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Howarth TP, Sillanmäki S, Karhu T, Rissanen M, Islind AS, Hrubos-Strøm H, de Chazal P, Huovila J, Kainulainen S, Leppänen T. Nocturnal oxygen resaturation parameters are associated with cardiorespiratory comorbidities. Sleep Med 2024; 118:101-112. [PMID: 38657349 DOI: 10.1016/j.sleep.2024.03.047] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/05/2023] [Revised: 03/12/2024] [Accepted: 03/30/2024] [Indexed: 04/26/2024]
Abstract
BACKGROUND There are strong associations between oxygen desaturations and cardiovascular outcomes. Additionally, oxygen resaturation rates are linked to excessive daytime sleepiness independent of oxygen desaturation severity. No studies have yet looked at the independent effects of comorbidities or medications on resaturation parameters. METHODS The Sleep Heart Health Study data was utilised to derive oxygen saturation parameters from 5804 participants. Participants with a history of comorbidities or medication usage were compared against healthy participants with no comorbidity/medication history. RESULTS 4293 participants (50.4% female, median age 64 years) were included in the analysis. Females recorded significantly faster resaturation rates (mean 0.61%/s) than males (mean 0.57%/s, p < 0.001), regardless of comorbidities. After adjusting for demographics, sleep parameters, and desaturation parameters, resaturation rate was reduced with hypertension (-0.09 (95% CI -0.16, -0.03)), myocardial infarction (-0.13 (95% CI -0.21, -0.04)) and heart failure (-0.19 (95% CI -0.33, -0.05)), or when using anti-hypertensives (-0.10 (95% CI -0.17, -0.03)), mental health medications (-0.18 (95% CI -0.27, -0.08)) or anticoagulants (-0.41 (95% CI -0.56, -0.26)). Desaturation to Resaturation ratio for duration was decreased with mental health (-0.21 (95% CI -0.34, -0.08)) or diabetic medications (-0.24 (95% CI -0.41, -0.07)), and desaturation to resaturation ratio for area decreased with heart failure (-0.25 (95% CI -0.42, -0.08)). CONCLUSIONS Comorbidities and medications significantly affect nocturnal resaturation parameters, independent of desaturation parameters. However, the causal relationship remains unclear. Further research can enhance our knowledge and develop more precise and safer interventions for individuals affected by certain comorbidities.
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Affiliation(s)
- Timothy P Howarth
- Department of Technical Physics, University of Eastern Finland, Kuopio, Finland; Darwin Respiratory and Sleep Health, Darwin Private Hospital, Darwin, Australia; College of Health and Human Sciences, Charles Darwin University, Darwin, Australia; Diagnostic Imaging Center, Kuopio University Hospital, Kuopio, Finland.
| | - Saara Sillanmäki
- Diagnostic Imaging Center, Kuopio University Hospital, Kuopio, Finland; Faculty of Medicine, University of Eastern Finland, Kuopio, Finland
| | - Tuomas Karhu
- Department of Technical Physics, University of Eastern Finland, Kuopio, Finland; Diagnostic Imaging Center, Kuopio University Hospital, Kuopio, Finland
| | - Marika Rissanen
- Department of Technical Physics, University of Eastern Finland, Kuopio, Finland; Department of Clinical Neurophysiology, Seinäjoki Central Hospital, Seinäjoki, Finland
| | | | - Harald Hrubos-Strøm
- Department of Otorhinolaryngology, Akershus University Hospital, Lørenskog, Norway; Clinic for Surgical Research, Campus Ahus, Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Philip de Chazal
- School of Biomedical Engineering, The University of Sydney, Sydney, Australia
| | - Juuso Huovila
- Department of Technical Physics, University of Eastern Finland, Kuopio, Finland
| | - Samu Kainulainen
- Department of Technical Physics, University of Eastern Finland, Kuopio, Finland; Diagnostic Imaging Center, Kuopio University Hospital, Kuopio, Finland
| | - Timo Leppänen
- Department of Technical Physics, University of Eastern Finland, Kuopio, Finland; Diagnostic Imaging Center, Kuopio University Hospital, Kuopio, Finland; School of Electrical Engineering and Computer Science, The University of Queensland, Brisbane, Australia
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Cohen O, Kundel V, Robson P, Al-Taie Z, Suárez-Fariñas M, Shah NA. Achieving Better Understanding of Obstructive Sleep Apnea Treatment Effects on Cardiovascular Disease Outcomes through Machine Learning Approaches: A Narrative Review. J Clin Med 2024; 13:1415. [PMID: 38592223 PMCID: PMC10932326 DOI: 10.3390/jcm13051415] [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: 01/31/2024] [Revised: 02/13/2024] [Accepted: 02/17/2024] [Indexed: 04/10/2024] Open
Abstract
