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Yang L, Ding Z, Zhou J, Zhang S, Wang Q, Zheng K, Wang X, Chen L. Algorithmic detection of sleep-disordered breathing using respiratory signals: a systematic review. Physiol Meas 2024; 45:03TR02. [PMID: 38387048 DOI: 10.1088/1361-6579/ad2c13] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/10/2023] [Accepted: 02/22/2024] [Indexed: 02/24/2024]
Abstract
Background and Objective. Sleep-disordered breathing (SDB) poses health risks linked to hypertension, cardiovascular disease, and diabetes. However, the time-consuming and costly standard diagnostic method, polysomnography (PSG), limits its wide adoption and leads to underdiagnosis. To tackle this, cost-effective algorithms using single-lead signals (like respiratory, blood oxygen, and electrocardiogram) have emerged. Despite respiratory signals being preferred for SDB assessment, a lack of comprehensive reviews addressing their algorithmic scope and performance persists. This paper systematically reviews 2012-2022 literature, covering signal sources, processing, feature extraction, classification, and application, aiming to bridge this gap and provide future research references.Methods. This systematic review followed the registered PROSPERO protocol (CRD42022385130), initially screening 342 papers, with 32 studies meeting data extraction criteria.Results. Respiratory signal sources include nasal airflow (NAF), oronasal airflow (OAF), and respiratory movement-related signals such as thoracic respiratory effort (TRE) and abdominal respiratory effort (ARE). Classification techniques include threshold rule-based methods (8), machine learning models (13), and deep learning models (11). The NAF-based algorithm achieved the highest average accuracy at 94.11%, surpassing 78.19% for other signals. Hypopnea detection sensitivity with single-source respiratory signals remained modest, peaking at 73.34%. The TRE and ARE signals proved to be reliable in identifying different types of SDB because distinct respiratory disorders exhibited different patterns of chest and abdominal motion.Conclusions. Multiple detection algorithms have been widely applied for SDB detection, and their accuracy is closely related to factors such as signal source, signal processing, feature selection, and model selection.
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Affiliation(s)
- Liqing Yang
- Key Laboratory of Biorheological Science and Technology of Ministry of Education, Chongqing University, Chongqing, People's Republic of China
- Chongqing Medical Electronics Engineering Technology Research Center, Chongqing University, Chongqing, People's Republic of China
| | - Zhimei Ding
- Key Laboratory of Biorheological Science and Technology of Ministry of Education, Chongqing University, Chongqing, People's Republic of China
- Chongqing Medical Electronics Engineering Technology Research Center, Chongqing University, Chongqing, People's Republic of China
| | - Jiangjie Zhou
- Chongqing Medical Electronics Engineering Technology Research Center, Chongqing University, Chongqing, People's Republic of China
- Chongqing Key Laboratory of Artificial Intelligence and Service Robot Control Technology, Chongqing, People's Republic of China
| | - Siyuan Zhang
- Key Laboratory of Biorheological Science and Technology of Ministry of Education, Chongqing University, Chongqing, People's Republic of China
- Chongqing Medical Electronics Engineering Technology Research Center, Chongqing University, Chongqing, People's Republic of China
| | - Qi Wang
- Chongqing Medical Electronics Engineering Technology Research Center, Chongqing University, Chongqing, People's Republic of China
- Chongqing Key Laboratory of Artificial Intelligence and Service Robot Control Technology, Chongqing, People's Republic of China
| | - Kaige Zheng
- Chongqing Medical Electronics Engineering Technology Research Center, Chongqing University, Chongqing, People's Republic of China
- Chongqing Key Laboratory of Artificial Intelligence and Service Robot Control Technology, Chongqing, People's Republic of China
| | - Xing Wang
- Key Laboratory of Biorheological Science and Technology of Ministry of Education, Chongqing University, Chongqing, People's Republic of China
- Chongqing Key Laboratory of Artificial Intelligence and Service Robot Control Technology, Chongqing, People's Republic of China
| | - Lin Chen
- Key Laboratory of Biorheological Science and Technology of Ministry of Education, Chongqing University, Chongqing, People's Republic of China
- Chongqing Key Laboratory of Artificial Intelligence and Service Robot Control Technology, Chongqing, People's Republic of China
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Zhou G, Zhao W, Zhang Y, Zhou W, Yan H, Wei Y, Tang Y, Zeng Z, Cheng H. Comparison of OPPO Watch Sleep Analyzer and Polysomnography for Obstructive Sleep Apnea Screening. Nat Sci Sleep 2024; 16:125-141. [PMID: 38348055 PMCID: PMC10860396 DOI: 10.2147/nss.s438065] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/31/2023] [Accepted: 01/24/2024] [Indexed: 02/15/2024] Open
Abstract
Objective To evaluate the clinical performance of the OPPO Watch (OW) Sleep Analyzer (OWSA) on OSA screening with polysomnography reference. Methods We recruited 350 participants using OWSA and PSG simultaneously in a sleep laboratory. The respiratory event index (REI) derived from OWSA and the apnea-hypopnea index (AHI) provided by PSG were compared. SHapley Additive exPlanation (SHAP) values were calculated to explain the model of OWSA. Results The OWSA-REI (26.5±18.5 events/h) correlated well with PSG-AHI (33.2±25.7 events/h; r = 0.91, p < 0.001), with an intraclass correlation coefficient (ICC) of 0.83. Using a threshold of AHI ≥15 events/h, the sensitivity, specificity, accuracy, and area under the curve (AUC) were 86.1%, 86.7%, 86.3%, and 0.94, respectively. Bland-Altman analysis showed that OWSA-REI and PSG-AHI were in good agreement (Mean Difference: -6.7, 95% CI:16.0 to -29.3 events/h). In addition, the effectiveness of the models in OWSA were also explained by visualizing SHAP values. Conclusion The OWSA demonstrated a reasonable performance for OSA screening in the clinical setting. In light of this, it is possible for smartwatches to become a complementary tool to PSG, which is particularly useful for larger-scale preliminary screenings.
