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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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Varis M, Karhu T, Leppänen T, Nikkonen S. Utilizing Envelope Analysis of a Nasal Pressure Signal for Sleep Apnea Severity Estimation. Diagnostics (Basel) 2023; 13:diagnostics13101776. [PMID: 37238259 DOI: 10.3390/diagnostics13101776] [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: 04/25/2023] [Revised: 05/12/2023] [Accepted: 05/13/2023] [Indexed: 05/28/2023] Open
Abstract
Obstructive sleep apnea (OSA) severity assessment is based on manually scored respiratory events and their arbitrary definitions. Thus, we present an alternative method to objectively evaluate OSA severity independently of the manual scorings and scoring rules. A retrospective envelope analysis was conducted on 847 suspected OSA patients. Four parameters were calculated from the difference between the nasal pressure signal's upper and lower envelopes: average (AV), median (MD), standard deviation (SD), and coefficient of variation (CoV). We computed the parameters from the entirety of the recorded signals to perform binary classifications of patients using three different apnea-hypopnea index (AHI) thresholds (5-15-30). Additionally, the calculations were undertaken in 30-second epochs to estimate the ability of the parameters to detect manually scored respiratory events. Classification performances were assessed with areas under the curves (AUCs). As a result, the SD (AUCs ≥ 0.86) and CoV (AUCs ≥ 0.82) were the best classifiers for all AHI thresholds. Furthermore, non-OSA and severe OSA patients were separated well with SD (AUC = 0.97) and CoV (AUC = 0.95). Respiratory events within the epochs were identified moderately with MD (AUC = 0.76) and CoV (AUC = 0.82). In conclusion, envelope analysis is a promising alternative method by which to assess OSA severity without relying on manual scoring or the scoring rules of respiratory events.
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Affiliation(s)
- Mikke Varis
- Department of Technical Physics, University of Eastern Finland, Canthia, P.O. Box 1627, FI-70211 Kuopio, Finland
- Diagnostic Imaging Center, Kuopio University Hospital, FI-70210 Kuopio, Finland
| | - Tuomas Karhu
- Department of Technical Physics, University of Eastern Finland, Canthia, P.O. Box 1627, FI-70211 Kuopio, Finland
- Diagnostic Imaging Center, Kuopio University Hospital, FI-70210 Kuopio, Finland
| | - Timo Leppänen
- Department of Technical Physics, University of Eastern Finland, Canthia, P.O. Box 1627, FI-70211 Kuopio, Finland
- Diagnostic Imaging Center, Kuopio University Hospital, FI-70210 Kuopio, Finland
- School of Information Technology and Electrical Engineering, The University of Queensland, Brisbane, QLD 4072, Australia
| | - Sami Nikkonen
- Department of Technical Physics, University of Eastern Finland, Canthia, P.O. Box 1627, FI-70211 Kuopio, Finland
- Diagnostic Imaging Center, Kuopio University Hospital, FI-70210 Kuopio, Finland
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Uddin MB, Chow CM, Ling SH, Su SW. A generalized algorithm for the automatic diagnosis of sleep apnea from per-sample encoding of airflow and oximetry. Physiol Meas 2022; 43. [PMID: 35477173 DOI: 10.1088/1361-6579/ac6b11] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2021] [Accepted: 04/27/2022] [Indexed: 11/12/2022]
Abstract
OBJECTIVE Sleep apnea is a common sleep breathing disorder that can significantly decrease sleep quality and have major health consequences. It is diagnosed based on the apnea hypopnea index (AHI). This study explored a novel, generalized algorithm for the automatic diagnosis of sleep apnea employing airflow (AF) and oximetry (SpO2) signals. APPROACH Of the 988 polysomnography records, 45 were randomly selected for developing the automatic algorithm and the remainder 943 for validating purposes. The algorithm detects apnea events by a per-sample encoding process applied to the peak excursion of AF signal. Hypopnea events were detected from the per-sample encoding of AF and SpO2 with an adjustment to time lag in SpO2. Total recording time was automatically processed and optimized for computation of total sleep time (TST). Total number of detected events and computed TST were used to estimate AHI. The estimated AHI was validated against the scored data from the Sleep Heart Health Study. MAIN RESULTS Intraclass correlation coefficient of 0.94 was obtained between estimated and scored AHIs. The diagnostic accuracies were 93.5%, 92.4%, and 96.6% for AHI cut-off values of ≥5, ≥15, and ≥30 respectively. The overall accuracy for the combined severity categories (normal, mild, moderate, and severe) and kappa were 83.4% and 0.77 respectively. SIGNIFICANCE This new automatic technique was found to be superior to the other existing methods and can be applied to any portable sleep devices especially for home sleep apnea tests.
