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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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Romero D, Jané R. Dynamic Bayesian Model for Detecting Obstructive Respiratory Events by Using an Experimental Model. SENSORS (BASEL, SWITZERLAND) 2023; 23:3371. [PMID: 37050431 PMCID: PMC10097311 DOI: 10.3390/s23073371] [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: 02/06/2023] [Revised: 03/17/2023] [Accepted: 03/21/2023] [Indexed: 06/19/2023]
Abstract
In this study, we propose a model-based tool for the detection of obstructive apnea episodes by using ECG features from a single lead channel. Several sequences of recurrent apnea were provoked in separate 15-min periods in anesthetized rats during an experimental model of obstructive sleep apnea (OSA). Morphology-based ECG markers and the beat-to-beat interval (RR) were assessed in each sequence. These markers were used to train dynamic Bayesian networks (DBN) with different orders and feature combinations to find a good tradeoff between network complexity and apnea-detection performance. By using a filtering approach, the resulting DBNs were used to infer the apnea probability signal for subsequent episodes in the same rat. These signals were then processed using by 15-s epochs to determine whether epochs were classified as apneic or nonapneic. Our results showed that fifth-order models provided suitable RMSE values, since higher order models become significantly more complex and present worse generalization. A global threshold of 0.2 gave the best overall performance for all combinations tested, with Acc = 81.3%, Se = 69.8% and Sp = 81.5%, using only two parameters including the RR and Ds (R-wave downslope) markers. We concluded that multivariate models using DBNs represent a powerful tool for detecting obstructive apnea episodes in short segments, which may also serve to estimate the number of total events in a given time period.
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Affiliation(s)
- Daniel Romero
- ESAII Department, Universitat Politècnica de Catalunya—BarcelonaTech (UPC), 08019 Barcelona, Spain
- Institute for Bioengineering of Catalonia (IBEC-BIST), 08028 Barcelona, Spain
- CIBER of Bioengineering, Biomaterials and Nanomedicine (CIBER-BBN), 28029 Madrid, Spain
| | - Raimon Jané
- ESAII Department, Universitat Politècnica de Catalunya—BarcelonaTech (UPC), 08019 Barcelona, Spain
- Institute for Bioengineering of Catalonia (IBEC-BIST), 08028 Barcelona, Spain
- CIBER of Bioengineering, Biomaterials and Nanomedicine (CIBER-BBN), 28029 Madrid, Spain
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Detection of Sleep Apnea from Electrocardiogram and Pulse Oximetry Signals Using Random Forest. APPLIED SCIENCES-BASEL 2022. [DOI: 10.3390/app12094218] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/08/2023]
Abstract
Sleep apnea (SA) is a common sleep disorder which could impair the human physiological system. Therefore, early diagnosis of SA is of great interest. The traditional method of diagnosing SA is an overnight polysomnography (PSG) evaluation. When PSG has limited availability, automatic SA screening with a fewer number of signals should be considered. The primary purpose of this study is to develop and evaluate a SA detection model based on electrocardiogram (ECG) and blood oxygen saturation (SpO2). We adopted a multimodal approach to fuse ECG and SpO2 signals at the feature level. Then, feature selection was conducted using the recursive feature elimination with cross-validation (RFECV) algorithm and random forest (RF) classifier used to discriminate between apnea and normal events. Experiments were conducted on the Apnea-ECG database. The introduced algorithm obtained an accuracy of 97.5%, a sensitivity of 95.9%, a specificity of 98.4% and an AUC of 0.992 in per-segment classification, and outperformed previous works. The results showed that ECG and SpO2 are complementary in detecting SA, and that the combination of ECG and SpO2 enhances the ability to diagnose SA. Therefore, the proposed method has the potential to be an alternative to conventional detection methods.