Obstructive sleep apnea (OSA) affects almost a billion people worldwide and is associated with a myriad of adverse health outcomes. Among the most prevalent and morbid are cardiovascular diseases (CVDs). Nonetheless, randomized controlled trials (RCTs) of OSA treatment have failed to show improvements in CVD outcomes. A major limitation in our field is the lack of precision in defining OSA and specifically subgroups with the potential to benefit from therapy. Further, this has called into question the validity of using the time-honored apnea-hypopnea index as the ultimate defining criteria for OSA. Recent applications of advanced statistical methods and machine learning have brought to light a variety of OSA endotypes and phenotypes. These methods also provide an opportunity to understand the interaction between OSA and comorbid diseases for better CVD risk stratification. Lastly, machine learning and specifically heterogeneous treatment effects modeling can help uncover subgroups with differential outcomes after treatment initiation. In an era of data sharing and big data, these techniques will be at the forefront of OSA research. Advanced data science methods, such as machine-learning analyses and artificial intelligence, will improve our ability to determine the unique influence of OSA on CVD outcomes and ultimately allow us to better determine precision medicine approaches in OSA patients for CVD risk reduction. In this narrative review, we will highlight how team science via machine learning and artificial intelligence applied to existing clinical data, polysomnography, proteomics, and imaging can do just that.
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Affiliation(s)
- Oren Cohen
- Department of Medicine, Division of Pulmonary, Critical Care and Sleep Medicine, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA; (O.C.); (V.K.)
| | - Vaishnavi Kundel
- Department of Medicine, Division of Pulmonary, Critical Care and Sleep Medicine, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA; (O.C.); (V.K.)
| | - Philip Robson
- Biomedical Engineering and Imaging Institute, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA;
| | - Zainab Al-Taie
- Center for Biostatistics, Department of Population Health Science and Policy, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA; (Z.A.-T.); (M.S.-F.)
| | - Mayte Suárez-Fariñas
- Center for Biostatistics, Department of Population Health Science and Policy, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA; (Z.A.-T.); (M.S.-F.)
| | - Neomi A. Shah
- Department of Medicine, Division of Pulmonary, Critical Care and Sleep Medicine, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA; (O.C.); (V.K.)
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Moradhasel B, Sheikhani A, Aloosh O, Jafarnia Dabanloo N. Spectrogram classification of patient chin electromyography based on deep learning: A novel method for accurate diagnosis obstructive sleep apnea. Biomed Signal Process Control 2023. [DOI: 10.1016/j.bspc.2022.104215] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/14/2022]
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4
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Sun C, Hong S, Wang J, Dong X, Han F, Li H. A systematic review of deep learning methods for modeling electrocardiograms during sleep. Physiol Meas 2022; 43. [PMID: 35853448 DOI: 10.1088/1361-6579/ac826e] [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: 01/25/2022] [Accepted: 07/19/2022] [Indexed: 11/11/2022]
Abstract
Sleep is one of the most important human physiological activities and plays an essential role in human health. Polysomnography (PSG) is the gold standard for measuring sleep quality and disorders, but it is time-consuming, labor-intensive, and prone to errors. Current research has confirmed the correlations between sleep and the respiratory/circulatory system. Electrocardiography (ECG) is convenient to perform, and ECG data are rich in breathing information. Therefore, sleep research based on ECG data has become popular. Currently, deep learning (DL) methods have achieved promising results on predictive health care tasks using ECG signals. Therefore, in this review, we systematically identify recent research studies and analyze them from the perspectives of data, model, and task. We discuss the shortcomings, summarize the findings, and highlight the potential opportunities. For sleep-related tasks, many ECG-based DL methods produce more accurate results than traditional approaches by combining multiple signal features and model structures. Methods that are more interpretable, scalable, and transferable will become ubiquitous in the daily practice of medicine and ambient-assisted-living applications. This paper is the first systematic review of ECG-based DL methods for sleep tasks.