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Affiliation(s)
- Guangxin Zhou
- Department of Sleep Medicine, Institute of Respiratory Diseases, Shenzhen People’s Hospital, The Second Clinical Medical College of Jinan University, The First Affiliated Hospital of Southern University of Science and Technology, Shenzhen, Guangdong, People’s Republic of China
- OPPO Health, Guangdong OPPO Mobile Telecommunications Co. Ltd., Shenzhen, Guangdong, People’s Republic of China
| | - Wei Zhao
- OPPO Health, Guangdong OPPO Mobile Telecommunications Co. Ltd., Shenzhen, Guangdong, People’s Republic of China
| | - Yi Zhang
- OPPO Health, Guangdong OPPO Mobile Telecommunications Co. Ltd., Shenzhen, Guangdong, People’s Republic of China
| | - Wenli Zhou
- OPPO Health, Guangdong OPPO Mobile Telecommunications Co. Ltd., Shenzhen, Guangdong, People’s Republic of China
| | - Haizhou Yan
- OPPO Health, Guangdong OPPO Mobile Telecommunications Co. Ltd., Shenzhen, Guangdong, People’s Republic of China
| | - Yongli Wei
- Department of Sleep Medicine, Institute of Respiratory Diseases, Shenzhen People’s Hospital, The Second Clinical Medical College of Jinan University, The First Affiliated Hospital of Southern University of Science and Technology, Shenzhen, Guangdong, People’s Republic of China
| | - Yuming Tang
- Department of Sleep Medicine, Institute of Respiratory Diseases, Shenzhen People’s Hospital, The Second Clinical Medical College of Jinan University, The First Affiliated Hospital of Southern University of Science and Technology, Shenzhen, Guangdong, People’s Republic of China
| | - Zijing Zeng
- OPPO Health, Guangdong OPPO Mobile Telecommunications Co. Ltd., Shenzhen, Guangdong, People’s Republic of China
| | - Hanrong Cheng
- Department of Sleep Medicine, Institute of Respiratory Diseases, Shenzhen People’s Hospital, The Second Clinical Medical College of Jinan University, The First Affiliated Hospital of Southern University of Science and Technology, Shenzhen, Guangdong, People’s Republic of China
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Martín-Montero A, Armañac-Julián P, Gil E, Kheirandish-Gozal L, Álvarez D, Lázaro J, Bailón R, Gozal D, Laguna P, Hornero R, Gutiérrez-Tobal GC. Pediatric sleep apnea: Characterization of apneic events and sleep stages using heart rate variability. Comput Biol Med 2023; 154:106549. [PMID: 36706566 DOI: 10.1016/j.compbiomed.2023.106549] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/19/2022] [Revised: 12/19/2022] [Accepted: 01/11/2023] [Indexed: 01/16/2023]
Abstract
Heart rate variability (HRV) is modulated by sleep stages and apneic events. Previous studies in children compared classical HRV parameters during sleep stages between obstructive sleep apnea (OSA) and controls. However, HRV-based characterization incorporating both sleep stages and apneic events has not been conducted. Furthermore, recently proposed novel HRV OSA-specific parameters have not been evaluated. Therefore, the aim of this study was to characterize and compare classic and pediatric OSA-specific HRV parameters while including both sleep stages and apneic events. A total of 1610 electrocardiograms from the Childhood Adenotonsillectomy Trial (CHAT) database were split into 10-min segments to extract HRV parameters. Segments were characterized and grouped by sleep stage (wake, W; non-rapid eye movement, NREMS; and REMS) and presence of apneic events (under 1 apneic event per segment, e/s; 1-5 e/s; 5-10 e/s; and over 10 e/s). NREMS showed significant changes in HRV parameters as apneic event frequency increased, which were less marked in REMS. In both NREMS and REMS, power in BW2, a pediatric OSA-specific frequency domain, allowed for the optimal differentiation among segments. Moreover, in the absence of apneic events, another defined band, BWRes, resulted in best differentiation between sleep stages. The clinical usefulness of segment-based HRV characterization was then confirmed by two ensemble-learning models aimed at estimating apnea-hypopnea index and classifying sleep stages, respectively. We surmise that basal sympathetic activity during REMS may mask apneic events-induced sympathetic excitation, thus highlighting the importance of incorporating sleep stages as well as apneic events when evaluating HRV in pediatric OSA.
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Affiliation(s)
- Adrián Martín-Montero
- Biomedical Engineering Group, University of Valladolid, Valladolid, Spain; CIBER de Bioingeniería, Biomateriales y Nanomedicina, Instituto de Salud Carlos III, Valladolid, Spain.
| | - Pablo Armañac-Julián
- CIBER de Bioingeniería, Biomateriales y Nanomedicina, Instituto de Salud Carlos III, Valladolid, Spain; Biomedical Signal Interpretation and Computational Simulation (BSICoS) Group, Institute of Engineering Research (I3A), IIS Aragón, University of Zaragoza, Zaragoza, Spain
| | - Eduardo Gil
- CIBER de Bioingeniería, Biomateriales y Nanomedicina, Instituto de Salud Carlos III, Valladolid, Spain; Biomedical Signal Interpretation and Computational Simulation (BSICoS) Group, Institute of Engineering Research (I3A), IIS Aragón, University of Zaragoza, Zaragoza, Spain
| | - Leila Kheirandish-Gozal
- Department of Child Health, The University of Missouri School of Medicine, Columbia, MO, USA
| | - Daniel Álvarez
- Biomedical Engineering Group, University of Valladolid, Valladolid, Spain; CIBER de Bioingeniería, Biomateriales y Nanomedicina, Instituto de Salud Carlos III, Valladolid, Spain
| | - Jesús Lázaro
- CIBER de Bioingeniería, Biomateriales y Nanomedicina, Instituto de Salud Carlos III, Valladolid, Spain; Biomedical Signal Interpretation and Computational Simulation (BSICoS) Group, Institute of Engineering Research (I3A), IIS Aragón, University of Zaragoza, Zaragoza, Spain
| | - Raquel Bailón
- CIBER de Bioingeniería, Biomateriales y Nanomedicina, Instituto de Salud Carlos III, Valladolid, Spain; Biomedical Signal Interpretation and Computational Simulation (BSICoS) Group, Institute of Engineering Research (I3A), IIS Aragón, University of Zaragoza, Zaragoza, Spain
| | - David Gozal
- Department of Child Health, The University of Missouri School of Medicine, Columbia, MO, USA
| | - Pablo Laguna
- CIBER de Bioingeniería, Biomateriales y Nanomedicina, Instituto de Salud Carlos III, Valladolid, Spain; Biomedical Signal Interpretation and Computational Simulation (BSICoS) Group, Institute of Engineering Research (I3A), IIS Aragón, University of Zaragoza, Zaragoza, Spain
| | - Roberto Hornero
- Biomedical Engineering Group, University of Valladolid, Valladolid, Spain; CIBER de Bioingeniería, Biomateriales y Nanomedicina, Instituto de Salud Carlos III, Valladolid, Spain
| | - Gonzalo C Gutiérrez-Tobal
- Biomedical Engineering Group, University of Valladolid, Valladolid, Spain; CIBER de Bioingeniería, Biomateriales y Nanomedicina, Instituto de Salud Carlos III, Valladolid, Spain
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MCFN: A Multichannel Fusion Network for Sleep Apnea Syndrome Detection. JOURNAL OF HEALTHCARE ENGINEERING 2023; 2023:5287043. [PMID: 36726772 PMCID: PMC9886480 DOI: 10.1155/2023/5287043] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 05/07/2022] [Revised: 06/24/2022] [Accepted: 11/24/2022] [Indexed: 01/25/2023]
Abstract
Sleep apnea syndrome (SAS) is the most common sleep disorder which affects human life and health. Many researchers use deep learning methods to automatically learn the features of physiological signals. However, these methods ignore the different effects of multichannel features from various physiological signals. To solve this problem, we propose a multichannel fusion network (MCFN), which learns the multilevel features through a convolution neural network on different respiratory signals and then reconstructs the relationship between feature channels with an attention mechanism. MCFN effectively fuses the multichannel features to improve the SAS detection performance. We conducted experiments on the Multi-Ethnic Study of Atherosclerosis (MESA) dataset, consisting of 2056 subjects. The experiment results show that our proposed network achieves an overall accuracy of 87.3%, which is better than other SAS detection methods and can better assist sleep experts in diagnosing sleep disorders.
