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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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Prasanna Venkatesh N, Pradeep Kumar R, Chakravarthy Neelapu B, Pal K, Sivaraman J. CatBoost-based improved detection of P-wave changes in sinus rhythm and tachycardia conditions: a lead selection study. Phys Eng Sci Med 2023; 46:925-944. [PMID: 37160538 DOI: 10.1007/s13246-023-01274-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2023] [Accepted: 05/03/2023] [Indexed: 05/11/2023]
Abstract
Examining P-wave morphological changes in Electrocardiogram (ECG) is essential for characterizing atrial arrhythmias. However, standard 12-lead ECGsuffer from diagnostic redundancy due to low signal-to-noise ratio of P-waves. To address this issue, various optimal leads have been proposed for improved atrial activity recording, but the right selection among these leads is crucial for enhancing diagnostic efficacy. This study proposes an automated lead selection technique using the CatBoost machine learning (ML) model to improve the detection of P-wave changes among optimal bipolar leads under different heart rates. ECGs were obtained from healthy participants with a mean age of 25 ± 3.81 years (34% women), including 114 in sinus rhythm (SR) and 38 in sinus tachycardia (ST). The recordings were made using a newly designed atrial lead system (ALS), standard limb lead (SLL), modified limb lead (MLL), modified Lewis lead (LLM) and P-lead. P-wave features and Atrioventricular (AV) ratio were extracted for statistical analysis and ML classification. The optimum ML model was chosen to identify the best-performing optimal lead, which was selected based on the SLL metrics among different ML classifiers. CatBoost was found to outperform the other ML models in SLL-II with the highest accuracy and sensitivity of 0.82 and 0.90, respectively. The CatBoost model, amid other optimal leads, gave the best results for AL-I and AL-II (0.86 and 0.83 in accuracy and 0.91 and 0.93 in sensitivity). The developed CatBoost model selected AL-I and AL-II as the top two best-performing optimal leads for the enhanced acquisition of P-wave changes, which may be useful for diagnosing atrial arrhythmias.
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Affiliation(s)
- N Prasanna Venkatesh
- Department of Biotechnology and Medical Engineering, National Institute of Technology Rourkela, Rourkela, Odisha, 769008, India
| | - R Pradeep Kumar
- Department of Cardiac Sciences, Jaiprakash Hospital and Research Centre, Rourkela, Odisha, 769004, India
| | - Bala Chakravarthy Neelapu
- Department of Biotechnology and Medical Engineering, National Institute of Technology Rourkela, Rourkela, Odisha, 769008, India
| | - Kunal Pal
- Department of Biotechnology and Medical Engineering, National Institute of Technology Rourkela, Rourkela, Odisha, 769008, India
| | - J Sivaraman
- Department of Biotechnology and Medical Engineering, National Institute of Technology Rourkela, Rourkela, Odisha, 769008, India.
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Li X, Leung FHF, Su S, Ling SH. Sleep Apnea Detection Using Multi-Error-Reduction Classification System with Multiple Bio-Signals. SENSORS (BASEL, SWITZERLAND) 2022; 22:5560. [PMID: 35898064 PMCID: PMC9371161 DOI: 10.3390/s22155560] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 06/16/2022] [Revised: 07/12/2022] [Accepted: 07/20/2022] [Indexed: 06/15/2023]
Abstract
INTRODUCTION Obstructive sleep apnea (OSA) can cause serious health problems such as hypertension or cardiovascular disease. The manual detection of apnea is a time-consuming task, and automatic diagnosis is much more desirable. The contribution of this work is to detect OSA using a multi-error-reduction (MER) classification system with multi-domain features from bio-signals. METHODS Time-domain, frequency-domain, and non-linear analysis features are extracted from oxygen saturation (SaO2), ECG, airflow, thoracic, and abdominal signals. To analyse the significance of each feature, we design a two-stage feature selection. Stage 1 is the statistical analysis stage, and Stage 2 is the final feature subset selection stage using machine learning methods. In Stage 1, two statistical analyses (the one-way analysis of variance (ANOVA) and the rank-sum test) provide a list of the significance level of each kind of feature. Then, in Stage 2, the support vector machine (SVM) algorithm is used to select a final feature subset based on the significance list. Next, an MER classification system is constructed, which applies a stacking with a structure that consists of base learners and an artificial neural network (ANN) meta-learner. RESULTS The Sleep Heart Health Study (SHHS) database is used to provide bio-signals. A total of 66 features are extracted. In the experiment that involves a duration parameter, 19 features are selected as the final feature subset because they provide a better and more stable performance. The SVM model shows good performance (accuracy = 81.68%, sensitivity = 97.05%, and specificity = 66.54%). It is also found that classifiers have poor performance when they predict normal events in less than 60 s. In the next experiment stage, the time-window segmentation method with a length of 60s is used. After the above two-stage feature selection procedure, 48 features are selected as the final feature subset that give good performance (accuracy = 90.80%, sensitivity = 93.95%, and specificity = 83.82%). To conduct the classification, Gradient Boosting, CatBoost, Light GBM, and XGBoost are used as base learners, and the ANN is used as the meta-learner. The performance of this MER classification system has the accuracy of 94.66%, the sensitivity of 96.37%, and the specificity of 90.83%.
