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Mortazavi E, Tarvirdizadeh B, Alipour K, Ghamari M. Deep learning approaches for assessing pediatric sleep apnea severity through SpO2 signals. Sci Rep 2024; 14:22696. [PMID: 39353980 PMCID: PMC11445237 DOI: 10.1038/s41598-024-67729-9] [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: 02/07/2024] [Accepted: 07/15/2024] [Indexed: 10/03/2024] Open
Abstract
Pediatric Sleep Apnea-Hypopnea (SAH) presents a significant health challenge, particularly in diagnostic contexts, where conventional Polysomnography (PSG) testing, although effective, can be distressing for children. Addressing this, our research proposes a less invasive method to assess pediatric SAH severity by analyzing blood oxygen saturation (SpO2) signals. We adopted two advanced deep learning architectures, namely ResNet-based and attention-augmented hybrid CNN-BiGRU models, to process SpO2 signals in a one-dimensional (1D) format for Apnea-Hypopnea Index (AHI) estimation in pediatric subjects. Employing the CHAT dataset, which includes 844 SpO2 signals, the data was partitioned into training (60%), testing (30%), and validation (10%) sets. A predefined validation subset was randomly selected to ensure the models' robustness via a threefold cross-validation approach. Comparative analysis revealed that while the ResNet model attained an average accuracy of 72.9% across four SAH severity categories with a kappa score of 0.57, the CNN-BiGRU-Attention model demonstrated superior performance, achieving an average accuracy of 75.95% and a kappa score of 0.63. This distinction underscores our method's efficacy in both estimating AHI and categorizing SAH severity levels with notable precision. Further, to evaluate diagnostic capabilities, the models were benchmarked against common AHI thresholds (1, 5, and 10 events/hour) in each test fold, affirming their effectiveness in identifying pediatric SAH. This study marks a significant advance in the field, offering a non-invasive, child-friendly alternative for pediatric SAH diagnosis. Although challenges persist in accurately estimating AHI, particularly in severe cases, our findings represent a critical stride towards improving diagnostic processes in pediatric SAH.
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Affiliation(s)
- Erfan Mortazavi
- Advanced Service Robots (ASR) Laboratory, Department of Mechatronics Engineering, School of Intelligent Systems Engineering, College of Interdisciplinary Science and Technology, University of Tehran, Tehran, Iran
| | - Bahram Tarvirdizadeh
- Advanced Service Robots (ASR) Laboratory, Department of Mechatronics Engineering, School of Intelligent Systems Engineering, College of Interdisciplinary Science and Technology, University of Tehran, Tehran, Iran.
| | - Khalil Alipour
- Advanced Service Robots (ASR) Laboratory, Department of Mechatronics Engineering, School of Intelligent Systems Engineering, College of Interdisciplinary Science and Technology, University of Tehran, Tehran, Iran
| | - Mohammad Ghamari
- Department of Electrical Eng., California Polytechnic State University, San Luis Obispo, CA, 93407, USA
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Landry V, Semsar-Kazerooni K, Chen T, Gurberg J, Nguyen LHP, Constantin E. Diagnostic accuracy of portable sleep monitors in pediatric sleep apnea: A systematic review. Sleep Med Rev 2024; 78:101991. [PMID: 39173472 DOI: 10.1016/j.smrv.2024.101991] [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: 12/11/2023] [Revised: 05/01/2024] [Accepted: 08/05/2024] [Indexed: 08/24/2024]
Abstract
In recent years, a plethora of new type III and IV portable sleep monitors (PSM) have been developed, although evidence regarding their diagnostic accuracy for use in children remains heterogeneous. This study systematically reviews the literature addressing the diagnostic accuracies of type III and IV PSM for pediatric sleep apnea. Publications indexed in Medline, Embase, or Web of Science were reviewed using the PRISMA framework. Of 1054 studies, 62 fulfilled the inclusion criteria. Of the studies evaluating oximetry-based type IV PSM, one (6.25 %) demonstrated a balanced set of high (≥80 %) sensitivities and specificities for the diagnosis of any pediatric sleep apnea, while five studies (27.8 %) showed similar accuracies for moderate-to-severe sleep apnea. For non-oximetry-based type IV PSM, two studies (40 %) reported a balanced set of high diagnostic accuracies for moderate-to-severe sleep apnea. Type III PSM repeatedly demonstrated higher diagnostic accuracies, with six studies (66.7 %) reporting a balanced set of high diagnostic accuracies for moderate-to-severe sleep apnea. This review highlights the potential of type III PSM to detect moderate-to-severe pediatric sleep apnea, although current evidence is limited to support the stand-alone use of type IV PSM for the diagnosis of sleep apnea in most children.
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Affiliation(s)
- Vivianne Landry
- Division of Otolaryngology-Head and Neck Surgery, University of Montreal, Montreal, QC, Canada
| | | | - Tanya Chen
- Department of Otolaryngology-Head and Neck Surgery, University of Toronto, Toronto, ON, Canada
| | - Joshua Gurberg
- Department of Otolaryngology-Head and Neck Surgery, McGill University, Montreal, QC, Canada
| | - Lily H P Nguyen
- Department of Otolaryngology-Head and Neck Surgery, McGill University, Montreal, QC, Canada
| | - Evelyn Constantin
- Department of Pediatrics, Pediatric Sleep Medicine, McGill University, Montreal, QC, Canada.
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Lin XX, Lin P, Yeh EH, Liu GR, Lien WC, Fang Y. RAPIDEST: A Framework for Obstructive Sleep Apnea Detection. IEEE Trans Neural Syst Rehabil Eng 2023; 31:387-397. [PMID: 36423308 DOI: 10.1109/tnsre.2022.3224474] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
Abstract
The traditional polysomnography (PSG) examination for Obstructive Sleep Apnea (OSA) diagnosis needs to measure several signals, such as EEG, ECG, EMG, EOG and the oxygen level in blood, of a patient who may have to wear many sensors during sleep. After the PSG examination, the Apnea-Hypopnea Index (AHI) is calculated based on the measured data to evaluate the severity of apnea and hypopnea for the patient. This process is obviously complicated and inconvenient. In this paper, we propose an AI-based framework, called RAre Pattern Identification and DEtection for Sleep-stage Transitions (RAPIDEST), to detect OSA based on the sequence of sleep stages from which a novel rarity score is defined to capture the unusualness of the sequence of sleep stages. More importantly, under this framework, we only need EEG signals, thus significantly simplifying the signal collection process and reducing the complexity of the severity determination of apnea and hypopnea. We have conducted extensive experiments to verify the relationship between the rarity score and AHI and demonstrate the effectiveness of our proposed approach.