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Affiliation(s)
- Chenxi Sun
- School of Artificial Intelligence, Peking University, No. 5, Yiheyuan Road, Haidian District, Beijing, 100871, CHINA
| | - Shenda Hong
- National Institute of Health Data Science, Peking University, No. 5, Yiheyuan Road, Haidian District, Beijing, Beijing, 100871, CHINA
| | - Jingyu Wang
- Sleep Center, Department of Respiratory and Critical Care Medicine, Peking University People's Hospital, No. 11, Xizhimen South Street, Xicheng District, Beijing, 100044, CHINA
| | - Xiaosong Dong
- Sleep Center, Department of Respiratory and Critical Care Medicine, Peking University People's Hospital, No. 11, Xizhimen South Street, Xicheng District, Beijing, 100044, CHINA
| | - Fang Han
- Sleep Center, Department of Respiratory and Critical Care Medicine, Peking University People's Hospital, No. 11, Xizhimen South Street, Xicheng District, Beijing, 100044, CHINA
| | - Hongyan Li
- School of Artificial Intelligence, Peking University, No. 5, Yiheyuan Road, Haidian District, Beijing, Beijing, 100871, CHINA
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Arnardottir ES, Islind AS, Óskarsdóttir M, Ólafsdóttir KA, August E, Jónasdóttir L, Hrubos-Strøm H, Saavedra JM, Grote L, Hedner J, Höskuldsson S, Ágústsson JS, Jóhannsdóttir KR, McNicholas WT, Pevernagie D, Sund R, Töyräs J, Leppänen T. The Sleep Revolution project: the concept and objectives. J Sleep Res 2022; 31:e13630. [PMID: 35770626 DOI: 10.1111/jsr.13630] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2022] [Revised: 04/19/2022] [Accepted: 04/19/2022] [Indexed: 12/18/2022]
Abstract
Obstructive sleep apnea is linked to severe health consequences such as hypertension, daytime sleepiness, and cardiovascular disease. Nearly a billion people are estimated to have obstructive sleep apnea with a substantial economic burden. However, the current diagnostic parameter of obstructive sleep apnea, the apnea-hypopnea index, correlates poorly with related comorbidities and symptoms. Obstructive sleep apnea severity is measured by counting respiratory events, while other physiologically relevant consequences are ignored. Furthermore, as the clinical methods for analysing polysomnographic signals are outdated, laborious, and expensive, most patients with obstructive sleep apnea remain undiagnosed. Therefore, more personalised diagnostic approaches are urgently needed. The Sleep Revolution, funded by the European Union's Horizon 2020 Research and Innovation Programme, aims to tackle these shortcomings by developing machine learning tools to better estimate obstructive sleep apnea severity and phenotypes. This allows for improved personalised treatment options, including increased patient participation. Also, implementing these tools will alleviate the costs and increase the availability of sleep studies by decreasing manual scoring labour. Finally, the project aims to design a digital platform that functions as a bridge between researchers, patients, and clinicians, with an electronic sleep diary, objective cognitive tests, and questionnaires in a mobile application. These ambitious goals will be achieved through extensive collaboration between 39 centres, including expertise from sleep medicine, computer science, and industry and by utilising tens of thousands of retrospectively and prospectively collected sleep recordings. With the commitment of the European Sleep Research Society and Assembly of National Sleep Societies, the Sleep Revolution has the unique possibility to create new standardised guidelines for sleep medicine.
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Affiliation(s)
- Erna S Arnardottir
- Reykjavik University Sleep Institute, Reykjavik University, Reykjavik, Iceland.,Landspitali University Hospital, Reykjavik, Iceland
| | - Anna Sigridur Islind
- Reykjavik University Sleep Institute, Reykjavik University, Reykjavik, Iceland.,Department of Computer Science, Reykjavik University, Reykjavik, Iceland
| | - María Óskarsdóttir
- Reykjavik University Sleep Institute, Reykjavik University, Reykjavik, Iceland.,Department of Computer Science, Reykjavik University, Reykjavik, Iceland
| | | | - Elias August
- Reykjavik University Sleep Institute, Reykjavik University, Reykjavik, Iceland.,Department of Engineering, Reykjavik University, Reykjavik, Iceland
| | - Lára Jónasdóttir
- Reykjavik University Sleep Institute, Reykjavik University, Reykjavik, Iceland
| | - Harald Hrubos-Strøm
- Department of Otorhinolaryngology, Akershus University Hospital, Lørenskog, Norway.,Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Jose M Saavedra
- Reykjavik University Sleep Institute, Reykjavik University, Reykjavik, Iceland.,Physical Activity, Physical Education, Sport and Health (PAPESH) Research Group, Department of Sports Science, Reykjavik University, Reykjavik, Iceland