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Gutiérrez-Tobal GC, Álvarez D, Vaquerizo-Villar F, Barroso-García V, Gómez-Pilar J, Del Campo F, Hornero R. Conventional Machine Learning Methods Applied to the Automatic Diagnosis of Sleep Apnea. ADVANCES IN EXPERIMENTAL MEDICINE AND BIOLOGY 2022; 1384:131-146. [PMID: 36217082 DOI: 10.1007/978-3-031-06413-5_8] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Abstract
The overnight polysomnography shows a range of drawbacks to diagnose obstructive sleep apnea (OSA) that have led to the search for artificial intelligence-based alternatives. Many classic machine learning methods have been already evaluated for this purpose. In this chapter, we show the main approaches found in the scientific literature along with the most used data to develop the models, useful and large easily available databases, and suitable methods to assess performances. In addition, a range of results from selected studies are presented as examples of these methods. Very high diagnostic performances are reported in these results regardless of the approaches taken. This leads us to conclude that conventional machine learning methods are useful techniques to develop new OSA diagnosis simplification proposals and to act as benchmark for other more recent methods such as deep learning.
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Affiliation(s)
- Gonzalo C Gutiérrez-Tobal
- Centro de Investigación Biomédica en Red, Bioingeniería, Biomateriales, Nanomedicina, Madrid, Spain.
- Biomedical Engineering Group, University of Valladolid, Valladolid, Spain.
| | - Daniel Álvarez
- Centro de Investigación Biomédica en Red, Bioingeniería, Biomateriales, Nanomedicina, Madrid, Spain
- Biomedical Engineering Group, University of Valladolid, Valladolid, Spain
| | - Fernando Vaquerizo-Villar
- Centro de Investigación Biomédica en Red, Bioingeniería, Biomateriales, Nanomedicina, Madrid, Spain
- Biomedical Engineering Group, University of Valladolid, Valladolid, Spain
| | - Verónica Barroso-García
- Centro de Investigación Biomédica en Red, Bioingeniería, Biomateriales, Nanomedicina, Madrid, Spain
- Biomedical Engineering Group, University of Valladolid, Valladolid, Spain
| | - Javier Gómez-Pilar
- Centro de Investigación Biomédica en Red, Bioingeniería, Biomateriales, Nanomedicina, Madrid, Spain
- Biomedical Engineering Group, University of Valladolid, Valladolid, Spain
| | - Félix Del Campo
- Centro de Investigación Biomédica en Red, Bioingeniería, Biomateriales, Nanomedicina, Madrid, Spain
- Biomedical Engineering Group, University of Valladolid, Valladolid, Spain
- Sleep Unit, Pneumology Service, Hospital Universitario Rio Hortega, Valladolid, Spain
| | - Roberto Hornero
- Centro de Investigación Biomédica en Red, Bioingeniería, Biomateriales, Nanomedicina, Madrid, Spain
- Biomedical Engineering Group, University of Valladolid, Valladolid, Spain
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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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Jiang Z, Fa B, Zhang X, Wang J, Feng Y, Shi H, Zhang Y, Sun D, Wang H, Yin S. Identifying genetic risk variants associated with noise-induced hearing loss based on a novel strategy for evaluating individual susceptibility. Hear Res 2021; 407:108281. [PMID: 34157653 DOI: 10.1016/j.heares.2021.108281] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/02/2021] [Revised: 05/10/2021] [Accepted: 05/26/2021] [Indexed: 12/20/2022]
Abstract
BACKGROUND The overall genetic profile for noise-induced hearing loss (NIHL) remains elusive. Herein we proposed a novel machine learning (ML) based strategy to evaluate individual susceptibility to NIHL and identify the underlying genetic risk variants based on a subsample of participants with extreme phenotypes. METHODS Five features (age, sex, cumulative noise exposure [CNE], smoking, and alcohol drinking status) of 5,539 shipbuilding workers from large cross-sectional surveys were included in four ML classification models to predict their hearing levels. The area under the curve (AUC) and prediction accuracy were exploited to evaluate the performance of the models. Based on the prediction error of the ML models, the NIHL-susceptible group (n=150) and NIHL-resistant group (n=150) with a paradoxical relationship between hearing levels and features were separately screened, to identify the underlying variants associated with NIHL risk using whole-exome sequencing (WES). Subsequently, candidate risk variants were validated in an additional replication cohort (n=2108), followed by a meta-analysis. RESULTS With 10-fold cross-validation, the performances of the four ML models were robust and similar, with average AUCs and accuracies ranging from 0.783 to 0.798 and 73.7% to 73.8%, respectively. The phenotypes of the NIHL-susceptible and NIHL-resistant groups were significantly different (all p<0.001). After WES analysis and filtering, 12 risk variants contributing to NIHL susceptibility were identified and replicated. The meta-analyses showed that the A allele of CDH23 rs41281334 (odds ratio [OR]=1.506, 95% confidence interval [CI]=1.106-2.051) and the C allele of WHRN rs12339210 (OR=3.06, 95% CI=1.398-6.700) were significantly associated with increased risk of NIHL after adjustment for confounding factors. CONCLUSIONS This study revealed two genetic variants in CDH23 rs41281334 and WHRN rs12339210 that associated with NIHL risk, based on a promising approach for evaluating individual susceptibility using ML models.
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Affiliation(s)
- Zhuang Jiang
- Department of Otolaryngology-Head and Neck Surgery, Shanghai Jiao Tong University Affiliated Sixth People's Hospital, 600 Yishan Road, Shanghai 200233, China; Otolaryngology Institute of Shanghai Jiao Tong University, Shanghai 200233, China; Shanghai Key Laboratory of Sleep Disordered Breathing, Shanghai 200233, China
| | - Botao Fa
- Department of Bioinformatics and Biostatistics, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, China
| | - Xunmiao Zhang
- Department of Occupational Disease, Shanghai Pulmonary Hospital, Tongji University School of Medicine, Shanghai 200433, China
| | - Jiping Wang
- Department of Otolaryngology-Head and Neck Surgery, Shanghai Jiao Tong University Affiliated Sixth People's Hospital, 600 Yishan Road, Shanghai 200233, China; Otolaryngology Institute of Shanghai Jiao Tong University, Shanghai 200233, China; Shanghai Key Laboratory of Sleep Disordered Breathing, Shanghai 200233, China
| | - Yanmei Feng
- Department of Otolaryngology-Head and Neck Surgery, Shanghai Jiao Tong University Affiliated Sixth People's Hospital, 600 Yishan Road, Shanghai 200233, China; Otolaryngology Institute of Shanghai Jiao Tong University, Shanghai 200233, China; Shanghai Key Laboratory of Sleep Disordered Breathing, Shanghai 200233, China
| | - Haibo Shi
- Department of Otolaryngology-Head and Neck Surgery, Shanghai Jiao Tong University Affiliated Sixth People's Hospital, 600 Yishan Road, Shanghai 200233, China; Otolaryngology Institute of Shanghai Jiao Tong University, Shanghai 200233, China; Shanghai Key Laboratory of Sleep Disordered Breathing, Shanghai 200233, China
| | - Yue Zhang
- Department of Bioinformatics and Biostatistics, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, China
| | - Daoyuan Sun
- Department of Occupational Disease, Shanghai Pulmonary Hospital, Tongji University School of Medicine, Shanghai 200433, China.
| | - Hui Wang
- Department of Otolaryngology-Head and Neck Surgery, Shanghai Jiao Tong University Affiliated Sixth People's Hospital, 600 Yishan Road, Shanghai 200233, China; Otolaryngology Institute of Shanghai Jiao Tong University, Shanghai 200233, China; Shanghai Key Laboratory of Sleep Disordered Breathing, Shanghai 200233, China.