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Sharma M, Kumar K, Kumar P, Tan RS, Rajendra Acharya U. Pulse oximetry SpO2signal for automated identification of sleep apnea: a review and future trends. Physiol Meas 2022; 43. [PMID: 36215979 DOI: 10.1088/1361-6579/ac98f0] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2022] [Accepted: 10/10/2022] [Indexed: 02/07/2023]
Abstract
Sleep apnea (SA) is characterized by intermittent episodes of apnea or hypopnea paused or reduced breathing, respectively each lasting at least ten seconds that occur during sleep. SA has an estimated global prevalence of 200 million and is associated with medical comorbidity, and sufferers are also more likely to sustain traffic- and work-related injury due to daytime somnolence. SA is amenable to treatment if detected early. Polysomnography (PSG) involving multi-channel signal acquisition is the reference standard for diagnosing SA but is onerous and costly. For home-based detection of SA, single-channelSpO2signal acquisition using portable pulse oximeters is feasible. Machine (ML) and deep learning (DL) models have been developed for automated classification of SA versus no SA usingSpO2signals alone. In this work, we review studies published between 2012 and 2022 on the use of ML and DL forSpO2signal-based diagnosis of SA. A literature search based on PRISMA recommendations yielded 297 publications, of which 31 were selected after considering the inclusion and exclusion criteria. There were 20 ML and 11 DL models; their methods, differences, results, merits, and limitations were discussed. Many studies reported encouraging performance, which indicates the utility ofSpO2signals in wearable devices for home-based SA detection.
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Affiliation(s)
- Manish Sharma
- Department of Electrical and Computer Science Engineering, Institute of Infrastructure Technology Research and Management, Ahmedabad 380026, India
| | - Kamlesh Kumar
- Department of Electrical and Computer Science Engineering, Institute of Infrastructure Technology Research and Management, Ahmedabad 380026, India
| | - Prince Kumar
- Department of Electrical and Computer Science Engineering, Institute of Infrastructure Technology Research and Management, Ahmedabad 380026, India
| | - Ru-San Tan
- Department of Cardiology, National Heart Centre Singapore, Singapore 169609, Singapore
| | - U Rajendra Acharya
- Department of Electronics and Computer Engineering, Ngee Ann Polytechnic, Singapore 639798, Singapore.,Department of Bioinformatics and Medical Engineering, Asia University, Taichung 41354, Taiwan.,Department of Biomedical Engineering, School of Science and Technology, Singapore 639798, Singapore
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Gutiérrez-Tobal GC, Álvarez D, Kheirandish-Gozal L, Del Campo F, Gozal D, Hornero R. Reliability of machine learning to diagnose pediatric obstructive sleep apnea: Systematic review and meta-analysis. Pediatr Pulmonol 2022; 57:1931-1943. [PMID: 33856128 DOI: 10.1002/ppul.25423] [Citation(s) in RCA: 19] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/12/2021] [Revised: 04/07/2021] [Accepted: 04/10/2021] [Indexed: 12/27/2022]
Abstract
BACKGROUND Machine-learning approaches have enabled promising results in efforts to simplify the diagnosis of pediatric obstructive sleep apnea (OSA). A comprehensive review and analysis of such studies increase the confidence level of practitioners and healthcare providers in the implementation of these methodologies in clinical practice. OBJECTIVE To assess the reliability of machine-learning-based methods to detect pediatric OSA. DATA SOURCES Two researchers conducted an electronic search on the Web of Science and Scopus using term, and studies were reviewed along with their bibliographic references. ELIGIBILITY CRITERIA Articles or reviews (Year 2000 onwards) that applied machine learning to detect pediatric OSA; reported data included information enabling derivation of true positive, false negative, true negative, and false positive cases; polysomnography served as diagnostic standard. APPRAISAL AND SYNTHESIS METHODS Pooled sensitivities and specificities were computed for three apnea-hypopnea index (AHI) thresholds: 1 event/hour (e/h), 5 e/h, and 10 e/h. Random-effect models were assumed. Summary receiver-operating characteristics (SROC) analyses were also conducted. Heterogeneity (I 2 ) was evaluated, and publication bias was corrected (trim and fill). RESULTS Nineteen studies were finally retained, involving 4767 different pediatric sleep studies. Machine learning improved diagnostic performance as OSA severity criteria increased reaching optimal values for AHI = 10 e/h (0.652 sensitivity; 0.931 specificity; and 0.940 area under the SROC curve). Publication bias correction had minor effect on summary statistics, but high heterogeneity was observed among the studies.
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Affiliation(s)
- Gonzalo C Gutiérrez-Tobal
- Biomedical Engineering Group, Universidad de Valladolid, Valladolid, Spain.,Centro de Investigación Biomédica en Red en Bioingeniería, Biomateriales y Nanomedicina, (CIBER-BBN), Zaragoza, Spain
| | - Daniel Álvarez
- Biomedical Engineering Group, Universidad de Valladolid, Valladolid, Spain.,Centro de Investigación Biomédica en Red en Bioingeniería, Biomateriales y Nanomedicina, (CIBER-BBN), Zaragoza, Spain.,Department of Pneumology, Río Hortega University Hospital, Valladolid, Spain
| | - Leila Kheirandish-Gozal
- Department of Child Health, Child Health Research Institute, The University of Missouri School of Medicine, Columbia, Missouri, USA
| | - Félix Del Campo
- Biomedical Engineering Group, Universidad de Valladolid, Valladolid, Spain.,Centro de Investigación Biomédica en Red en Bioingeniería, Biomateriales y Nanomedicina, (CIBER-BBN), Zaragoza, Spain.,Department of Pneumology, Río Hortega University Hospital, Valladolid, Spain
| | - David Gozal
- Department of Child Health, Child Health Research Institute, The University of Missouri School of Medicine, Columbia, Missouri, USA
| | - Roberto Hornero
- Biomedical Engineering Group, Universidad de Valladolid, Valladolid, Spain.,Centro de Investigación Biomédica en Red en Bioingeniería, Biomateriales y Nanomedicina, (CIBER-BBN), Zaragoza, Spain.,Department of Pneumology, Río Hortega University Hospital, Valladolid, Spain
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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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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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A Novel Portable Real-Time Low-Cost Sleep Apnea Monitoring System based on the Global System for Mobile Communications (GSM) Network. Med Biol Eng Comput 2022; 60:619-632. [PMID: 35029814 PMCID: PMC8759063 DOI: 10.1007/s11517-021-02492-x] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2021] [Accepted: 12/17/2021] [Indexed: 11/16/2022]
Abstract