| | - Ludger Grote
- Internal Medicine, Center for Sleep and Wake Disorders, University of Gothenburg Sahlgrenska Academy, Gothenburg, Sweden
| | - Jan Hedner
- Internal Medicine, Center for Sleep and Wake Disorders, University of Gothenburg Sahlgrenska Academy, Gothenburg, Sweden
| | | | | | - Kamilla Rún Jóhannsdóttir
- Reykjavik University Sleep Institute, Reykjavik University, Reykjavik, Iceland.,Department of Psychology, Reykjavik University, Reykjavik, Iceland
| | - Walter T McNicholas
- Department of Respiratory and Sleep Medicine, St. Vincent's Hospital Group, School of Medicine, University College Dublin, Dublin, Ireland
| | - Dirk Pevernagie
- Respiratory Diseases, University Hospital Ghent, Ghent, Belgium.,Department of Internal Medicine and Paediatrics, Ghent University, Ghent, Belgium
| | - Reijo Sund
- Department of Applied Physics, University of Eastern Finland, Kuopio, Finland
| | - Juha Töyräs
- Department of Applied Physics, University of Eastern Finland, Kuopio, Finland.,School of Information Technology and Electrical Engineering, The University of Queensland, Brisbane, Queensland, Australia.,Science Service Center, Kuopio University Hospital, Kuopio, Finland
| | - Timo Leppänen
- Department of Applied Physics, University of Eastern Finland, Kuopio, Finland.,School of Information Technology and Electrical Engineering, The University of Queensland, Brisbane, Queensland, Australia.,Diagnostic Imaging Center, Kuopio University Hospital, Kuopio, Finland
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Kim S, Shin DY, Kim T, Lee S, Hyun JK, Park SM. Enhanced Recognition of Amputated Wrist and Hand Movements by Deep Learning Method Using Multimodal Fusion of Electromyography and Electroencephalography. SENSORS 2022; 22:s22020680. [PMID: 35062641 PMCID: PMC8778369 DOI: 10.3390/s22020680] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/08/2021] [Revised: 01/09/2022] [Accepted: 01/14/2022] [Indexed: 02/04/2023]
Abstract
Motion classification can be performed using biometric signals recorded by electroencephalography (EEG) or electromyography (EMG) with noninvasive surface electrodes for the control of prosthetic arms. However, current single-modal EEG and EMG based motion classification techniques are limited owing to the complexity and noise of EEG signals, and the electrode placement bias, and low-resolution of EMG signals. We herein propose a novel system of two-dimensional (2D) input image feature multimodal fusion based on an EEG/EMG-signal transfer learning (TL) paradigm for detection of hand movements in transforearm amputees. A feature extraction method in the frequency domain of the EEG and EMG signals was adopted to establish a 2D image. The input images were used for training on a model based on the convolutional neural network algorithm and TL, which requires 2D images as input data. For the purpose of data acquisition, five transforearm amputees and nine healthy controls were recruited. Compared with the conventional single-modal EEG signal trained models, the proposed multimodal fusion method significantly improved classification accuracy in both the control and patient groups. When the two signals were combined and used in the pretrained model for EEG TL, the classification accuracy increased by 4.18-4.35% in the control group, and by 2.51-3.00% in the patient group.
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Affiliation(s)
- Sehyeon Kim
- Department of Convergence IT Engineering, Pohang University of Science and Technology, Pohang 37673, Korea;
| | - Dae Youp Shin
- Department of Rehabilitation Medicine, College of Medicine, Dankook University, Cheonan 31116, Korea;
| | - Taekyung Kim
- Department of Medical Device Management and Research, SAIHST, Sungkyunkwan University, Seoul 03063, Korea;
| | - Sangsook Lee
- Department of Rehabilitation Medicine, Daejeon Hospital, Daejeon 34383, Korea;
| | - Jung Keun Hyun
- Department of Rehabilitation Medicine, College of Medicine, Dankook University, Cheonan 31116, Korea;
- Department of Nanobiomedical Science & BK21 NBM Global Research Center for Regenerative Medicine, Dankook University, Cheonan 31116, Korea
- Institute of Tissue Regeneration Engineering (ITREN), Dankook University, Cheonan 31116, Korea
- Correspondence: (J.K.H.); (S.-M.P.); Tel.: +82-10-2293-3415 (J.K.H.); +82-10-7208-7740 (S.-M.P.)
| | - Sung-Min Park
- Department of Convergence IT Engineering, Pohang University of Science and Technology, Pohang 37673, Korea;
- Department of Electrical Engineering, Pohang University of Science and Technology, Pohang 37673, Korea
- Department of Mechanical Engineering, Pohang University of Science and Technology, Pohang 37673, Korea
- Correspondence: (J.K.H.); (S.-M.P.); Tel.: +82-10-2293-3415 (J.K.H.); +82-10-7208-7740 (S.-M.P.)