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Affiliation(s)
- Xilin Li
- School of Biomedical Engineering, Faculty of Engineering and Information Technology (FEIT), University of Technology Sydney (UTS), Ultimo, NSW 2007, Australia; (X.L.); (S.S.)
| | - Frank H. F. Leung
- Department of Electronic and Information Engineering, The Hong Kong Polytechnic University, Hung Hum, Hong Kong, China;
| | - Steven Su
- School of Biomedical Engineering, Faculty of Engineering and Information Technology (FEIT), University of Technology Sydney (UTS), Ultimo, NSW 2007, Australia; (X.L.); (S.S.)
| | - Sai Ho Ling
- School of Electrical and Data Engineering, Faculty of Engineering and Information Technology (FEIT), University of Technology Sydney (UTS), Ultimo, NSW 2007, Australia
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Balci M, Tasdemir S, Ozmen G, Golcuk A. Machine Learning-Based Detection of Sleep-Disordered Breathing Type Using Time and Time-Frequency Features. Biomed Signal Process Control 2022. [DOI: 10.1016/j.bspc.2021.103402] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/02/2022]
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Gutiérrez-Tobal GC, Álvarez D, Vaquerizo-Villar F, Barroso-García V, Gómez-Pilar J, Del Campo F, Hornero R. Conventional Machine Learning Methods Applied to the Automatic Diagnosis of Sleep Apnea. ADVANCES IN EXPERIMENTAL MEDICINE AND BIOLOGY 2022; 1384:131-146. [PMID: 36217082 DOI: 10.1007/978-3-031-06413-5_8] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Abstract
The overnight polysomnography shows a range of drawbacks to diagnose obstructive sleep apnea (OSA) that have led to the search for artificial intelligence-based alternatives. Many classic machine learning methods have been already evaluated for this purpose. In this chapter, we show the main approaches found in the scientific literature along with the most used data to develop the models, useful and large easily available databases, and suitable methods to assess performances. In addition, a range of results from selected studies are presented as examples of these methods. Very high diagnostic performances are reported in these results regardless of the approaches taken. This leads us to conclude that conventional machine learning methods are useful techniques to develop new OSA diagnosis simplification proposals and to act as benchmark for other more recent methods such as deep learning.
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Affiliation(s)
- Gonzalo C Gutiérrez-Tobal
- Centro de Investigación Biomédica en Red, Bioingeniería, Biomateriales, Nanomedicina, Madrid, Spain.