Background and objective Continuous monitoring of breathing activity plays a vital role in the detection of respiratory-based diseases (SA, COPD, etc.). Sleep Apnea (SA) is characterized by recurrent upper airway obstruction during sleep associated with arterial blood desaturation, sympathetic nervous system activation, and cardiovascular impairment. Untreated patients with SA have increased mortality rates compared to the general population. This study aims to design a remote monitoring system for sleep apnea to ensure patient safety and ease the workload of doctors in the Covid-19 era. Methods This study aims to design a remote monitoring system for sleep apnea to ensure patient safety and ease the workload of doctors. Our study focuses on a novel portable real-time low-cost sleep apnea monitoring system utilizing the GSM network (GSM Shield Sim900a). Proposed system is a remote monitoring and patient tracking system to detect the apnea event in real time, and to provide information of the sleep position, pulse, and respiratory and oxygen saturation to the medical specialists (SpO2) by establishing a direct contact. As soon as an abnormal condition is detected in the light of these parameters, the condition is reported (instant or in the form of short reports after sleep) to the patient relatives, the doctor’s mobile telephone or to the emergency medical centers (EMCs) through a GSM network to handle the case depending on the patient’s emergency condition. Results A study group was formed of six patients for monitoring apnea events (three males and three females) between the ages of 20 and 60. The patients in the study group have sleep apnea (SA) in different grades. All the apnea events were detected, and all the patients were successfully alerted. Also, the patient parameters were successfully sent to all patient relatives. Patients who could not get out of apnea were called through the CALL feature, and they were informed about their ongoing apnea event and told that intervention was necessary. The proposed system is tested on six patients. The beginning moment of apnea was successfully detected and the SMS/CALL feature was successfully activated without delay. During the testing, it has been observed that while some of the patients start breathing after the first SMS, some others needed the second or the third SMS. According to the measurement result, the maximum breathless time is 46 s among the patients, and a SMS is sent every 15 s. In addition, in cases where the patient was breathless for a long time, the CALL feature was actively sought from the relatives of the patient and enabled him to intervene. The proposed monitoring system could be used in both clinical and home settings. Conclusions The monitoring of a patient in real time allows to intervene in any unexpected circumstances about the patient. The proposed work uses an acceleration sensor as a reliable method of the sleep apnea for monitoring and prevention. The developed device is more economical, comfortable, and convenient than existing systems not only for the patients but also for the doctors. The patients can easily use this device in their home environment, so which could yield a more comfortable, easy to use, cost-effective, and long-term breathing monitoring system for healthcare applications. Graphical abstract ![]()
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Álvarez D, Gutiérrez-Tobal GC, Vaquerizo-Villar F, Moreno F, Del Campo F, Hornero R. Oximetry Indices in the Management of Sleep Apnea: From Overnight Minimum Saturation to the Novel Hypoxemia Measures. ADVANCES IN EXPERIMENTAL MEDICINE AND BIOLOGY 2022; 1384:219-239. [PMID: 36217087 DOI: 10.1007/978-3-031-06413-5_13] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
Obstructive sleep apnea (OSA) is a multidimensional disease often underdiagnosed due to the complexity and unavailability of its standard diagnostic method: the polysomnography. Among the alternative abbreviated tests searching for a compromise between simplicity and accurateness, oximetry is probably the most popular. The blood oxygen saturation (SpO2) signal is characterized by a near-constant profile in healthy subjects breathing normally, while marked drops (desaturations) are linked to respiratory events. Parameterization of the desaturations has led to a great number of indices of severity assessment commonly used to assist in OSA diagnosis. In this chapter, the main methodologies used to characterize the overnight oximetry profile are reviewed, from visual inspection and simple statistics to complex measures involving signal processing and pattern recognition techniques. We focus on the individual performance of each approach, but also on the complementarity among the great amount of indices existing in the state of the art, looking for the most relevant oximetric feature subset. Finally, a quick overview of SpO2-based deep learning applications for OSA management is carried out, where the raw oximetry signal is analyzed without previous parameterization. Our research allows us to conclude that all the methodologies (conventional, time, frequency, nonlinear, and hypoxemia-based) demonstrate high ability to provide relevant oximetric indices, but only a reduced set provide non-redundant complementary information leading to a significant performance increase. Finally, although oximetry is a robust tool, greater standardization and prospective validation of the measures derived from complex signal processing techniques are still needed to homogenize interpretation and increase generalizability.
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Affiliation(s)
- Daniel Álvarez
- Biomedical Engineering Group (GIB), University of Valladolid, Valladolid, Spain.
- Pneumology Department, Río Hortega University Hospital, Valladolid, Spain.
- Centro de Investigación Biomédica en Red de Bioingeniería, Biomateriales y Nanomedicina, Instituto de Salud Carlos III, Valladolid, Spain.
| | - Gonzalo C Gutiérrez-Tobal
- Biomedical Engineering Group (GIB), University of Valladolid, Valladolid, Spain
- Centro de Investigación Biomédica en Red de Bioingeniería, Biomateriales y Nanomedicina, Instituto de Salud Carlos III, Valladolid, Spain
| | - Fernando Vaquerizo-Villar
- Biomedical Engineering Group (GIB), University of Valladolid, Valladolid, Spain
- Centro de Investigación Biomédica en Red de Bioingeniería, Biomateriales y Nanomedicina, Instituto de Salud Carlos III, Valladolid, Spain
| | - Fernando Moreno
- Pneumology Department, Río Hortega University Hospital, Valladolid, Spain
| | - Félix Del Campo
- Biomedical Engineering Group (GIB), University of Valladolid, Valladolid, Spain
- Pneumology Department, Río Hortega University Hospital, Valladolid, Spain
- Centro de Investigación Biomédica en Red de Bioingeniería, Biomateriales y Nanomedicina, Instituto de Salud Carlos III, Valladolid, Spain
| | - Roberto Hornero
- Biomedical Engineering Group (GIB), University of Valladolid, Valladolid, Spain
- Centro de Investigación Biomédica en Red de Bioingeniería, Biomateriales y Nanomedicina, Instituto de Salud Carlos III, Valladolid, Spain
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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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Obstructive sleep apnea screening from unprocessed ECG signals using statistical modelling. Biomed Signal Process Control 2021. [DOI: 10.1016/j.bspc.2021.102685] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
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12
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Barroso-García V, Gutiérrez-Tobal GC, Gozal D, Vaquerizo-Villar F, Álvarez D, del Campo F, Kheirandish-Gozal L, Hornero R. Wavelet Analysis of Overnight Airflow to Detect Obstructive Sleep Apnea in Children. SENSORS 2021; 21:s21041491. [PMID: 33669996 PMCID: PMC7926995 DOI: 10.3390/s21041491] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/19/2021] [Revised: 02/16/2021] [Accepted: 02/18/2021] [Indexed: 01/08/2023]
Abstract
This study focused on the automatic analysis of the airflow signal (AF) to aid in the diagnosis of pediatric obstructive sleep apnea (OSA). Thus, our aims were: (i) to characterize the overnight AF characteristics using discrete wavelet transform (DWT) approach, (ii) to evaluate its diagnostic utility, and (iii) to assess its complementarity with the 3% oxygen desaturation index (ODI3). In order to reach these goals, we analyzed 946 overnight pediatric AF recordings in three stages: (i) DWT-derived feature extraction, (ii) feature selection, and (iii) pattern recognition. AF recordings from OSA patients showed both lower detail coefficients and decreased activity associated with the normal breathing band. Wavelet analysis also revealed that OSA disturbed the frequency and energy distribution of the AF signal, increasing its irregularity. Moreover, the information obtained from the wavelet analysis was complementary to ODI3. In this regard, the combination of both wavelet information and ODI3 achieved high diagnostic accuracy using the common OSA-positive cutoffs: 77.97%, 81.91%, and 90.99% (AdaBoost.M2), and 81.96%, 82.14%, and 90.69% (Bayesian multi-layer perceptron) for 1, 5, and 10 apneic events/hour, respectively. Hence, these findings suggest that DWT properly characterizes OSA-related severity as embedded in nocturnal AF, and could simplify the diagnosis of pediatric OSA.