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Affiliation(s)
- Md Bashir Uddin
- Biomedical Engineering, Khulna University of Engineering and Technology, Khulna University of Engineering & Technology, Khulna-9203, Bangladesh, Khulna, 9203, BANGLADESH
| | - Chin-Moi Chow
- Faculty of Health Sciences, The University of Sydney, The University of Sydney, Sydney, NSW 2006, Sydney, New South Wales, NSW 2006, AUSTRALIA
| | - Steve H Ling
- University of Technology Sydney, University of Technology Sydney, Sydney, NSW 2007, Sydney, New South Wales, NSW 2007, AUSTRALIA
| | - Steven W Su
- Biomedical Systems Laboratory, The University of New South Wales, Sydney 2052, N.S.W., Sydney, 2007, AUSTRALIA
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Automated detection of obstructive sleep apnea in more than 8000 subjects using frequency optimized orthogonal wavelet filter bank with respiratory and oximetry signals. Comput Biol Med 2022; 144:105364. [PMID: 35299046 DOI: 10.1016/j.compbiomed.2022.105364] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2021] [Revised: 02/27/2022] [Accepted: 02/27/2022] [Indexed: 12/12/2022]
Abstract
Obstructive sleep apnea (OSA) is a common respiratory disorder marked by interruption of the respiratory tract and difficulty in breathing. The risk of serious health damage can be reduced if OSA is diagnosed and treated at an early stage. OSA is primarily diagnosed using polysomnography (PSG) monitoring performed for overnight sleep; furthermore, capturing PSG signals during the night is expensive, time-consuming, complex and highly inconvenient to patients. Hence, we are proposing to detect OSA automatically using respiratory and oximetry signals. The aim of this study is to develop a simple and computationally efficient wavelet-based automated system based on these signals to detect OSA in elderly subjects. In this study, we proposed an accurate, reliable, and less complex OSA automated detection system by using pulse oximetry (SpO2) and respiratory signals including thoracic (ThorRes) movement, abdominal (AbdoRes) movement, and airflow (AF). These signals are collected from the Sleep Heart Health Study (SHHS) database from the National Sleep Research Resource (NSRR), which is one of the largest repositories of publicly available sleep databases. The database comprises of two groups SHHS-1 and SHHS-2, which involves 5,793 and 2,651 subjects, respectively with an average age of ≥60 years. The 30-s epochs of the signals are decomposed into sub-bands using frequency optimized orthogonal wavelet filter bank. Tsallis entropies are extracted from the sub-band coefficients of wavelet filter bank. A total 4,415,229 epochs of respiratory and oximetry signals are used to develop the model. The proposed model is developed using GentleBoost and Random under-sampling Boosting (RUSBoosted Tree) algorithms with 10-fold cross-validation technique. Our developed model has obtained the highest classification accuracy of 89.39% and 84.64% for the imbalanced and balanced datasets, respectively using 10-fold cross-validation technique. Using the 20% hold-out validation, the model yielded an accuracy of 88.26% and 84.31% for the imbalanced and balanced datasets, respectively. Hence, the respiratory and SpO2 signals-based model can be used for automated OSA detection. The results obtained from the proposed model are better than the state-of-the-art models and can be used in-home for screening the OSA.