- Biomedical Engineering Group, University of Valladolid, Valladolid, Spain.
| | - Daniel Álvarez
- Centro de Investigación Biomédica en Red, Bioingeniería, Biomateriales, Nanomedicina, Madrid, Spain
- Biomedical Engineering Group, University of Valladolid, Valladolid, Spain
| | - Fernando Vaquerizo-Villar
- Centro de Investigación Biomédica en Red, Bioingeniería, Biomateriales, Nanomedicina, Madrid, Spain
- Biomedical Engineering Group, University of Valladolid, Valladolid, Spain
| | - Verónica Barroso-García
- Centro de Investigación Biomédica en Red, Bioingeniería, Biomateriales, Nanomedicina, Madrid, Spain
- Biomedical Engineering Group, University of Valladolid, Valladolid, Spain
| | - Javier Gómez-Pilar
- Centro de Investigación Biomédica en Red, Bioingeniería, Biomateriales, Nanomedicina, Madrid, Spain
- Biomedical Engineering Group, University of Valladolid, Valladolid, Spain
| | - Félix Del Campo
- Centro de Investigación Biomédica en Red, Bioingeniería, Biomateriales, Nanomedicina, Madrid, Spain
- Biomedical Engineering Group, University of Valladolid, Valladolid, Spain
- Sleep Unit, Pneumology Service, Hospital Universitario Rio Hortega, Valladolid, Spain
| | - Roberto Hornero
- Centro de Investigación Biomédica en Red, Bioingeniería, Biomateriales, Nanomedicina, Madrid, Spain
- Biomedical Engineering Group, University of Valladolid, Valladolid, Spain
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Gutiérrez-Tobal GC, Álvarez D, Vaquerizo-Villar F, Crespo A, Kheirandish-Gozal L, Gozal D, del Campo F, Hornero R. Ensemble-learning regression to estimate sleep apnea severity using at-home oximetry in adults. Appl Soft Comput 2021. [DOI: 10.1016/j.asoc.2021.107827] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
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Cimr D, Studnicka F, Fujita H, Cimler R, Slegr J. Application of mechanical trigger for unobtrusive detection of respiratory disorders from body recoil micro-movements. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2021; 207:106149. [PMID: 34015736 DOI: 10.1016/j.cmpb.2021.106149] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/04/2021] [Accepted: 04/29/2021] [Indexed: 06/12/2023]
Abstract
Background and Objectives Automatic detection of breathing disorders plays an important role in the early signalization of respiratory diseases. Measuring methods can be based on electrocardiogram (ECG), sound, oximetry, or respiratory analysis. However, these approaches require devices placed on the human body or they are prone to disturbance by environmental influences. To solve these problems, we proposed a heart contraction mechanical trigger for unobtrusive detection of respiratory disorders from the mechanical measurement of cardiac contractions. We designed a novel method to calculate this mechanical trigger purely from measured mechanical signals without the use of ECG. Methods The approach is a built-on calculation of the so-called euclidean arc length from the signals. In comparison to previous researches, this system does not require any equipment attached to a person. This is achieved by locating the tensometers on the bed. Data from sensors are fused by the Cartan curvatures method to beat-to-beat vector input for the Convolutional neural network (CNN) classifier. Results In sum, 2281 disordered and 5130 normal breathing samples was collected for analysis. The experiments with use of 10-fold cross validation show that accuracy, sensitivity, and specificity reach values of 96.37%, 92.46%, and 98.11% respectively. Conclusions By the approach for detection, the system offers a novel way for a completely unobtrusive diagnosis of breathing-related health problems. The proposed solution can effectively be deployed in all clinical or home environments.
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Affiliation(s)
- Dalibor Cimr
- Faculty of Science, University of Hradec Kralove, Rokitanskeho 62, Hradec Kralove 50003, Czech Republic
| | - Filip Studnicka
- Faculty of Science, University of Hradec Kralove, Rokitanskeho 62, Hradec Kralove 50003, Czech Republic
| | - Hamido Fujita
- Faculty of Information Technology, Ho Chi Minh City University of Technology (HUTECH), Ho Chi Minh City, Vietnam; DaSCI Andalusian Institute of Data Science and Computational Intelligence, University of Granada, Granada, Spain; Regional Research Center, Iwate Prefectural University, Iwate, Japan.