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Automated detection of obstructive sleep apnea in more than 8000 subjects using frequency optimized orthogonal wavelet filter bank with respiratory and oximetry signals. Comput Biol Med 2022; 144:105364. [PMID: 35299046 DOI: 10.1016/j.compbiomed.2022.105364] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2021] [Revised: 02/27/2022] [Accepted: 02/27/2022] [Indexed: 12/12/2022]
Abstract
Obstructive sleep apnea (OSA) is a common respiratory disorder marked by interruption of the respiratory tract and difficulty in breathing. The risk of serious health damage can be reduced if OSA is diagnosed and treated at an early stage. OSA is primarily diagnosed using polysomnography (PSG) monitoring performed for overnight sleep; furthermore, capturing PSG signals during the night is expensive, time-consuming, complex and highly inconvenient to patients. Hence, we are proposing to detect OSA automatically using respiratory and oximetry signals. The aim of this study is to develop a simple and computationally efficient wavelet-based automated system based on these signals to detect OSA in elderly subjects. In this study, we proposed an accurate, reliable, and less complex OSA automated detection system by using pulse oximetry (SpO2) and respiratory signals including thoracic (ThorRes) movement, abdominal (AbdoRes) movement, and airflow (AF). These signals are collected from the Sleep Heart Health Study (SHHS) database from the National Sleep Research Resource (NSRR), which is one of the largest repositories of publicly available sleep databases. The database comprises of two groups SHHS-1 and SHHS-2, which involves 5,793 and 2,651 subjects, respectively with an average age of ≥60 years. The 30-s epochs of the signals are decomposed into sub-bands using frequency optimized orthogonal wavelet filter bank. Tsallis entropies are extracted from the sub-band coefficients of wavelet filter bank. A total 4,415,229 epochs of respiratory and oximetry signals are used to develop the model. The proposed model is developed using GentleBoost and Random under-sampling Boosting (RUSBoosted Tree) algorithms with 10-fold cross-validation technique. Our developed model has obtained the highest classification accuracy of 89.39% and 84.64% for the imbalanced and balanced datasets, respectively using 10-fold cross-validation technique. Using the 20% hold-out validation, the model yielded an accuracy of 88.26% and 84.31% for the imbalanced and balanced datasets, respectively. Hence, the respiratory and SpO2 signals-based model can be used for automated OSA detection. The results obtained from the proposed model are better than the state-of-the-art models and can be used in-home for screening the OSA.
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Barroso-García V, Jiménez-García J, Gutiérrez-Tobal GC, Hornero R. Airflow Analysis in the Context of Sleep Apnea. ADVANCES IN EXPERIMENTAL MEDICINE AND BIOLOGY 2022; 1384:241-253. [PMID: 36217088 DOI: 10.1007/978-3-031-06413-5_14] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
Abstract
The airflow (AF) is a physiological signal involved in the overnight polysomnography (PSG) that reflects the respiratory activity. This signal is able to show the particularities of sleep apnea and is therefore used to define apneic events. In this regard, a growing number of studies have shown the usefulness of employing the overnight airflow as the only or combined information source for diagnosing sleep apnea in both children and adults. Due to its easy acquisition and interpretation, this biosignal has been widely analyzed by means of different signal processing techniques. In this chapter, we review the main methodological approaches applied to characterize and extract relevant information from this signal. In view of the results, we can conclude that the overnight airflow successfully reflects the particularities caused by the occurrence of apneic and hypopneic events and provides useful information for obtaining relevant biomarkers that characterize this disease.
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Affiliation(s)
- Verónica Barroso-García
- Biomedical Engineering Group, University of Valladolid, Valladolid, Spain.
