from the phone oximeter in the detection of paediatric sleep apnoea. Physiol Meas 2018; 39:104002. [PMID: 30230476 DOI: 10.1088/1361-6579/aae2a8] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/31/2022]
Abstract
OBJECTIVE This study is aimed at assessing symbolic dynamics as a reliable technique to characterise complex fluctuations of portable oximetry in the context of automated detection of childhood obstructive sleep apnoea-hypopnoea syndrome (OSAHS). APPROACH Nocturnal oximetry signals from 142 children with suspected OSAHS were acquired using the Phone Oximeter: a portable device that integrates a pulse oximeter with a smartphone. An apnoea-hypopnoea index (AHI) ≥5 events/h from simultaneous in-lab polysomnography was used to confirm moderate-to-severe childhood OSAHS. Symbolic dynamics was used to parameterise non-linear changes in the overnight oximetry profile. Conventional indices, anthropometric measures, and time-domain linear statistics were also considered. Forward stepwise logistic regression was used to obtain an optimum feature subset. Logistic regression (LR) was used to identify children with moderate-to-severe OSAHS. MAIN RESULTS The histogram of 3-symbol words from symbolic dynamics showed significant differences (p <0.01) between children with AHI <5 events/h and moderate-to-severe patients (AHI ≥5 events/h). Words representing increasing oximetry values after apnoeic events (re-saturations) showed relevant diagnostic information. Regarding the performance of individual characterization approaches, the LR model composed of features from symbolic dynamics alone reached a maximum performance of 78.4% accuracy (65.2% sensitivity; 86.8% specificity) and 0.83 area under the ROC curve (AUC). The classification performance improved combining all features. The optimum model from feature selection achieved 83.3% accuracy (73.5% sensitivity; 89.5% specificity) and 0.89 AUC, significantly (p-value <0.01) outperforming the other models. SIGNIFICANCE Symbolic dynamics provides complementary information to conventional oximetry analysis enabling reliable detection of moderate-to-severe paediatric OSAHS from portable oximetry.
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Affiliation(s)
- Daniel Álvarez
- Pneumology Service, Rio Hortega University Hospital, Valladolid, Valladolid, SPAIN
| | - Andrea Crespo
- Pneumology Service, Rio Hortega University Hospital, Valladolid, Valladolid, SPAIN
| | - Fernado Vaquerizo-Villar
- Biomedical Engineering Group, ETSI Telecomunicación, Universidad de Valladolid, Valladolid, Castilla y León, SPAIN
| | - Gonzalo Cesar Gutierrez-Tobal
- Biomedical Engineering Group ETS Ingenieros de Telecommunicacion, Universidad de Valladolid, Camino del Cementerio sn, 47011 Valladoid, Valladolid, SPAIN
| | - Ana Cerezo-Hernández
- Pneumology Service, Rio Hortega University Hospital, Valladolid, Valladolid, SPAIN
| | - Verónica Barroso-García
- Biomedical Engineering Group, ETSI Telecomunicación, Universidad de Valladolid, Valladolid, Castilla y León, SPAIN
| | | | - Guy A Dumont
- University of British Columbia, Vancouver, British Columbia, CANADA
| | - Roberto Hornero
- Biomedical Engineering Group, ETSI Telecomunicación, Universidad de Valladolid, Valladolid, Castilla y León, SPAIN
| | - Felix Del Campo
- Pneumology Service, Rio Hortega University Hospital, Valladolid, Valladolid, SPAIN
| | - Ainara Garde
- Universiteit Twente, Enschede, 7500 AE, NETHERLANDS
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Improving the understanding of sleep apnea characterization using Recurrence Quantification Analysis by defining overall acceptable values for the dimensionality of the system, the delay, and the distance threshold. PLoS One 2018; 13:e0194462. [PMID: 29621264 PMCID: PMC5886413 DOI: 10.1371/journal.pone.0194462] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2017] [Accepted: 03/02/2018] [Indexed: 11/25/2022] Open
Abstract
Our contribution focuses on the characterization of sleep apnea from a cardiac rate point of view, using Recurrence Quantification Analysis (RQA), based on a Heart Rate Variability (HRV) feature selection process. Three parameters are crucial in RQA: those related to the embedding process (dimension and delay) and the threshold distance. There are no overall accepted parameters for the study of HRV using RQA in sleep apnea. We focus on finding an overall acceptable combination, sweeping a range of values for each of them simultaneously. Together with the commonly used RQA measures, we include features related to recurrence times, and features originating in the complex network theory. To the best of our knowledge, no author has used them all for sleep apnea previously. The best performing feature subset is entered into a Linear Discriminant classifier. The best results in the “Apnea-ECG Physionet database” and the “HuGCDN2014 database” are, according to the area under the receiver operating characteristic curve, 0.93 (Accuracy: 86.33%) and 0.86 (Accuracy: 84.18%), respectively. Our system outperforms, using a relatively small set of features, previously existing studies in the context of sleep apnea. We conclude that working with dimensions around 7–8 and delays about 4–5, and using for the threshold distance the Fixed Amount of Nearest Neighbours (FAN) method with 5% of neighbours, yield the best results. Therefore, we would recommend these reference values for future work when applying RQA to the analysis of HRV in sleep apnea. We also conclude that, together with the commonly used vertical and diagonal RQA measures, there are newly used features that contribute valuable information for apnea minutes discrimination. Therefore, they are especially interesting for characterization purposes. Using two different databases supports that the conclusions reached are potentially generalizable, and are not limited by database variability.
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Mostafa SS, Morgado-Dias F, Ravelo-García AG. Comparison of SFS and mRMR for oximetry feature selection in obstructive sleep apnea detection. Neural Comput Appl 2018. [DOI: 10.1007/s00521-018-3455-8] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
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Heart rate variability feature selection in the presence of sleep apnea: An expert system for the characterization and detection of the disorder. Comput Biol Med 2017; 91:47-58. [DOI: 10.1016/j.compbiomed.2017.10.004] [Citation(s) in RCA: 35] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2017] [Revised: 10/06/2017] [Accepted: 10/06/2017] [Indexed: 11/18/2022]
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