| | - Shankai Yin
- Department of Otolaryngology-Head and Neck Surgery, Shanghai Jiao Tong University Affiliated Sixth People's Hospital, 600 Yishan Road, Shanghai 200233, China; Otolaryngology Institute of Shanghai Jiao Tong University, Shanghai 200233, China; Shanghai Key Laboratory of Sleep Disordered Breathing, Shanghai 200233, China
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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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Yue H, Lin Y, Wu Y, Wang Y, Li Y, Guo X, Huang Y, Wen W, Zhao G, Pang X, Lei W. Deep Learning for Diagnosis and Classification of Obstructive Sleep Apnea: A Nasal Airflow-Based Multi-Resolution Residual Network. Nat Sci Sleep 2021; 13:361-373. [PMID: 33737850 PMCID: PMC7966385 DOI: 10.2147/nss.s297856] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/22/2020] [Accepted: 02/19/2021] [Indexed: 12/17/2022] Open
Abstract
PURPOSE This study evaluated a novel approach for diagnosis and classification of obstructive sleep apnea (OSA), called Obstructive Sleep Apnea Smart System (OSASS), using residual networks and single-channel nasal pressure airflow signals. METHODS Data were collected from the sleep center of the First Affiliated Hospital, Sun Yat-sen University, and the Integrative Department of Guangdong Province Traditional Chinese Medical Hospital. We developed a new model called the multi-resolution residual network (Mr-ResNet) based on a residual network to detect nasal pressure airflow signals recorded by polysomnography (PSG) automatically. The performance of the model was assessed by its sensitivity, specificity, accuracy, and F1-score. We built OSASS based on Mr-ResNet to estimate the apnea‒hypopnea index (AHI) and to classify the severity of OSA, and compared the agreement between OSASS output and the registered polysomnographic technologist (RPSGT) score, assessed by two technologists. RESULTS In the primary test set, the sensitivity, specificity, accuracy, and F1-score of Mr-ResNet were 90.8%, 90.5%, 91.2%, and 90.5%, respectively. In the independent test set, the Spearman correlation for AHI between OSASS and the RPSGT score determined by two technologists was 0.94 (p < 0.001) and 0.96 (p < 0.001), respectively. Cohen's Kappa scores for classification between OSASS and the two technologists' scores were 0.81 and 0.84, respectively. CONCLUSION Our results indicated that OSASS can automatically diagnose and classify OSA using signals from a single-channel nasal pressure airflow, which is consistent with polysomnographic technologists' findings. Thus, OSASS holds promise for clinical application.
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Affiliation(s)
- Huijun Yue
- Otorhinolaryngology Hospital, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, 510080, People's Republic of China
| | - Yu Lin
- Otorhinolaryngology Hospital, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, 510080, People's Republic of China
| | - Yitao Wu
- School of Computer Science, South China Normal University, Guangzhou, 510631, People's Republic of China
| | - Yongquan Wang
- Otorhinolaryngology Hospital, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, 510080, People's Republic of China
| | - Yun Li
- Otorhinolaryngology Hospital, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, 510080, People's Republic of China
| | - Xueqin Guo
- Otorhinolaryngology Hospital, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, 510080, People's Republic of China
| | - Ying Huang
- Guangdong Province Traditional Chinese Medical Hospital, Guangzhou, 510000, People's Republic of China
| | - Weiping Wen
- Otorhinolaryngology Hospital, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, 510080, People's Republic of China
| | - Gansen Zhao
- School of Computer Science, South China Normal University, Guangzhou, 510631, People's Republic of China
| | - Xiongwen Pang
- School of Computer Science, South China Normal University, Guangzhou, 510631, People's Republic of China
| | - Wenbin Lei
- Otorhinolaryngology Hospital, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, 510080, People's Republic of China
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Gaussian Mixture Models for Detecting Sleep Apnea Events Using Single Oronasal Airflow Record. APPLIED SCIENCES-BASEL 2020. [DOI: 10.3390/app10217889] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/30/2022]
Abstract
Sleep apnea is a common sleep-related disorder that significantly affects the population. It is characterized by repeated breathing interruption during sleep. Such events can induce hypoxia, which is a risk factor for multiple cardiovascular and cerebrovascular diseases. Polysomnography, the gold standard, is expensive, inaccessible, uncomfortable and an expert technician is needed to score sleep-related events. To address these limitations, many previous studies have proposed and implemented automatic scoring processes based on fewer sensors and machine learning classification algorithms. However, alternative device technologies developed for both home and hospital still have limited diagnostic accuracy for detecting apnea events even though many of the previous investigational algorithms are based on multiple physiological channel inputs. In this paper, we propose a new probabilistic algorithm based on (only) oronasal respiration signal for automated detection of apnea events during sleep. The proposed model leverages AASM recommendations for characterizing apnea events with respect to dynamic changes in the local respiratory airflow baseline. Unlike classical threshold-based classification models, we use a Gaussian mixture probability model for detecting sleep apnea based on the posterior probabilities of the respective events. Our results show significant improvement in the ability to detect sleep apnea events compared to a rule-based classifier that uses the same classification features and also compared to two previously published studies for automated apnea detection using the same respiratory flow signal. We use 96 sleep patients with different apnea severity levels as reflected by their Apnea-Hypopnea Index (AHI) levels. The performance was not only analyzed over obstructive sleep apnea (OSA) but also over other types of sleep apnea events including central and mixed sleep apnea (CSA, MSA). Also the performance was comprehensively analyzed and evaluated over patients with varying disease severity conditions, where it achieved an overall performance of TPR=88.5%, TNR=82.5%, and AUC=86.7%. The proposed approach contributes a new probabilistic framework for detecting sleep apnea events using a single airflow record with an improved capability to generalize over different apnea severity conditions
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ElMoaqet H, Eid M, Glos M, Ryalat M, Penzel T. Deep Recurrent Neural Networks for Automatic Detection of Sleep Apnea from Single Channel Respiration Signals. SENSORS 2020; 20:s20185037. [PMID: 32899819 PMCID: PMC7570636 DOI: 10.3390/s20185037] [Citation(s) in RCA: 21] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/23/2020] [Revised: 08/22/2020] [Accepted: 09/01/2020] [Indexed: 01/10/2023]
Abstract
Sleep apnea is a common sleep disorder that causes repeated breathing interruption during sleep. The performance of automated apnea detection methods based on respiratory signals depend on the signals considered and feature extraction methods. Moreover, feature engineering techniques are highly dependent on the experts’ experience and their prior knowledge about different physiological signals and conditions of the subjects. To overcome these problems, a novel deep recurrent neural network (RNN) framework is developed for automated feature extraction and detection of apnea events from single respiratory channel inputs. Long short-term memory (LSTM) and bidirectional long short-term memory (BiLSTM) are investigated to develop the proposed deep RNN model. The proposed framework is evaluated over three respiration signals: Oronasal thermal airflow (FlowTh), nasal pressure (NPRE), and abdominal respiratory inductance plethysmography (ABD). To demonstrate our results, we use polysomnography (PSG) data of 17 patients with obstructive, central, and mixed apnea events. Our results indicate the effectiveness of the proposed framework in automatic extraction for temporal features and automated detection of apneic events over the different respiratory signals considered in this study. Using a deep BiLSTM-based detection model, the NPRE signal achieved the highest overall detection results with true positive rate (sensitivity) = 90.3%, true negative rate (specificity) = 83.7%, and area under receiver operator characteristic curve = 92.4%. The present results contribute a new deep learning approach for automated detection of sleep apnea events from single channel respiration signals that can potentially serve as a helpful and alternative tool for the traditional PSG method.