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Application of machine learning analysis based on diffusion tensor imaging to identify REM sleep behavior disorder. Sleep Breath 2021; 26:633-640. [PMID: 34236578 DOI: 10.1007/s11325-021-02434-9] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2021] [Revised: 06/28/2021] [Accepted: 06/30/2021] [Indexed: 12/19/2022]
Abstract
PURPOSE We evaluated the feasibility of machine learning analysis using diffusion tensor imaging (DTI) parameters to identify patients with idiopathic rapid eye movement (REM) sleep behavior disorder (RBD). We hypothesized that patients with idiopathic RBD could be identified via machine learning analysis based on DTI. METHODS We enrolled 20 patients with newly diagnosed idiopathic RBD at a tertiary hospital. We also included 20 healthy subjects as a control group. All of the subjects underwent DTI. We obtained the conventional DTI parameters and structural connectomic profiles from the DTI. We investigated the differences in conventional DTI measures and structural connectomic profiles between patients with idiopathic RBD and healthy controls. We then used machine learning analysis using a support vector machine (SVM) algorithm to identify patients with idiopathic RBD using conventional DTI and structural connectomic profiles. RESULTS Several regions showed significant differences in conventional DTI measures and structural connectomic profiles between patients with idiopathic RBD and healthy controls. The SVM classifier based on conventional DTI measures revealed an accuracy of 87.5% and an area under the curve of 0.900 to identify patients with idiopathic RBD. Another SVM classifier based on structural connectomic profiles yielded an accuracy of 75.0% and an area under the curve of 0.833. CONCLUSION Our findings demonstrate the feasibility of machine learning analysis based on DTI to identify patients with idiopathic RBD. The conventional DTI parameters might be more important than the structural connectomic profiles in identifying patients with idiopathic RBD.
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Nasifoglu H, Erogul O. Obstructive sleep apnea prediction from electrocardiogram scalograms and spectrograms using convolutional neural networks. Physiol Meas 2021; 42. [PMID: 34116519 DOI: 10.1088/1361-6579/ac0a9c] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2021] [Accepted: 06/11/2021] [Indexed: 11/12/2022]
Abstract
Objective.In this study, we conducted a comparative analysis of deep convolutional neural network (CNN) models in predicting obstructive sleep apnea (OSA) using electrocardiograms. Unlike other studies in the literature, this study automatically extracts time-frequency features by using CNNs instead of manual feature extraction from ECG recordings.Approach.The proposed model generates scalogram and spectrogram representations by transforming preprocessed 30 s ECG segments from time domain to the frequency domain using continuous wavelet transform and short time Fourier transform, respectively. We examined AlexNet, GoogleNet and ResNet18 models in predicting OSA events. The effect of transfer learning on success is also investigated. Based on the observed results, we proposed a new model that is found more effective in estimation. In total, 152 ECG recordings were included in the study for training and evaluation of the models.Main results.The prediction using scalograms immediately 30 s before potential OSA onsets gave the best performance with 82.30% accuracy, 83.22% sensitivity, 82.27% specificity and 82.95% positive predictive value. The prediction using spectrograms also achieved up to 80.13% accuracy and 81.99% sensitivity on prediction. Per-recording classification suggested considerable results with 91.93% accuracy for prediction of OSA events.Significance.Time-frequency deep features of scalograms and spectrograms of ECG segments prior to OSA events provided reliable information about the possible events in the future. The proposed CNN model can be used as a good indicator to accurately predict OSA events using ECG recordings.
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Affiliation(s)
- Huseyin Nasifoglu
- Department of Biomedical Engineering, TOBB University of Economics and Technology, Ankara 06560, Turkey
| | - Osman Erogul
- Department of Biomedical Engineering, TOBB University of Economics and Technology, Ankara 06560, Turkey
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Kainulainen S, Töyräs J, Oksenberg A, Korkalainen H, Afara IO, Leino A, Kalevo L, Nikkonen S, Gadoth N, Kulkas A, Myllymaa S, Leppänen T. Power spectral densities of nocturnal pulse oximetry signals differ in OSA patients with and without daytime sleepiness. Sleep Med 2020; 73:231-237. [DOI: 10.1016/j.sleep.2020.07.015] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/20/2020] [Revised: 06/25/2020] [Accepted: 07/10/2020] [Indexed: 02/07/2023]
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