- Centro de Investigación Biomédica en Red en Bioingeniería, Biomateriales y Nanomedicina (CIBER-BBN), Valladolid, Spain.
| | | | - Gonzalo C Gutiérrez-Tobal
- Biomedical Engineering Group, University of Valladolid, Valladolid, Spain
- Centro de Investigación Biomédica en Red en Bioingeniería, Biomateriales y Nanomedicina (CIBER-BBN), Valladolid, Spain
| | - Roberto Hornero
- Biomedical Engineering Group, University of Valladolid, Valladolid, Spain
- Centro de Investigación Biomédica en Red en Bioingeniería, Biomateriales y Nanomedicina (CIBER-BBN), Valladolid, Spain
- Mathematics Research Institute of the University of Valladolid (IMUVa), Valladolid, Spain
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Martín-Montero A, Gutiérrez-Tobal GC, Kheirandish-Gozal L, Jiménez-García J, Álvarez D, del Campo F, Gozal D, Hornero R. Heart rate variability spectrum characteristics in children with sleep apnea. Pediatr Res 2021; 89:1771-1779. [PMID: 32927472 PMCID: PMC7956022 DOI: 10.1038/s41390-020-01138-2] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/19/2020] [Revised: 08/04/2020] [Accepted: 08/10/2020] [Indexed: 01/04/2023]
Abstract
BACKGROUND Classic spectral analysis of heart rate variability (HRV) in pediatric sleep apnea-hypopnea syndrome (SAHS) traditionally evaluates the very low frequency (VLF: 0-0.04 Hz), low frequency (LF: 0.04-0.15 Hz), and high frequency (HF: 0.15-0.40 Hz) bands. However, specific SAHS-related frequency bands have not been explored. METHODS One thousand seven hundred and thirty-eight HRV overnight recordings from two pediatric databases (0-13 years) were evaluated. The first one (981 children) served as training set to define new HRV pediatric SAHS-related frequency bands. The associated relative power (RP) were computed in the test set, the Childhood Adenotonsillectomy Trial database (CHAT, 757 children). Their relationships with polysomnographic variables and diagnostic ability were assessed. RESULTS Two new specific spectral bands of pediatric SAHS within 0-0.15 Hz were related to duration of apneic events, number of awakenings, and wakefulness after sleep onset (WASO), while an adaptive individual-specific new band from HF was related to oxyhemoglobin desaturations, arousals, and WASO. Furthermore, these new spectral bands showed improved diagnostic ability than classic HRV. CONCLUSIONS Novel spectral bands provide improved characterization of pediatric SAHS. These findings may pioneer a better understanding of the effects of SAHS on cardiac function and potentially serve as detection biomarkers. IMPACT New specific heart rate variability (HRV) spectral bands are identified and characterized as potential biomarkers in pediatric sleep apnea. Spectral band BW1 (0.001-0.005 Hz) is related to macro sleep disruptions. Spectral band BW2 (0.028-0.074 Hz) is related to the duration of apneic events. An adaptive spectral band within the respiratory range, termed ABW3, is related to oxygen desaturations. The individual and collective diagnostic ability of these novel spectral bands outperforms classic HRV bands.
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Affiliation(s)
| | - 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 and The Child Health Research Institute, The University of Missouri School of Medicine, Columbia, Missouri
| | | | - 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 and The Child Health Research Institute, The University of Missouri School of Medicine, Columbia, Missouri
| | - 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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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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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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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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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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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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Van Steenkiste T, Groenendaal W, Deschrijver D, Dhaene T. Automated Sleep Apnea Detection in Raw Respiratory Signals Using Long Short-Term Memory Neural Networks. IEEE J Biomed Health Inform 2019; 23:2354-2364. [PMID: 30530344 DOI: 10.1109/jbhi.2018.2886064] [Citation(s) in RCA: 43] [Impact Index Per Article: 8.6] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