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Affiliation(s)
- Hisham ElMoaqet
- Department of Mechatronics Engineering, German Jordanian University, Amman 11180, Jordan;
- Correspondence: or
| | - Mohammad Eid
- Department of Biomedical Engineering, German Jordanian University, Amman 11180, Jordan;
| | - Martin Glos
- Interdisciplinary Center of Sleep Medicine, Charité-Universitätsmedizin Berlin, 10117 Berlin, Germany; (M.G.); (T.P.)
| | - Mutaz Ryalat
- Department of Mechatronics Engineering, German Jordanian University, Amman 11180, Jordan;
| | - Thomas Penzel
- Interdisciplinary Center of Sleep Medicine, Charité-Universitätsmedizin Berlin, 10117 Berlin, Germany; (M.G.); (T.P.)
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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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Application of automatic detection based on overnight airflow and blood oxygen in patients with sleep disordered breathing. Eur Arch Otorhinolaryngol 2020; 278:873-881. [PMID: 32409858 DOI: 10.1007/s00405-020-06008-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2020] [Accepted: 04/24/2020] [Indexed: 10/24/2022]
Abstract
PURPOSE To explore the feasibility of automatic detection based on air flow and blood oxygen in patients with sleep disordered breathing. METHODS This study proposes a new automated detection method for sleep disordered breathing based on overnight airflow and blood oxygen saturation (SaO2). In this regard, local range (LR) of the airflow was adopted to detect apnea events and the SaO2 sudden drops were used to help determine hypopnea events. Pearson correlation index was used to evaluate the relationship between the two automated methods (this study vs. Remlogic software) and the manual reports. Error and mean absolute error (MAE) were used to assess the two automated methods. RESULTS For all patients, the apnea-hypopnea index (AHI), apnea index (AI) and hypopnea index (HI) for our automated scoring and manual reports were highly correlated (the Pearson correlation index were 0.996, 0.995 and 0.928, respectively, P < 0.001). However, HI for Remlogic automated scoring and clinical manual reports was poorly correlated (r = 0.316, P < 0.001). Compared with the manual reports, mean absolute error of AHI, AI and HI between the two automated methods (this study vs. Remlogic software) were statistically significant (P < 0.0001). Furthermore, among the three subgroups (group 1, AHI < 15/h, group 2, 15/h ≤ AHI < 30/h and group 3, AHI ≥ 30/h), the mean error and MAE of AHI between the two automated methods were also statistically significant (P < 0.01). CONCLUSIONS Generally, good agreements were shown between our automated detection and clinical reports. This procedure is robust and effective, which would significantly shorten the analysis time.
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Zarei A, Asl BM. Automatic classification of apnea and normal subjects using new features extracted from HRV and ECG-derived respiration signals. Biomed Signal Process Control 2020. [DOI: 10.1016/j.bspc.2020.101927] [Citation(s) in RCA: 23] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
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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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Chriskos P, Frantzidis CA, Gkivogkli PT, Papanastasiou E, Kourtidou-Papadeli C, Bamidis PD. SmartHypnos: Developing a Toolbox for Polysomnographic Data Visualization and Analysis .. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2020; 2019:1395-1398. [PMID: 31946153 DOI: 10.1109/embc.2019.8857416] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
In this paper we present the first steps in developing SmartHypnos, an easy to use and user friendly graphical user interface, which aims to provide polysomngographic data visualization and the detection and classification of sleep related events. Currently SmartHypnos supports the visualization of EEG, ECG, EOG and EMG signals, and respiratory signals such as nasal pressure, thermistor, oxygen saturation, thoracic and abdominal belt recordings. All these are incorporated into an interface that provides quick and effortless access to the signals mentioned above. The interface displays automatic sleep staging capabilities as well as the detection of apnea events with accuracy rates surpassing 80%. It is expected that SmartHypnos will reduce the time required to analyze sleep data and also reduce possible human errors.
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18
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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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Zhang Y, Qiao S, Lu R, Han N, Liu D, Zhou J. How to balance the bioinformatics data: pseudo-negative sampling. BMC Bioinformatics 2019; 20:695. [PMID: 31874622 PMCID: PMC6929457 DOI: 10.1186/s12859-019-3269-4] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022] Open
Abstract
BACKGROUND Imbalanced datasets are commonly encountered in bioinformatics classification problems, that is, the number of negative samples is much larger than that of positive samples. Particularly, the data imbalance phenomena will make us underestimate the performance of the minority class of positive samples. Therefore, how to balance the bioinformatic data becomes a very challenging and difficult problem. RESULTS In this study, we propose a new data sampling approach, called pseudo-negative sampling, which can be effectively applied to handle the case that: negative samples greatly dominate positive samples. Specifically, we design a supervised learning method based on a max-relevance min-redundancy criterion beyond Pearson correlation coefficient (MMPCC), which is used to choose pseudo-negative samples from the negative samples and view them as positive samples. In addition, MMPCC uses an incremental searching technique to select optimal pseudo-negative samples to reduce the computation cost. Consequently, the discovered pseudo-negative samples have strong relevance to positive samples and less redundancy to negative ones. CONCLUSIONS To validate the performance of our method, we conduct experiments base on four UCI datasets and three real bioinformatics datasets. According to the experimental results, we clearly observe the performance of MMPCC is better than other sampling methods in terms of Sensitivity, Specificity, Accuracy and the Mathew's Correlation Coefficient. This reveals that the pseudo-negative samples are particularly helpful to solve the imbalance dataset problem. Moreover, the gain of Sensitivity from the minority samples with pseudo-negative samples grows with the improvement of prediction accuracy on all dataset.
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Affiliation(s)
- Yongqing Zhang
- School of Computer Science, Chengdu University of Information Technology, Chengdu, 610225, China
- School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, 610054, China
| | - Shaojie Qiao
- School of Software Engineering, Chengdu University of Information Technology, Chengdu, 610225, China.
- Software Automatic Generation and Intelligent Service Key Laboratory of Sichuan Province, Chengdu University of Information Technology, Chengdu, 610225, China.
| | - Rongzhao Lu
- School of Computer Science, Chengdu University of Information Technology, Chengdu, 610225, China
| | - Nan Han
- School of Management, Chengdu University of Information Technology, Chengdu, 610103, China
| | - Dingxiang Liu
- School of Cybersecurity, Chengdu University of Information Technology, Chengdu, 610225, China
| | - Jiliu Zhou
- School of Computer Science, Chengdu University of Information Technology, Chengdu, 610225, China
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Hsu MH, Fang SC, Wang FT, Chan HL, Huang HE, Yang SC. Sleep apnea assessment using declination duration-based global metrics from unobtrusive fiber optic sensors. Physiol Meas 2019; 40:075005. [PMID: 31361598 DOI: 10.1088/1361-6579/ab21b5] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/29/2022]
Abstract
OBJECTIVE Sufficient sleep helps to restore the immune, nervous and cardiovascular systems, but is sometimes disturbed by sleep apnea (SA). The early diagnosis of sleep apnea is beneficial for the prevention of diseases. Polysomnography (PSG) recording provides comprehensive data for such assessment, but is not suitable for use at home due to discomfort during measurement and the difficulty of identification. This study proposes an unobtrusive measurement process by placing fiber optic sensors (FOSs) in a pillow (head-neck) or a bed mattress (thoracic-dorsal). APPROACH We test two approaches: drop degrees from the baseline to validate the capability of catching respiratory drops, and linear regression models based on a new global measure, the percentage of the total duration of respiratory declination (PTDRD), to estimate the hand-scored apnea/hypopnea index (AHI). MAIN RESULTS Based on data recorded from 63 adults, the drop degrees derived from respiratory signals exhibited statistical differences among central sleep apnea (CSA), obstructive sleep apnea (OSA) and normal breathing. The regression models based on the PTDRDs derived from head-neck FOS and thoracic-dorsal FOS also achieved good agreement with manually scored AHIs in Bland-Altman plots as well as oronasal airflow and thoracic wall movement. SIGNIFICANCE The aforementioned performance demonstrates the capability of the FOS measurement and the efficacy of the PTDRD metrics for SA assessment.