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Affiliation(s)
- Verónica Barroso-García
- Biomedical Engineering Group, University of Valladolid, 47011 Valladolid, Spain; (V.B.-G.); (F.V.-V.); (D.Á.); (F.d.C.); (R.H.)
- 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; (V.B.-G.); (F.V.-V.); (D.Á.); (F.d.C.); (R.H.)
- Centro de Investigación Biomédica en Red en Bioingeniería, Biomateriales y Nanomedicina (CIBER-BBN), 47011 Valladolid, Spain
- Correspondence: ; Tel.: +34-983-423000 (ext. 4713)
| | - David Gozal
- Department of Child Health, The University of Missouri School of Medicine, Columbia, MO 65212, USA; (D.G.); (L.K.-G.)
| | - Fernando Vaquerizo-Villar
- Biomedical Engineering Group, University of Valladolid, 47011 Valladolid, Spain; (V.B.-G.); (F.V.-V.); (D.Á.); (F.d.C.); (R.H.)
- Centro de Investigación Biomédica en Red en Bioingeniería, Biomateriales y Nanomedicina (CIBER-BBN), 47011 Valladolid, Spain
| | - Daniel Álvarez
- Biomedical Engineering Group, University of Valladolid, 47011 Valladolid, Spain; (V.B.-G.); (F.V.-V.); (D.Á.); (F.d.C.); (R.H.)
- Centro de Investigación Biomédica en Red en Bioingeniería, Biomateriales y Nanomedicina (CIBER-BBN), 47011 Valladolid, Spain
- Sleep-Ventilation Unit, Pneumology Department, Río Hortega University Hospital, 47012 Valladolid, Spain
| | - Félix del Campo
- Biomedical Engineering Group, University of Valladolid, 47011 Valladolid, Spain; (V.B.-G.); (F.V.-V.); (D.Á.); (F.d.C.); (R.H.)
- Centro de Investigación Biomédica en Red en Bioingeniería, Biomateriales y Nanomedicina (CIBER-BBN), 47011 Valladolid, Spain
- Sleep-Ventilation Unit, Pneumology Department, Río Hortega University Hospital, 47012 Valladolid, Spain
| | - Leila Kheirandish-Gozal
- Department of Child Health, The University of Missouri School of Medicine, Columbia, MO 65212, USA; (D.G.); (L.K.-G.)
| | - Roberto Hornero
- Biomedical Engineering Group, University of Valladolid, 47011 Valladolid, Spain; (V.B.-G.); (F.V.-V.); (D.Á.); (F.d.C.); (R.H.)
- Centro de Investigación Biomédica en Red en Bioingeniería, Biomateriales y Nanomedicina (CIBER-BBN), 47011 Valladolid, Spain
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13
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Barroso-García V, Gutiérrez-Tobal GC, Kheirandish-Gozal L, Vaquerizo-Villar F, Álvarez D, Del Campo F, Gozal D, Hornero R. Bispectral analysis of overnight airflow to improve the pediatric sleep apnea diagnosis. Comput Biol Med 2020; 129:104167. [PMID: 33385706 DOI: 10.1016/j.compbiomed.2020.104167] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/08/2020] [Revised: 11/19/2020] [Accepted: 12/04/2020] [Indexed: 12/15/2022]
Abstract
Pediatric Obstructive Sleep Apnea (OSA) is a respiratory disease whose diagnosis is performed through overnight polysomnography (PSG). Since it is a complex, time-consuming, expensive, and labor-intensive test, simpler alternatives are being intensively sought. In this study, bispectral analysis of overnight airflow (AF) signal is proposed as a potential approach to replace PSG when indicated. Thus, our objective was to characterize AF through bispectrum, and assess its performance to diagnose pediatric OSA. This characterization was conducted using 13 bispectral features from 946 AF signals. The oxygen desaturation index ≥3% (ODI3), a common clinical measure of OSA severity, was also obtained to evaluate its complementarity to the AF bispectral analysis. The fast correlation-based filter (FCBF) and a multi-layer perceptron (MLP) were used for subsequent automatic feature selection and pattern recognition stages. FCBF selected 3 bispectral features and ODI3, which were used to train a MLP model with ability to estimate apnea-hypopnea index (AHI). The model reached 82.16%, 82.49%, and 90.15% accuracies for the common AHI cut-offs 1, 5, and 10 events/h, respectively. The different bispectral approaches used to characterize AF in children provided complementary information. Accordingly, bispectral analysis showed that the occurrence of apneic events decreases the non-gaussianity and non-linear interaction of the AF harmonic components, as well as the regularity of the respiratory patterns. Moreover, the bispectral information from AF also showed complementarity with ODI3. Our findings suggest that AF bispectral analysis may serve as a useful tool to simplify the diagnosis of pediatric OSA, particularly for children with moderate-to-severe OSA.