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Barroso-García V, Jiménez-García J, Gutiérrez-Tobal GC, Hornero R. Airflow Analysis in the Context of Sleep Apnea. ADVANCES IN EXPERIMENTAL MEDICINE AND BIOLOGY 2022; 1384:241-253. [PMID: 36217088 DOI: 10.1007/978-3-031-06413-5_14] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
Abstract
The airflow (AF) is a physiological signal involved in the overnight polysomnography (PSG) that reflects the respiratory activity. This signal is able to show the particularities of sleep apnea and is therefore used to define apneic events. In this regard, a growing number of studies have shown the usefulness of employing the overnight airflow as the only or combined information source for diagnosing sleep apnea in both children and adults. Due to its easy acquisition and interpretation, this biosignal has been widely analyzed by means of different signal processing techniques. In this chapter, we review the main methodological approaches applied to characterize and extract relevant information from this signal. In view of the results, we can conclude that the overnight airflow successfully reflects the particularities caused by the occurrence of apneic and hypopneic events and provides useful information for obtaining relevant biomarkers that characterize this disease.
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Affiliation(s)
- Verónica Barroso-García
- Biomedical Engineering Group, University of Valladolid, Valladolid, Spain.
- Centro de Investigación Biomédica en Red en Bioingeniería, Biomateriales y Nanomedicina (CIBER-BBN), Valladolid, Spain.
| | | | - Gonzalo C Gutiérrez-Tobal
- Biomedical Engineering Group, University of Valladolid, Valladolid, Spain
- Centro de Investigación Biomédica en Red en Bioingeniería, Biomateriales y Nanomedicina (CIBER-BBN), Valladolid, Spain
| | - Roberto Hornero
- Biomedical Engineering Group, University of Valladolid, Valladolid, Spain
- Centro de Investigación Biomédica en Red en Bioingeniería, Biomateriales y Nanomedicina (CIBER-BBN), Valladolid, Spain
- Mathematics Research Institute of the University of Valladolid (IMUVa), 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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Drzazga J, Cyganek B. An LSTM Network for Apnea and Hypopnea Episodes Detection in Respiratory Signals. SENSORS (BASEL, SWITZERLAND) 2021; 21:5858. [PMID: 34502748 PMCID: PMC8434530 DOI: 10.3390/s21175858] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/07/2021] [Revised: 08/24/2021] [Accepted: 08/27/2021] [Indexed: 11/17/2022]
Abstract
One of the most common sleep disorders is sleep apnea. It manifests itself by episodes of shallow breathing or pauses in breathing during the night. Diagnosis of this disease involves polysomnography examination, which is expensive. Alternatively, diagnostic doctors can be supported with recordings from the in-home polygraphy sensors. Furthermore, numerous attempts for providing an automated apnea episodes annotation algorithm have been made. Most of them, however, do not distinguish between apnea and hypopnea episodes. In this work, a novel solution for epoch-based annotation problem is presented. Utilizing an architecture based on the long short-term memory (LSTM) networks, the proposed model provides locations of sleep disordered breathing episodes and identifies them as either apnea or hypopnea. To achieve this, special pre- and postprocessing steps have been designed. The obtained labels can be then used for calculation of the respiratory event index (REI), which serves as a disease severity indicator. The input for the model consists of the oronasal airflow along with the thoracic and abdominal respiratory effort signals. Performance of the proposed architecture was verified on the SHHS-1 and PhysioNet Sleep databases, obtaining mean REI classification error of 9.24/10.52 with standard deviation of 11.61/7.92 (SHHS-1/PhysioNet). Normal breathing, hypopnea and apnea differentiation accuracy is assessed on both databases, resulting in the correctly classified samples percentage of 86.42%/84.35%, 49.30%/58.28% and 68.20%/69.50% for normal breathing, hypopnea and apnea classes, respectively. Overall accuracies are 80.66%/82.04%. Additionally, the effect of wake periods is investigated. The results show that the proposed model can be successfully used for both episode classification and REI estimation tasks.
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Affiliation(s)
- Jakub Drzazga
- Department of Electronics, AGH University of Science and Technology, 30-059 Kraków, Poland;
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