| | - Richard Cimler
- Faculty of Science, University of Hradec Kralove, Rokitanskeho 62, Hradec Kralove 50003, Czech Republic
| | - Jan Slegr
- Faculty of Science, University of Hradec Kralove, Rokitanskeho 62, Hradec Kralove 50003, Czech Republic
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Casal R, Di Persia LE, Schlotthauer G. Classifying sleep–wake stages through recurrent neural networks using pulse oximetry signals. Biomed Signal Process Control 2021. [DOI: 10.1016/j.bspc.2020.102195] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/23/2022]
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Rolon RE, Di Persia LE, Spies RD, Rufiner HL. A multi-class structured dictionary learning method using discriminant atom selection. Pattern Anal Appl 2020. [DOI: 10.1007/s10044-020-00939-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/23/2022]
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Detection of Abnormal Respiratory Events with Single Channel ECG and Hybrid Machine Learning Model in Patients with Obstructive Sleep Apnea. Ing Rech Biomed 2020. [DOI: 10.1016/j.irbm.2020.05.006] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
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Rolon R, Gareis I, Larrateguy L, Di Persia L, Spies R, Rufiner H. Automatic scoring of apnea and hypopnea events using blood oxygen saturation signals. Biomed Signal Process Control 2020. [DOI: 10.1016/j.bspc.2020.102062] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/31/2022]
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Application of automatic detection based on overnight airflow and blood oxygen in patients with sleep disordered breathing. Eur Arch Otorhinolaryngol 2020; 278:873-881. [PMID: 32409858 DOI: 10.1007/s00405-020-06008-5] [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: 02/24/2020] [Accepted: 04/24/2020] [Indexed: 10/24/2022]
Abstract
PURPOSE To explore the feasibility of automatic detection based on air flow and blood oxygen in patients with sleep disordered breathing. METHODS This study proposes a new automated detection method for sleep disordered breathing based on overnight airflow and blood oxygen saturation (SaO2). In this regard, local range (LR) of the airflow was adopted to detect apnea events and the SaO2 sudden drops were used to help determine hypopnea events. Pearson correlation index was used to evaluate the relationship between the two automated methods (this study vs. Remlogic software) and the manual reports. Error and mean absolute error (MAE) were used to assess the two automated methods. RESULTS For all patients, the apnea-hypopnea index (AHI), apnea index (AI) and hypopnea index (HI) for our automated scoring and manual reports were highly correlated (the Pearson correlation index were 0.996, 0.995 and 0.928, respectively, P < 0.001). However, HI for Remlogic automated scoring and clinical manual reports was poorly correlated (r = 0.316, P < 0.001). Compared with the manual reports, mean absolute error of AHI, AI and HI between the two automated methods (this study vs. Remlogic software) were statistically significant (P < 0.0001). Furthermore, among the three subgroups (group 1, AHI < 15/h, group 2, 15/h ≤ AHI < 30/h and group 3, AHI ≥ 30/h), the mean error and MAE of AHI between the two automated methods were also statistically significant (P < 0.01). CONCLUSIONS Generally, good agreements were shown between our automated detection and clinical reports. This procedure is robust and effective, which would significantly shorten the analysis time.
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Kang CH, Erdenebayar U, Park JU, Lee KJ. Multi-Class Classification of Sleep Apnea/Hypopnea Events Based on Long Short-Term Memory Using a Photoplethysmography Signal. J Med Syst 2019; 44:14. [PMID: 31811401 DOI: 10.1007/s10916-019-1485-0] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/03/2018] [Accepted: 10/15/2019] [Indexed: 10/25/2022]
Abstract
In this study, we proposed a new method for multi-class classification of sleep apnea/hypopnea events based on a long short-term memory (LSTM) using photoplethysmography (PPG) signals. The three-layer LSTM model was used with batch-normalization and dropout to classify the multi-class events including normal, apnea, and hypopnea. The PPG signals, which were measured by the nocturnal polysomnography with 7 h from 82 patients suffered from sleep apnea, were used to model training and evaluation. The performance of the proposed method was evaluated on the training set from 63 patients and test set from 13 patients. The results of the LSTM model showed the following high performances: the positive predictive value of 94.16% for normal, 81.38% for apnea, and 97.92% for hypopnea; sensitivity of 86.03% for normal, 91.24% for apnea, and 99.38% for hypopnea events. The proposed method had especially higher performance of hypopnea classification which had been a drawback of previous studies. Furthermore, it can be applied to a system that can classify sleep apnea/hypopnea and normal events automatically without expert's intervention at home.