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Affiliation(s)
- Verónica Barroso-García
- Biomedical Engineering Group, University of Valladolid, Valladolid, Spain; CIBER-BBN, Centro de Investigación Biomédica en Red en Bioingeniería, Biomateriales y Nanomedicina, Valladolid, Spain.
| | - Gonzalo C Gutiérrez-Tobal
- Biomedical Engineering Group, University of Valladolid, Valladolid, Spain; CIBER-BBN, Centro de Investigación Biomédica en Red en Bioingeniería, Biomateriales y Nanomedicina, Valladolid, Spain
| | - Leila Kheirandish-Gozal
- Department of Child Health, The University of Missouri School of Medicine, Columbia, MO, USA
| | - Fernando Vaquerizo-Villar
- Biomedical Engineering Group, University of Valladolid, Valladolid, Spain; CIBER-BBN, Centro de Investigación Biomédica en Red en Bioingeniería, Biomateriales y Nanomedicina, Valladolid, Spain
| | - Daniel Álvarez
- Biomedical Engineering Group, University of Valladolid, Valladolid, Spain; CIBER-BBN, Centro de Investigación Biomédica en Red en Bioingeniería, Biomateriales y Nanomedicina, Valladolid, Spain; Sleep-Ventilation Unit, Pneumology Department, Río Hortega University Hospital, Valladolid, Spain
| | - Félix Del Campo
- Biomedical Engineering Group, University of Valladolid, Valladolid, Spain; CIBER-BBN, Centro de Investigación Biomédica en Red en Bioingeniería, Biomateriales y Nanomedicina, Valladolid, Spain; Sleep-Ventilation Unit, Pneumology Department, Río Hortega University Hospital, Valladolid, Spain
| | - David Gozal
- Department of Child Health, The University of Missouri School of Medicine, Columbia, MO, USA
| | - Roberto Hornero
- Biomedical Engineering Group, University of Valladolid, Valladolid, Spain; CIBER-BBN, Centro de Investigación Biomédica en Red en Bioingeniería, Biomateriales y Nanomedicina, Valladolid, Spain
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Li X, Ling SH, Su S. A Hybrid Feature Selection and Extraction Methods for Sleep Apnea Detection Using Bio-Signals. SENSORS 2020; 20:s20154323. [PMID: 32756353 PMCID: PMC7436101 DOI: 10.3390/s20154323] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/10/2020] [Revised: 07/06/2020] [Accepted: 07/31/2020] [Indexed: 11/16/2022]
Abstract
People with sleep apnea (SA) are at increased risk of having stroke and cardiovascular diseases. Polysomnography (PSG) is used to detect SA. This paper conducts feature selection from PSG signals and uses a support vector machine (SVM) to detect SA. To analyze SA, the Physionet Apnea Database was used to obtain various features. Electrocardiography (ECG), oxygen saturation (SaO2), airflow, abdominal, and thoracic signals were used to provide various frequency-, time-domain and non-linear features (n = 87). To analyse the significance of these features, firstly, two evaluation measures, the rank-sum method and the analysis of variance (ANOVA) were used to evaluate the significance of the features. These features were then classified according to their significance. Finally, different class feature sets were presented as inputs for an SVM classifier to detect the onset of SA. The hill-climbing feature selection algorithm and the k-fold cross-validation method were applied to evaluate each classification performance. Through the experiments, we discovered that the best feature set (including the top-five significant features) obtained the best classification performance. Furthermore, we plotted receiver operating characteristic (ROC) curves to examine the performance of the SVM, and the results showed the SVM with Linear kernel (regularization parameter = 1) outperformed other classifiers (area under curve = 95.23%, sensitivity = 94.29%, specificity = 96.17%). The results confirm that feature subsets based on multiple bio-signals have the potential to identify patients with SA. The use of a smaller subset avoids dimensionality problems and reduces the computational load.
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Assessment of Airflow and Oximetry Signals to Detect Pediatric Sleep Apnea-Hypopnea Syndrome Using AdaBoost. ENTROPY 2020; 22:e22060670. [PMID: 33286442 PMCID: PMC7517204 DOI: 10.3390/e22060670] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/30/2020] [Revised: 06/09/2020] [Accepted: 06/15/2020] [Indexed: 12/17/2022]
Abstract
The reference standard to diagnose pediatric Obstructive Sleep Apnea (OSA) syndrome is an overnight polysomnographic evaluation. When polysomnography is either unavailable or has limited availability, OSA screening may comprise the automatic analysis of a minimum number of signals. The primary objective of this study was to evaluate the complementarity of airflow (AF) and oximetry (SpO2) signals to automatically detect pediatric OSA. Additionally, a secondary goal was to assess the utility of a multiclass AdaBoost classifier to predict OSA severity in children. We extracted the same features from AF and SpO2 signals from 974 pediatric subjects. We also obtained the 3% Oxygen Desaturation Index (ODI) as a common clinically used variable. Then, feature selection was conducted using the Fast Correlation-Based Filter method and AdaBoost classifiers were evaluated. Models combining ODI 3% and AF features outperformed the diagnostic performance of each signal alone, reaching 0.39 Cohens's kappa in the four-class classification task. OSA vs. No OSA accuracies reached 81.28%, 82.05% and 90.26% in the apnea-hypopnea index cutoffs 1, 5 and 10 events/h, respectively. The most relevant information from SpO2 was redundant with ODI 3%, and AF was complementary to them. Thus, the joint analysis of AF and SpO2 enhanced the diagnostic performance of each signal alone using AdaBoost, thereby enabling a potential screening alternative for OSA in children.
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16
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Álvarez D, Cerezo-Hernández A, Crespo A, Gutiérrez-Tobal GC, Vaquerizo-Villar F, Barroso-García V, Moreno F, Arroyo CA, Ruiz T, Hornero R, Del Campo F. A machine learning-based test for adult sleep apnoea screening at home using oximetry and airflow. Sci Rep 2020; 10:5332. [PMID: 32210294 PMCID: PMC7093547 DOI: 10.1038/s41598-020-62223-4] [Citation(s) in RCA: 30] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2019] [Accepted: 03/09/2020] [Indexed: 02/05/2023] Open
Abstract
The most appropriate physiological signals to develop simplified as well as accurate screening tests for obstructive sleep apnoea (OSA) remain unknown. This study aimed at assessing whether joint analysis of at-home oximetry and airflow recordings by means of machine-learning algorithms leads to a significant diagnostic performance increase compared to single-channel approaches. Consecutive patients showing moderate-to-high clinical suspicion of OSA were involved. The apnoea-hypopnoea index (AHI) from unsupervised polysomnography was the gold standard. Oximetry and airflow from at-home polysomnography were parameterised by means of 38 time, frequency, and non-linear variables. Complementarity between both signals was exhaustively inspected via automated feature selection. Regression support vector machines were used to estimate the AHI from single-channel and dual-channel approaches. A total of 239 patients successfully completed at-home polysomnography. The optimum joint model reached 0.93 (95%CI 0.90–0.95) intra-class correlation coefficient between estimated and actual AHI. Overall performance of the dual-channel approach (kappa: 0.71; 4-class accuracy: 81.3%) significantly outperformed individual oximetry (kappa: 0.61; 4-class accuracy: 75.0%) and airflow (kappa: 0.42; 4-class accuracy: 61.5%). According to our findings, oximetry alone was able to reach notably high accuracy, particularly to confirm severe cases of the disease. Nevertheless, oximetry and airflow showed high complementarity leading to a remarkable performance increase compared to single-channel approaches. Consequently, their joint analysis via machine learning enables accurate abbreviated screening of OSA at home.