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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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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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Mendonca F, Mostafa SS, Ravelo-Garcia AG, Morgado-Dias F, Penzel T. A Review of Obstructive Sleep Apnea Detection Approaches. IEEE J Biomed Health Inform 2019; 23:825-837. [DOI: 10.1109/jbhi.2018.2823265] [Citation(s) in RCA: 75] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
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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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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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20
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Hornero R, Kheirandish-Gozal L, Gutiérrez-Tobal GC, Philby MF, Alonso-Álvarez ML, Álvarez D, Dayyat EA, Xu Z, Huang YS, Tamae Kakazu M, Li AM, Van Eyck A, Brockmann PE, Ehsan Z, Simakajornboon N, Kaditis AG, Vaquerizo-Villar F, Crespo Sedano A, Sans Capdevila O, von Lukowicz M, Terán-Santos J, Del Campo F, Poets CF, Ferreira R, Bertran K, Zhang Y, Schuen J, Verhulst S, Gozal D. Nocturnal Oximetry-based Evaluation of Habitually Snoring Children. Am J Respir Crit Care Med 2017; 196:1591-1598. [PMID: 28759260 DOI: 10.1164/rccm.201705-0930oc] [Citation(s) in RCA: 76] [Impact Index Per Article: 10.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/09/2023] Open
Abstract
RATIONALE The vast majority of children around the world undergoing adenotonsillectomy for obstructive sleep apnea-hypopnea syndrome (OSA) are not objectively diagnosed by nocturnal polysomnography because of access availability and cost issues. Automated analysis of nocturnal oximetry (nSpO2), which is readily and globally available, could potentially provide a reliable and convenient diagnostic approach for pediatric OSA. METHODS Deidentified nSpO2 recordings from a total of 4,191 children originating from 13 pediatric sleep laboratories around the world were prospectively evaluated after developing and validating an automated neural network algorithm using an initial set of single-channel nSpO2 recordings from 589 patients referred for suspected OSA. MEASUREMENTS AND MAIN RESULTS The automatically estimated apnea-hypopnea index (AHI) showed high agreement with AHI from conventional polysomnography (intraclass correlation coefficient, 0.785) when tested in 3,602 additional subjects. Further assessment on the widely used AHI cutoff points of 1, 5, and 10 events/h revealed an incremental diagnostic ability (75.2, 81.7, and 90.2% accuracy; 0.788, 0.854, and 0.913 area under the receiver operating characteristic curve, respectively). CONCLUSIONS Neural network-based automated analyses of nSpO2 recordings provide accurate identification of OSA severity among habitually snoring children with a high pretest probability of OSA. Thus, nocturnal oximetry may enable a simple and effective diagnostic alternative to nocturnal polysomnography, leading to more timely interventions and potentially improved outcomes.
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Affiliation(s)
- Roberto Hornero
- 1 Biomedical Engineering Group, University of Valladolid, Valladolid, Spain
| | - Leila Kheirandish-Gozal
- 2 Section of Sleep Medicine, Department of Pediatrics, Pritzker School of Medicine, Biological Sciences Division, University of Chicago, Chicago, Illinois
| | | | - Mona F Philby
- 2 Section of Sleep Medicine, Department of Pediatrics, Pritzker School of Medicine, Biological Sciences Division, University of Chicago, Chicago, Illinois
| | - María Luz Alonso-Álvarez
- 3 Unidad Multidisciplinar del Sueño, Centro de Investigación Biomédica en Red Respiratorio, Hospital Universitario de Burgos, Burgos, Spain
| | - Daniel Álvarez
- 1 Biomedical Engineering Group, University of Valladolid, Valladolid, Spain.,4 Sleep-Ventilation Unit, Pneumology Service, Río Hortega University Hospital, Valladolid, Spain
| | - Ehab A Dayyat
- 5 Division of Child Neurology, Department of Pediatrics, LeBonheur Children's Hospital, University of Tennessee Health Science Center, School of Medicine, Memphis, Tennessee