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Affiliation(s)
- Ming-Hung Hsu
- Department of Electrical Engineering, Chang Gung University, Taoyuan, Taiwan. These authors contributed equally to this work
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Yu H, Deng C, Sun J, Chen Y, Cao Y. Cascading detection model for prediction of apnea-hypopnea events based on nasal flow and arterial blood oxygen saturation. Sleep Breath 2019; 24:483-490. [PMID: 31278530 PMCID: PMC7289775 DOI: 10.1007/s11325-019-01886-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2019] [Revised: 06/20/2019] [Accepted: 06/27/2019] [Indexed: 11/28/2022]
Abstract
Purpose Sleep apnea and hypopnea syndrome (SAHS) seriously affects sleep quality. In recent years, much research has focused on the detection of SAHS using various physiological signals and algorithms. The purpose of this study is to find an efficient model for detection of apnea-hypopnea events based on nasal flow and SpO2 signals. Methods A 60-s detector and a 10-s detector were cascaded for precise detection of apnea-hypopnea (AH) events. Random forests were adopted for classification of data segments based on morphological features extracted from nasal flow and arterial blood oxygen saturation (SpO2). Then the segments’ classification results were fed into an event detector to locate the start and end time of every AH event and predict the AH index (AHI). Results A retrospective study of 24 subjects’ polysomnography recordings was conducted. According to segment analysis, the cascading detection model reached an accuracy of 88.3%. While Pearson’s correlation coefficient between estimated AHI and reference AHI was 0.99, in the diagnosis of SAHS severity, the proposed method exhibited a performance with Cohen’s kappa coefficient of 0.76. Conclusions The cascading detection model is able to detect AH events and provide an estimate of AHI. The results indicate that it has the potential to be a useful tool for SAHS diagnosis.
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Affiliation(s)
- Hui Yu
- Department of Biomedical Engineering, Tianjin University, Tianjin, China
| | - Chenyang Deng
- Department of Biomedical Engineering, Tianjin University, Tianjin, China
| | - Jinglai Sun
- Department of Biomedical Engineering, Tianjin University, Tianjin, China
| | - Yanjin Chen
- Tianjin Hospital of ITCWM Nankai Hospital, Tianjin, China
| | - Yuzhen Cao
- Department of Biomedical Engineering, Tianjin University, Tianjin, China.
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Shahid AH, Singh M. Computational intelligence techniques for medical diagnosis and prognosis: Problems and current developments. Biocybern Biomed Eng 2019. [DOI: 10.1016/j.bbe.2019.05.010] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/27/2022]
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Shi K, Schellenberger S, Michler F, Steigleder T, Malessa A, Lurz F, Ostgathe C, Weigel R, Koelpin A. Automatic Signal Quality Index Determination of Radar-Recorded Heart Sound Signals Using Ensemble Classification. IEEE Trans Biomed Eng 2019; 67:773-785. [PMID: 31180834 DOI: 10.1109/tbme.2019.2921071] [Citation(s) in RCA: 17] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Abstract
OBJECTIVE Radar technology promises to be a touchless and thereby burden-free method for continuous heart sound monitoring, which can be used to detect cardiovascular diseases. However, the first and most crucial step is to differentiate between high- and low-quality segments in a recording to assess their suitability for a subsequent automated analysis. This paper gives a comprehensive study on this task and first addresses the specific characteristics of radar-recorded heart sound signals. METHODS To gather heart sound signals recorded from radar, a bistatic radar system was built and installed at the university hospital. Under medical supervision, heart sound data were recorded from 30 healthy test subjects. The signals were segmented and labeled as high- or low-quality by a medical expert. Different state-of-the-art pattern classification algorithms were evaluated for the task of automated signal quality determination and the most promising one was optimized and evaluated using leave-one-subject-out cross validation. RESULTS The proposed classifier is able to achieve an accuracy of up to 96.36% and demonstrates a superior classification performance compared with the state-of-the-art classifier with a maximum accuracy of 76.00%. CONCLUSION This paper introduces an ensemble classifier that is able to perform automated signal quality determination of radar-recorded heart sound signals with a high accuracy. SIGNIFICANCE Besides achieving a higher performance compared with state-of-the-art classifiers, this study is the first one to deal with the quality determination of heart sounds that are recorded by radar systems. The proposed method enables contactless and continuous heart sound monitoring for the detection of cardiovascular diseases.
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Kim J, ElMoaqet H, Tilbury DM, Ramachandran SK, Penzel T. Time domain characterization for sleep apnea in oronasal airflow signal: a dynamic threshold classification approach. Physiol Meas 2019; 40:054007. [DOI: 10.1088/1361-6579/aaf4a9] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
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Álvarez D, Sánchez-Fernández A, Andrés-Blanco AM, Gutiérrez-Tobal GC, Vaquerizo-Villar F, Barroso-García V, Hornero R, del Campo F. Influence of Chronic Obstructive Pulmonary Disease and Moderate-To-Severe Sleep Apnoea in Overnight Cardiac Autonomic Modulation: Time, Frequency and Non-Linear Analyses. ENTROPY 2019; 21:e21040381. [PMID: 33267095 PMCID: PMC7514865 DOI: 10.3390/e21040381] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/18/2019] [Revised: 04/02/2019] [Accepted: 04/05/2019] [Indexed: 11/25/2022]
Abstract
Chronic obstructive pulmonary disease (COPD) is one of the most prevalent lung diseases worldwide. COPD patients show major dysfunction in cardiac autonomic modulation due to sustained hypoxaemia, which has been significantly related to higher risk of cardiovascular disease. Obstructive sleep apnoea syndrome (OSAS) is a frequent comorbidity in COPD patients. It has been found that patients suffering from both COPD and OSAS simultaneously, the so-called overlap syndrome, have notably higher morbidity and mortality. Heart rate variability (HRV) has demonstrated to be useful to assess changes in autonomic functioning in different clinical conditions. However, there is still little scientific evidence on the magnitude of changes in cardiovascular dynamics elicited by the combined effect of both respiratory diseases, particularly during sleep, when apnoeic events occur. In this regard, we hypothesised that a non-linear analysis is able to provide further insight into long-term dynamics of overnight cardiovascular modulation. Accordingly, this study is aimed at assessing the usefulness of sample entropy (SampEn) to distinguish changes in overnight pulse rate variability (PRV) recordings among three patient groups while sleeping: COPD, moderate-to-severe OSAS, and overlap syndrome. In order to achieve this goal, a population composed of 297 patients were studied: 22 with COPD alone, 213 showing moderate-to-severe OSAS, and 62 with COPD and moderate-to-severe OSAS simultaneously (COPD+OSAS). Cardiovascular dynamics were analysed using pulse rate (PR) recordings from unattended pulse oximetry carried out at patients’ home. Conventional time- and frequency- domain analyses were performed to characterise sympathetic and parasympathetic activation of the nervous system, while SampEn was applied to quantify long-term changes in irregularity. Our analyses revealed that overnight PRV recordings from COPD+OSAS patients were significantly more irregular (higher SampEn) than those from patients with COPD alone (0.267 [0.210–0.407] vs. 0.212 [0.151–0.267]; p < 0.05) due to recurrent apnoeic events during the night. Similarly, COPD + OSAS patients also showed significantly higher irregularity in PRV during the night than subjects with OSAS alone (0.267 [0.210–0.407] vs. 0.241 [0.189–0.325]; p = 0.05), which suggests that the cumulative effect of both diseases increases disorganization of pulse rate while sleeping. On the other hand, no statistical significant differences were found between COPD and COPD + OSAS patients when traditional frequency bands (LF and HF) were analysed. We conclude that SampEn is able to properly quantify changes in overnight cardiovascular dynamics of patients with overlap syndrome, which could be useful to assess cardiovascular impairment in COPD patients due to the presence of concomitant OSAS.