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Affiliation(s)
- Daniel Álvarez
- Pneumology Department, Río Hortega University Hospital, Valladolid, Spain. .,Biomedical Engineering Group, University of Valladolid, Valladolid, Spain. .,Centro de Investigación Biomédica en Red en Bioingeniería, Biomateriales y Nanomedicina (CIBER-BBN), Valladolid, Spain.
| | | | - Andrea Crespo
- Pneumology Department, Río Hortega University Hospital, Valladolid, Spain.,Biomedical Engineering Group, University of Valladolid, 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
| | | | | | - Fernando Moreno
- Pneumology Department, Río Hortega University Hospital, Valladolid, Spain
| | - C Ainhoa Arroyo
- Pneumology Department, Río Hortega University Hospital, Valladolid, Spain
| | - Tomás Ruiz
- Pneumology Department, Río Hortega University Hospital, 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
| | - Félix Del Campo
- Pneumology Department, Río Hortega University Hospital, Valladolid, Spain.,Biomedical Engineering Group, University of Valladolid, Valladolid, Spain.,Centro de Investigación Biomédica en Red en Bioingeniería, Biomateriales y Nanomedicina (CIBER-BBN), Valladolid, Spain
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Yüzer A, Sümbül H, Polat K. A Novel Wearable Real-Time Sleep Apnea Detection System Based on the Acceleration Sensor. Ing Rech Biomed 2020. [DOI: 10.1016/j.irbm.2019.10.007] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/23/2023]
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18
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Barroso-Garcia V, Gutierrez-Tobal GC, Kheirandish-Gozal L, Alvarez D, Vaquerizo-Villar F, Del Campo F, Gozal D, Hornero R. Usefulness of Spectral Analysis of Respiratory Rate Variability to Help in Pediatric Sleep Apnea-Hypopnea Syndrome Diagnosis. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2020; 2019:4580-4583. [PMID: 31946884 DOI: 10.1109/embc.2019.8857719] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
The sleep apnea-hypopnea syndrome (SAHS) is a chronic respiratory disorder of high prevalence among children (up to 4%). Nocturnal polysomnography (PSG) is the gold standard method to diagnose SAHS, which is a complex, expensive, and time-consuming test. Consequently, alternative simplified methods are demanded. We propose the analysis of the respiratory rate variability (RRV) signal, directly obtained from the airflow (AF) signals. The aim of our study is to evaluate the usefulness of the spectral information obtained from RRV in the diagnosis of pediatric SAHS. A database composed of 946 AF and blood oxygen saturation (SpO2) recordings from children between 0 and 13 years old was used. Our database was divided into four severity groups according to the apnea-hipopnea index (AHI): no-SAHS (AHI <; 1 events/h), mild (1 events/h ≤ AHI <; 5 events/h), moderate (5 events/h ≤ AHI <; 10 events/h), and severe SAHS (AHI ≥ 10 events/h). RRV and 3% oxygen desaturation index (ODI3) were obtained from AF and SpO2 recordings, respectively. A spectral band of interest was determined (0.09-0.20 Hz.) and a total of 12 spectral features were extracted. Nine of these features showed statistically significant differences (p-value <; 0.05) among the four severity groups. The spectral features from RRV along with ODI3 were used as inputs to binary logistic regression (LR) classifiers. The diagnostic performance of LR models were evaluated for the AHI cut-off points of 1, 5, and 10 e/h, achieving 66.5%, 84.0%, and 88.5% accuracy, respectively. These results outperformed those obtained by single ODI3. The joint use of the spectral information from RRV and ODI3 achieved a high diagnostic capability in the most severely-affected children, thus showing their complementarity. These results suggest that the information contained in RRV spectrum together with ODI3 is useful to help identify moderate-to-severe SAHS.
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Barroso-García V, Gutiérrez-Tobal GC, Kheirandish-Gozal L, Álvarez D, Vaquerizo-Villar F, Núñez P, Del Campo F, Gozal D, Hornero R. Usefulness of recurrence plots from airflow recordings to aid in paediatric sleep apnoea diagnosis. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2020; 183:105083. [PMID: 31590097 DOI: 10.1016/j.cmpb.2019.105083] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/26/2019] [Revised: 08/28/2019] [Accepted: 09/16/2019] [Indexed: 06/10/2023]
Abstract
BACKGROUND AND OBJECTIVE In-laboratory overnight polysomnography (PSG) is the gold standard method to diagnose the Sleep Apnoea-Hypopnoea Syndrome (SAHS). PSG is a complex, expensive, labour-intensive and time-consuming test. Consequently, simplified diagnostic methods are desirable. We propose the analysis of the airflow (AF) signal by means of recurrence plots (RP) features. The main goal of our study was to evaluate the utility of the information from RPs of the AF signals to detect paediatric SAHS at different levels of severity. In addition, we also evaluated the complementarity with the 3% oxygen desaturation index (ODI3). METHODS 946 AF and blood oxygen saturation (SpO2) recordings from children ages 0-13 years were used. The population under study was randomly split into training (60%) and test (40%) sets. RP was computed and 9 RP features were extracted from each AF recording. ODI3 was also calculated from each SpO2 recording. A feature selection stage was conducted in the training group by means of the fast correlation-based filter (FCBF) methodology to obtain a relevant and non-redundant optimum feature subset. A multi-layer perceptron neural network with Bayesian approach (BY-MLP), trained with these optimum features, was used to estimate the apnoea-hypopnoea index (AHI). RESULTS 8 of the RP features showed statistically significant differences (p-value <0.01) among the SAHS severity groups. FCBF selected the maximum length of the diagonal lines from RP, as well as the ODI3. Using these optimum features, the BY-MLP model achieved 83.2%, 78.5%, and 91.0% accuracy in the test group for the AHI thresholds 1, 5, and 10 events/h, respectively. Moreover, this model reached a negative likelihood ratio of 0.1 for 1 event/h and a positive likelihood ratio of 13.7 for 10 events/h. CONCLUSIONS RP analysis enables extraction of useful SAHS-related information from overnight AF paediatric recordings. Moreover, it provides complementary information to the widely-used clinical variable ODI3. Thus, RP applied to AF signals can be used along with ODI3 to help in paediatric SAHS diagnosis, particularly to either confirm the absence of SAHS or the presence of severe SAHS.