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Affiliation(s)
- Chang-Hoon Kang
- Department of Biomedical Engineering, College of Health Science, Yonsei University, 1, Yeonsedae-gil, Wonju-si, Gangwon-do, 26493, South Korea
| | - Urtnasan Erdenebayar
- Department of Biomedical Engineering, College of Health Science, Yonsei University, 1, Yeonsedae-gil, Wonju-si, Gangwon-do, 26493, South Korea
| | - Jong-Uk Park
- Department of Biomedical Engineering, College of Health Science, Yonsei University, 1, Yeonsedae-gil, Wonju-si, Gangwon-do, 26493, South Korea
| | - Kyoung-Joung Lee
- Department of Biomedical Engineering, College of Health Science, Yonsei University, 1, Yeonsedae-gil, Wonju-si, Gangwon-do, 26493, South Korea.
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In obstructive sleep apnea patients, automatic determination of respiratory arrests by photoplethysmography signal and heart rate variability. AUSTRALASIAN PHYSICAL & ENGINEERING SCIENCES IN MEDICINE 2019; 42:959-979. [PMID: 31515685 DOI: 10.1007/s13246-019-00796-9] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/09/2019] [Accepted: 08/30/2019] [Indexed: 01/09/2023]
Abstract
Obstructive sleep apnea is a disease that occurs in connection to pauses in respiration during sleep. Detection of the disease is achieved using a polysomnography device, which is the gold standard in diagnosis. Diagnosis is made by the steps of sleep staging and respiration scoring. Respiration scoring is performed with at least four signals. Technical knowledge is required for attaching the electrodes. Additionally, the electrodes are disturbing to an extent that will delay the patient's sleep. It is needed to have systems as alternatives for polysomnography devices that will bring a solution to these issues. This study proposes a new approach for the process of respiration scoring which is one of the diagnostic steps for the disease. A machine-learning-based apnea detection algorithm was developed for the process of respiration scoring. The study used Photoplethysmography (PPG) signal and Heart Rate Variability (HRV) that is derived from this signal. The PPG records obtained from the patient and control groups were cleaned out using a digital filter. Then, the HRV parameter was derived from the PPG signal. Later, 46 features were derived from the PPG signal and 40 features were derived from the HRV. The derived features were classified with reduced machine-learning techniques using the F-score feature-selection algorithm. The evaluation was made in a multifaceted manner. Besides, Principal Component Analysis was performed to reduce system input (features). According to the results, if a real-time embedded system is designed, the system can operate with 16 PPG feature 95%, four PPG feature 93.81% accuracy rate. These success rates are highly sufficient for the system to work. Considering all these values, it is possible to realize a practical respiration scoring system. With this study, it was agreed upon that PPG signal may be used in the diagnosis of this disease by processing it with machine learning and signal processing techniques.
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Deviaene M, Testelmans D, Buyse B, Borzee P, Van Huffel S, Varon C. Automatic Screening of Sleep Apnea Patients Based on the SpO2 Signal. IEEE J Biomed Health Inform 2019; 23:607-617. [DOI: 10.1109/jbhi.2018.2817368] [Citation(s) in RCA: 31] [Impact Index Per Article: 6.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Affiliation(s)
- Margot Deviaene
- Department of Electrical Engineering-ESAT, STADIUS Center for Dynamical Systems, Signal Processing and Data Analytics, and imec, KU Leuven, Leuven, Belgium
| | | | - Bertien Buyse
- Department of Pneumology, UZ Leuven, Leuven, Belgium
| | - Pascal Borzee
- Department of Pneumology, UZ Leuven, Leuven, Belgium
| | - Sabine Van Huffel
- Department of Electrical Engineering-ESAT, STADIUS Center for Dynamical Systems, Signal Processing and Data Analytics, and imec, KU Leuven, Leuven, Belgium
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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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