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Affiliation(s)
- Daniel Álvarez
- Sleep-Ventilation Unit, Pneumology Service, Río Hortega University Hospital, c/ Dulzaina 2, 47012 Valladolid, Spain
- Biomedical Engineering Group, University of Valladolid, Paseo de Belén 15, 47011 Valladolid, Spain
- Correspondence: ; Tel.: +34-983-420400 (ext. 85776)
| | - Ana Sánchez-Fernández
- Sleep-Ventilation Unit, Pneumology Service, Río Hortega University Hospital, c/ Dulzaina 2, 47012 Valladolid, Spain
| | - Ana M. Andrés-Blanco
- Sleep-Ventilation Unit, Pneumology Service, Río Hortega University Hospital, c/ Dulzaina 2, 47012 Valladolid, Spain
| | | | | | - Verónica Barroso-García
- Biomedical Engineering Group, University of Valladolid, Paseo de Belén 15, 47011 Valladolid, Spain
| | - Roberto Hornero
- Biomedical Engineering Group, University of Valladolid, Paseo de Belén 15, 47011 Valladolid, Spain
| | - Félix del Campo
- Sleep-Ventilation Unit, Pneumology Service, Río Hortega University Hospital, c/ Dulzaina 2, 47012 Valladolid, Spain
- Biomedical Engineering Group, University of Valladolid, Paseo de Belén 15, 47011 Valladolid, Spain
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The Respiratory Fluctuation Index: A global metric of nasal airflow or thoracoabdominal wall movement time series to diagnose obstructive sleep apnea. Biomed Signal Process Control 2019. [DOI: 10.1016/j.bspc.2018.12.015] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022]
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Gutierrez-Tobal GC, Alvarez D, Crespo A, del Campo F, Hornero R. Evaluation of Machine-Learning Approaches to Estimate Sleep Apnea Severity From At-Home Oximetry Recordings. IEEE J Biomed Health Inform 2019; 23:882-892. [DOI: 10.1109/jbhi.2018.2823384] [Citation(s) in RCA: 28] [Impact Index Per Article: 5.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
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Vaquerizo-Villar F, Alvarez D, Kheirandish-Gozal L, Gutierrez-Tobal GC, Barroso-Garcia V, Crespo A, Del Campo F, Gozal D, Hornero R. Improving the Diagnostic Ability of Oximetry Recordings in Pediatric Sleep Apnea-Hypopnea Syndrome by Means of Multi-Class AdaBoost. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2018; 2018:167-170. [PMID: 30441742 DOI: 10.1109/embc.2018.8512264] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
Pediatric sleep apnea-hypopnea syndrome (SAHS) is a highly prevalent respiratory disorder that may impose many negative effects on the health and development of children. Due to the drawbacks of overnight polysomnography (PSG), the gold standard diagnosis technique, automated analysis of nocturnal oximetry has emerged as a simplified alternative. In order to improve diagnosis ability of oximetry, we propose to evaluate the usefulness of AdaBoost, a classification boosting algorithm, in the context of pediatric SAHS. A database composed of 981 SpO2 recordings from pediatric subjects was used. For this purpose, a signal processing approach divided into two main stages was conducted: (i) feature extraction, where 3% oxygen desaturation index (ODI3), spectral, and nonlinear features were computed from the oximetry signal, and (ii) AdaBoost classification, where an AdaBoost.M2 model was trained with these features in order to determine the severity of pediatric SAHS according to the apnea-hypopnea index (AHI): AHI<1 events per hour (e/h), 1≤AHI<5 e/h, and AHI≥5 e/h. Our AdaBoost.M2 model achieved a Cohen's kappa of 0.474 in an independent test set in the 3-class classification task. In addition, high accuracies were obtained when using the AHI cutoffs for diagnosis of mild (AHI=1 e/h) and moderate-to-severe (AHI=5 e/h) SAHS: 80.9% and 82.9%, respectively. These results achieved slightly higher diagnostic accuracies than ODI3 as well as state-of-the-art studies. Therefore, AdaBoost could help to enhance the diagnostic ability of the oximetry signal to assess pediatric SAHS severity.
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Mosquera-Lopez C, Leitschuh J, Condon J, Hagen CC, Hanks C, Jacobs PG. In-Home Sleep Apnea Severity Classification using Contact-free Load Cells and an AdaBoosted Decision Tree Algorithm. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2018; 2018:6044-6047. [PMID: 30441714 DOI: 10.1109/embc.2018.8513602] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/22/2022]
Abstract
We present a method for automated diagnosis and classification of severity of sleep apnea using an array of non-contact pressure-sensitive sensors placed underneath a mattress as an alternative to conventional obtrusive sensors. Our algorithm comprises two stages: i) A decision tree classifier that identifies patients with sleep apnea, and ii) a subsequent linear regression model that estimates the Apnea-Hypopnea Index (AHI), which is used to determine the severity of sleep disordered breathing. We tested our algorithm on a cohort of 14 patients who underwent overnight home sleep apnea test. The machine learning algorithm was trained and performance was evaluated using leave-one-patient-out cross-validation. The accuracy of the proposed approach in detecting sleep apnea is 86.96%, with sensitivity and specificity of 81.82% and 91.67%, respectively. Moreover, classification of severity of the sleep disorder was correctly assigned in 11 out of 14 cases, and the mean absolute error in the AHI estimation was calculated to be 3.83 events/hr.