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Affiliation(s)
| | | | - Leila Kheirandish-Gozal
- Department of Child Health, The University of Missouri School of Medicine, Columbia, MO, USA
| | - Daniel Álvarez
- Biomedical Engineering Group, Universidad de Valladolid, Valladolid, Spain; Sleep-Ventilation Unit, Pneumology Service, Río Hortega University Hospital, Valladolid, Spain
| | | | - Pablo Núñez
- Biomedical Engineering Group, Universidad de Valladolid, Valladolid, Spain
| | - Félix Del Campo
- Biomedical Engineering Group, Universidad de Valladolid, Valladolid, Spain; Sleep-Ventilation Unit, Pneumology Service, Río Hortega University Hospital, Valladolid, Spain
| | - David Gozal
- Department of Child Health, The University of Missouri School of Medicine, Columbia, MO, USA
| | - Roberto Hornero
- Biomedical Engineering Group, Universidad de Valladolid, Valladolid, Spain; IMUVA, Instituto de Investigación en Matemáticas, Universidad de Valladolid, Valladolid, Spain. http://www.gib.tel.uva.es
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20
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Li X, Al-Ani A, Ling SH. Feature Selection for the Detection of Sleep Apnea using Multi-Bio Signals from Overnight Polysomnography. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2018; 2018:1444-1447. [PMID: 30440664 DOI: 10.1109/embc.2018.8512585] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Abstract
Patients with sleep apnea (SA) are at increased risk of stroke and cardiovascular disease. Diagnosis of sleep apnea depends on the standard overnight polysomnography (PSG). In this study, the DREAM Apnea Database was used to evaluate the importance of the various features proposed in the literature for the analysis of sleep apnea. Various timeand frequency- domain features that include wavelet and power spectral density were extracted from ECG, EMG, EEG, airflow, SaO2, abdominal and thoracic recordings. Evaluation measures of one-way analysis of variance (ANOVA) and Rank-Sum test were used to test the performance of different features. The selected feature subset indicated that frequency-domain features outperform time-domain ones. This study will help in enhancing the detection accuracy of sleep apnea for the various polysomnography signals.
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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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Del Campo F, Crespo A, Cerezo-Hernández A, Gutiérrez-Tobal GC, Hornero R, Álvarez D. Oximetry use in obstructive sleep apnea. Expert Rev Respir Med 2018; 12:665-681. [PMID: 29972344 DOI: 10.1080/17476348.2018.1495563] [Citation(s) in RCA: 31] [Impact Index Per Article: 5.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/28/2022]
Abstract
INTRODUCTION Overnight oximetry has been proposed as an accessible, simple, and reliable technique for obstructive sleep apnea syndrome (OSAS) diagnosis. From visual inspection to advanced signal processing, several studies have demonstrated the usefulness of oximetry as a screening tool. However, there is still controversy regarding the general application of oximetry as a single screening methodology for OSAS. Areas covered: Currently, high-resolution portable devices combined with pattern recognition-based applications are able to achieve high performance in the detection of this disease. In this review, recent studies involving automated analysis of oximetry by means of advanced signal processing and machine learning algorithms are analyzed. Advantages and limitations are highlighted and novel research lines aimed at improving the screening ability of oximetry are proposed. Expert commentary: Oximetry is a cost-effective tool for OSAS screening in patients showing high pretest probability for the disease. Nevertheless, exhaustive analyses are still needed to further assess unattended oximetry monitoring as a single diagnostic test for sleep apnea, particularly in the pediatric population and in populations with significant comorbidities. In the following years, communication technologies and big data analyses will overcome current limitations of simplified sleep testing approaches, changing the detection and management of OSAS.
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Affiliation(s)
- Félix Del Campo
- a Pneumology Service , Río Hortega University Hospital , Valladolid , Spain.,b Biomedical Engineering Group , University of Valladolid , Valladolid , Spain
| | - Andrea Crespo
- a Pneumology Service , Río Hortega University Hospital , Valladolid , Spain.,b Biomedical Engineering Group , University of Valladolid , Valladolid , Spain
| | | | | | - Roberto Hornero
- b Biomedical Engineering Group , University of Valladolid , Valladolid , Spain
| | - Daniel Álvarez
- a Pneumology Service , Río Hortega University Hospital , Valladolid , Spain.,b Biomedical Engineering Group , University of Valladolid , Valladolid , Spain
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Vaquerizo-Villar F, Álvarez D, Kheirandish-Gozal L, Gutiérrez-Tobal GC, Barroso-García V, Crespo A, Del Campo F, Gozal D, Hornero R. Utility of bispectrum in the screening of pediatric sleep apnea-hypopnea syndrome using oximetry recordings. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2018; 156:141-149. [PMID: 29428066 DOI: 10.1016/j.cmpb.2017.12.020] [Citation(s) in RCA: 23] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/24/2017] [Revised: 12/11/2017] [Accepted: 12/21/2017] [Indexed: 06/08/2023]
Abstract
BACKGROUND AND OBJECTIVE The aim of this study was to assess the utility of bispectrum-based oximetry approaches as a complementary tool to traditional techniques in the screening of pediatric sleep apnea-hypopnea syndrome (SAHS). METHODS 298 blood oxygen saturation (SpO2) signals from children ranging 0-13 years of age were recorded during overnight polysomnography (PSG). These recordings were divided into three severity groups according to the PSG-derived apnea hypopnea index (AHI): AHI < 5 events per hour (e/h), 5 ≤ AHI < 10 e/h, AHI ≥ 10 e/h. For each pediatric subject, anthropometric variables, 3% oxygen desaturation index (ODI3) and spectral features from power spectral density (PSD) and bispectrum were obtained. Then, the fast correlation-based filter (FCBF) was applied to select a subset of relevant features that may be complementary, excluding those that are redundant. The selected features fed a multiclass multi-layer perceptron (MLP) neural network to build a model to estimate the SAHS severity degrees. RESULTS An optimum subset with features from all the proposed methodological approaches was obtained: variables from bispectrum, as well as PSD, ODI3, Age, and Sex. In the 3-class classification task, the MLP model trained with these features achieved an accuracy of 76.0% and a Cohen's kappa of 0.56 in an independent test set. Additionally, high accuracies were reached using the AHI cutoffs for diagnosis of moderate (AHI = 5 e/h) and severe (AHI = 10 e/h) SAHS: 81.3% and 85.3%, respectively. These results outperformed the diagnostic ability of a MLP model built without using bispectral features. CONCLUSIONS Our results suggest that bispectrum provides additional information to anthropometric variables, ODI3 and PSD regarding characterization of changes in the SpO2 signal caused by respiratory events. Thus, oximetry bispectrum can be a useful tool to provide complementary information for screening of moderate-to-severe pediatric SAHS.
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Affiliation(s)
| | - Daniel Álvarez
- Biomedical Engineering Group, Universidad de Valladolid, Valladolid, Spain; Servicio de Neumología, Hospital Universitario Rio Hortega, Valladolid, Spain
| | - Leila Kheirandish-Gozal
- Dept. of Pediatrics, Pritzker School of Medicine, Biological Sciences Division, The University of Chicago, Chicago, United States of America
| | | | | | - Andrea Crespo
- Biomedical Engineering Group, Universidad de Valladolid, Valladolid, Spain; Servicio de Neumología, Hospital Universitario Rio Hortega, Valladolid, Spain
| | - Félix Del Campo
- Biomedical Engineering Group, Universidad de Valladolid, Valladolid, Spain; Servicio de Neumología, Hospital Universitario Rio Hortega, Valladolid, Spain
| | - David Gozal
- Dept. of Pediatrics, Pritzker School of Medicine, Biological Sciences Division, The University of Chicago, Chicago, United States of America
| | - Roberto Hornero
- Biomedical Engineering Group, Universidad de Valladolid, Valladolid, Spain; IMUVA, Instituto de Investigación en Matemáticas, Universidad de Valladolid, Valladolid, Spain
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Assessment of oximetry-based statistical classifiers as simplified screening tools in the management of childhood obstructive sleep apnea. Sleep Breath 2018; 22:1063-1073. [PMID: 29453636 DOI: 10.1007/s11325-018-1637-3] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2017] [Revised: 01/12/2018] [Accepted: 01/28/2018] [Indexed: 10/18/2022]
Abstract
PURPOSE A variety of statistical models based on overnight oximetry has been proposed to simplify the detection of children with suspected obstructive sleep apnea syndrome (OSAS). Despite the usefulness reported, additional thorough comparative analyses are required. This study was aimed at assessing common binary classification models from oximetry for the detection of childhood OSAS. METHODS Overnight oximetry recordings from 176 children referred for clinical suspicion of OSAS were acquired during in-lab polysomnography. Several training and test datasets were randomly composed by means of bootstrapping for model optimization and independent validation. For every child, blood oxygen saturation (SpO2) was parameterized by means of 17 features. Fast correlation-based filter (FCBF) was applied to search for the optimum features. The discriminatory power of three statistical pattern recognition algorithms was assessed: linear discriminant analysis (LDA), quadratic discriminant analysis (QDA), and logistic regression (LR). The performance of each automated model was evaluated for the three common diagnostic polysomnographic cutoffs in pediatric OSAS: 1, 3, and 5 events/h. RESULTS Best screening performances emerged using the 1 event/h cutoff for mild-to-severe childhood OSAS. LR achieved 84.3% accuracy (95% CI 76.8-91.5%) and 0.89 AUC (95% CI 0.83-0.94), while QDA reached 96.5% PPV (95% CI 90.3-100%) and 0.91 AUC (95% CI 0.85-0.96%). Moreover, LR and QDA reached diagnostic accuracies of 82.7% (95% CI 75.0-89.6%) and 82.1% (95% CI 73.8-89.5%) for a cutoff of 5 events/h, respectively. CONCLUSIONS Automated analysis of overnight oximetry may be used to develop reliable as well as accurate screening tools for childhood OSAS.
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Irregularity and Variability Analysis of Airflow Recordings to Facilitate the Diagnosis of Paediatric Sleep Apnoea-Hypopnoea Syndrome. ENTROPY 2017. [DOI: 10.3390/e19090447] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
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Vaquerizo-Villar F, Alvarez D, Gutierrez-Tobal GC, Barroso-Garcia V, Kheirandish-Gozal L, Crespo A, Del Campo F, Gozal D, Hornero R. Usefulness of discrete wavelet transform in the analysis of oximetry signals to assist in childhood sleep apnea-hypopnea syndrome diagnosis. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2017; 2017:3753-3756. [PMID: 29060714 DOI: 10.1109/embc.2017.8037673] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/07/2023]
Abstract
Sleep apnea hypopnea syndrome (SAHS) is a highly prevalent respiratory disorder that may cause many negative consequences for the health and development of children. The gold standard for diagnosis is the overnight polysomnography (PSG), which is a high cost, complex, intrusive, and time-demanding technique. To improve the early detection of pediatric SAHS, we propose an automated analysis of the SpO2 signal from nocturnal oximetry. A database composed of 298 SpO2 recordings from children ranging from 0 to 13 years old was used for this purpose. Due to the abrupt changes caused by respiratory events in the SpO2 signal, our goal was to evaluate the diagnostic ability of this by means of the discrete wavelet transform (DWT). To achieve this objective, we conducted a signal processing approach divided into two main stages: (i) feature extraction, where features from the DWT detail coefficients were computed, and (ii) feature classification, where a logistic regression (LR) model was used to classify children into SAHS negative or SAHS positive. Our results showed that respiratory events introduced more variability in two detail levels of the DWT from SpO2: 0.024-0.049 Hz and 0.012-0.024 Hz. Moreover, the LR classifier achieved an 81.9% accuracy (79.1% sensitivity and 84.1% specificity) in an independent test set for a clinical cutoff point of 5 events/h, as derived from PSG. These results suggest that DWT analysis may be a useful tool to analyze SpO2 recordings in the context of childhood SAHS.
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Multiscale Entropy Analysis of Unattended Oximetric Recordings to Assist in the Screening of Paediatric Sleep Apnoea at Home. ENTROPY 2017. [DOI: 10.3390/e19060284] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/01/2023]
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Gutierrez-Tobal GC, Kheirandish-Gozal L, Alvarez D, Crespo A, Philby MF, Mohammadi M, Del Campo F, Gozal D, Hornero R. Analysis and classification of oximetry recordings to predict obstructive sleep apnea severity in children. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2015; 2015:4540-4543. [PMID: 26737304 DOI: 10.1109/embc.2015.7319404] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/05/2023]
Abstract
Current study is focused around the potential use of oximetry to determine the obstructive sleep apnea-hypopnea syndrome (OSAHS) severity in children. Single-channel SpO2 recordings from 176 children were divided into three severity groups according to the apnea-hypopnea index (AHI): AHI<;1 events per hour (e/h), 1≤AHI<;5 e/h, and AHI ≥5 e/h. Spectral analysis was conducted to define and characterize a frequency band of interest in SpO2. Then we combined the spectral data with the 3% oxygen desaturation index (ODI3) by means of a multi-layer perceptron (MLP) neural network, in order to classify children into one of the three OSAHS severity groups. Following our MLP multiclass approach, a diagnostic protocol with capability to reduce the need of polysomnography tests by 46% could be derived. Moreover, our proposal can be also evaluated, in a binary classification task for two common AHI diagnostic cutoffs (AHI = 1 e/h and AHI= 5 e/h). High diagnostic ability was reached in both cases (84.7% and 85.8% accuracy, respectively) outperforming the clinical variable ODI3 as well as other measures reported in recent studies. These results suggest that the information contained in SpO2 could be helpful in pediatric OSAHS severity detection.
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