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Uddin MB, Chow CM, Su SW. Classification methods to detect sleep apnea in adults based on respiratory and oximetry signals: a systematic review. Physiol Meas 2018; 39:03TR01. [DOI: 10.1088/1361-6579/aaafb8] [Citation(s) in RCA: 40] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/11/2023]
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Andrés-Blanco AM, Álvarez D, Crespo A, Arroyo CA, Cerezo-Hernández A, Gutiérrez-Tobal GC, Hornero R, del Campo F. Assessment of automated analysis of portable oximetry as a screening test for moderate-to-severe sleep apnea in patients with chronic obstructive pulmonary disease. PLoS One 2017; 12:e0188094. [PMID: 29176802 PMCID: PMC5703515 DOI: 10.1371/journal.pone.0188094] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2017] [Accepted: 10/31/2017] [Indexed: 11/19/2022] Open
Abstract
BACKGROUND The coexistence of obstructive sleep apnea syndrome (OSAS) and chronic obstructive pulmonary disease (COPD) leads to increased morbidity and mortality. The development of home-based screening tests is essential to expedite diagnosis. Nevertheless, there is still very limited evidence on the effectiveness of portable monitoring to diagnose OSAS in patients with pulmonary comorbidities. OBJECTIVE To assess the influence of suffering from COPD in the performance of an oximetry-based screening test for moderate-to-severe OSAS, both in the hospital and at home. METHODS A total of 407 patients showing moderate-to-high clinical suspicion of OSAS were involved in the study. All subjects underwent (i) supervised portable oximetry simultaneously to in-hospital polysomnography (PSG) and (ii) unsupervised portable oximetry at home. A regression-based multilayer perceptron (MLP) artificial neural network (ANN) was trained to estimate the apnea-hypopnea index (AHI) from portable oximetry recordings. Two independent validation datasets were analyzed: COPD versus non-COPD. RESULTS The portable oximetry-based MLP ANN reached similar intra-class correlation coefficient (ICC) values between the estimated AHI and the actual AHI for the non-COPD and the COPD groups either in the hospital (non-COPD: 0.937, 0.909-0.956 CI95%; COPD: 0.936, 0.899-0.960 CI95%) and at home (non-COPD: 0.731, 0.631-0.808 CI95%; COPD: 0.788, 0.678-0.864 CI95%). Regarding the area under the receiver operating characteristics curve (AUC), no statistically significant differences (p >0.01) between COPD and non-COPD groups were found in both settings, particularly for severe OSAS (AHI ≥30 events/h): 0.97 (0.92-0.99 CI95%) non-COPD vs. 0.98 (0.92-1.0 CI95%) COPD in the hospital, and 0.87 (0.79-0.92 CI95%) non-COPD vs. 0.86 (0.75-0.93 CI95%) COPD at home. CONCLUSION The agreement and the diagnostic performance of the estimated AHI from automated analysis of portable oximetry were similar regardless of the presence of COPD both in-lab and at-home. Particularly, portable oximetry could be used as an abbreviated screening test for moderate-to-severe OSAS in patients with COPD.
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Affiliation(s)
| | - Daniel Álvarez
- Pneumology Service, Río Hortega University Hospital, Valladolid, Spain
- Biomedical Engineering Group, University of Valladolid, Valladolid, Spain
| | - Andrea Crespo
- Pneumology Service, Río Hortega University Hospital, Valladolid, Spain
- Biomedical Engineering Group, University of Valladolid, Valladolid, Spain
| | - C. Ainhoa Arroyo
- Pneumology Service, Río Hortega University Hospital, Valladolid, Spain
| | | | | | - Roberto Hornero
- Biomedical Engineering Group, University of Valladolid, Valladolid, Spain
| | - Félix del Campo
- Pneumology Service, Río Hortega University Hospital, Valladolid, Spain
- Biomedical Engineering Group, University of Valladolid, Valladolid, Spain
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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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Shokoueinejad M, Fernandez C, Carroll E, Wang F, Levin J, Rusk S, Glattard N, Mulchrone A, Zhang X, Xie A, Teodorescu M, Dempsey J, Webster J. Sleep apnea: a review of diagnostic sensors, algorithms, and therapies. Physiol Meas 2017; 38:R204-R252. [PMID: 28820743 DOI: 10.1088/1361-6579/aa6ec6] [Citation(s) in RCA: 40] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/05/2023]
Abstract
While public awareness of sleep related disorders is growing, sleep apnea syndrome (SAS) remains a public health and economic challenge. Over the last two decades, extensive controlled epidemiologic research has clarified the incidence, risk factors including the obesity epidemic, and global prevalence of obstructive sleep apnea (OSA), as well as establishing a growing body of literature linking OSA with cardiovascular morbidity, mortality, metabolic dysregulation, and neurocognitive impairment. The US Institute of Medicine Committee on Sleep Medicine estimates that 50-70 million US adults have sleep or wakefulness disorders. Furthermore, the American Academy of Sleep Medicine (AASM) estimates that more than 29 million US adults suffer from moderate to severe OSA, with an estimated 80% of those individuals living unaware and undiagnosed, contributing to more than $149.6 billion in healthcare and other costs in 2015. Although various devices have been used to measure physiological signals, detect apneic events, and help treat sleep apnea, significant opportunities remain to improve the quality, efficiency, and affordability of sleep apnea care. As our understanding of respiratory and neurophysiological signals and sleep apnea physiological mechanisms continues to grow, and our ability to detect and process biomedical signals improves, novel diagnostic and treatment modalities emerge. OBJECTIVE This article reviews the current engineering approaches for the detection and treatment of sleep apnea. APPROACH It discusses signal acquisition and processing, highlights the current nonsurgical and nonpharmacological treatments, and discusses potential new therapeutic approaches. MAIN RESULTS This work has led to an array of validated signal and sensor modalities for acquiring, storing and viewing sleep data; a broad class of computational and signal processing approaches to detect and classify SAS disease patterns; and a set of distinctive therapeutic technologies whose use cases span the continuum of disease severity. SIGNIFICANCE This review provides a current perspective of the classes of tools at hand, along with a sense of their relative strengths and areas for further improvement.
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Affiliation(s)
- Mehdi Shokoueinejad
- Department of Biomedical Engineering, University of Wisconsin-Madison, 1550 Engineering Drive, Madison, WI 53706-1609, United States of America. Department of Population Health Sciences, University of Wisconsin-Madison, 610 Walnut St 707, Madison, WI 53726, United States of America. EnsoData Research, EnsoData Inc., 111 N Fairchild St, Suite 240, Madison, WI 53703, United States of America
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Gutierrez-Tobal GC, de Frutos J, Alvarez D, Vaquerizo-Villar F, Barroso-Garcia V, Crespo A, Del Campo F, Hornero R. A Bayesian neural network approach to compare the spectral information from nasal pressure and thermistor airflow in the automatic sleep apnea severity estimation. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2017; 2017:3741-3744. [PMID: 29060711 DOI: 10.1109/embc.2017.8037670] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/07/2023]
Abstract
In the sleep apnea-hypopnea syndrome (SAHS) context, airflow signal plays a key role for the simplification of the diagnostic process. It is measured during the standard diagnostic test by the acquisition of two simultaneous sensors: a nasal prong pressure (NPP) and a thermistor (TH). The current study focuses on the comparison of their spectral content to help in the automatic SAHS-severity estimation. The spectral analysis of 315 NPP and corresponding TH recordings is firstly proposed to characterize the conventional band of interest for SAHS (0.025-0.050 Hz.). A magnitude squared coherence analysis is also conducted to quantify possible differences in the frequency components of airflow from both sensors. Then, a feature selection stage is implemented to assess the relevance and redundancy of the information extracted from the spectrum of NPP and TH airflow. Finally, a multiclass Bayesian multi-layer perceptron (BY-MLP) was used to perform an automatic estimation of SAHS severity (no-SAHS, mild, moderate, and severe), by the use of the selected spectral features from: airflow NPP alone, airflow TH alone, and both sensors jointly. The highest diagnostic performance was reached by BY-MLP only trained with NPP spectral features, reaching Cohen's κ = 0.498 in the overall four-class classification task. It also achieved 91.3%, 84.9%, and 83.3% of accuracy in the binary evaluation of the 3 apnea-hypopnea index cut-offs (5, 15, and 30 events/hour) that define the four SAHS degrees. Our results suggest that TH sensor might be not necessary for SAHS severity estimation if an automatic comprehensive characterization approach is adopted to simplify the diagnostic process.
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