| | - Zhifei Xu
- 6 Sleep Unit, Beijing Children's Hospital, Capital Medical University, Beijing, People's Republic of China
| | - Yu-Shu Huang
- 7 Department of Child Psychiatry and Sleep Center, Chang Gung Memorial Hospital and University, Taoyuan, Taiwan
| | | | - Albert M Li
- 9 Department of Pediatrics, Prince of Wales Hospital, Chinese University of Hong Kong, Hong Kong, China
| | - Annelies Van Eyck
- 10 Laboratory of Experimental Medicine and Pediatrics and.,11 Department of Pediatrics, University of Antwerp and Antwerp University Hospital, Antwerp, Belgium
| | - Pablo E Brockmann
- 12 Sleep Medicine Center, Department of Pediatric Cardiology and Pulmonology, School of Medicine, Pontificia Universidad Católica de Chile, Santiago, Chile
| | - Zarmina Ehsan
- 13 Division of Pulmonary and Sleep Medicine, Cincinnati Children's Medical Center, Cincinnati, Ohio
| | - Narong Simakajornboon
- 13 Division of Pulmonary and Sleep Medicine, Cincinnati Children's Medical Center, Cincinnati, Ohio
| | - Athanasios G Kaditis
- 14 Pediatric Pulmonology Unit, Sleep Disorders Laboratory, First Department of Pediatrics, National and Kapodistrian University of Athens School of Medicine and Aghia Sophia Children's Hospital, Athens, Greece
| | | | - Andrea Crespo Sedano
- 4 Sleep-Ventilation Unit, Pneumology Service, Río Hortega University Hospital, Valladolid, Spain
| | - Oscar Sans Capdevila
- 15 Sleep Unit, Department of Neurology, Sant Joan de Deu, Barcelona Children's Hospital, Barcelona, Spain
| | - Magnus von Lukowicz
- 16 Department of Neonatology and Sleep Unit, University of Tubingen, Tubingen, Germany; and
| | - Joaquín Terán-Santos
- 3 Unidad Multidisciplinar del Sueño, Centro de Investigación Biomédica en Red Respiratorio, Hospital Universitario de Burgos, Burgos, Spain
| | - Félix Del Campo
- 1 Biomedical Engineering Group, University of Valladolid, Valladolid, Spain.,4 Sleep-Ventilation Unit, Pneumology Service, Río Hortega University Hospital, Valladolid, Spain
| | - Christian F Poets
- 16 Department of Neonatology and Sleep Unit, University of Tubingen, Tubingen, Germany; and
| | - Rosario Ferreira
- 17 Pediatric Respiratory Unit, Department of Pediatrics, Hospital de Santa Maria, Academic Medical Center of Lisbon, Lisbon, Portugal
| | - Katalina Bertran
- 15 Sleep Unit, Department of Neurology, Sant Joan de Deu, Barcelona Children's Hospital, Barcelona, Spain
| | - Yamei Zhang
- 6 Sleep Unit, Beijing Children's Hospital, Capital Medical University, Beijing, People's Republic of China
| | - John Schuen
- 8 Spectrum Health, Michigan State University, Grand Rapids, Michigan
| | - Stijn Verhulst
- 10 Laboratory of Experimental Medicine and Pediatrics and.,11 Department of Pediatrics, University of Antwerp and Antwerp University Hospital, Antwerp, Belgium
| | - David Gozal
- 2 Section of Sleep Medicine, Department of Pediatrics, Pritzker School of Medicine, Biological Sciences Division, University of Chicago, Chicago, Illinois
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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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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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Lee H, Park J, Kim H, Lee KJ. New Rule-Based Algorithm for Real-Time Detecting Sleep Apnea and Hypopnea Events Using a Nasal Pressure Signal. J Med Syst 2016; 40:282. [PMID: 27787786 DOI: 10.1007/s10916-016-0637-8] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/29/2014] [Accepted: 10/14/2016] [Indexed: 11/28/2022]
Abstract
We developed a rule-based algorithm for automatic real-time detection of sleep apnea and hypopnea events using a nasal pressure signal. Our basic premise was that the performance of our new algorithm using the nasal pressure signal would be comparable to that using other sensors as well as manual annotation labeled by a technician on polysomnography study. We investigated fifty patients with sleep apnea-hypopnea syndrome (age: 56.8 ± 10.5 years, apnea-hypopnea index (AHI): 36.2 ± 18.1/h) during full night PSG recordings at the sleep center. The algorithm was comprised of pre-processing with a median filter, amplitude computation and apnea-hypopnea detection parts. We evaluated the performance of the algorithm a confusion matric for each event and statistical analyses for AHI. Our evaluation achieved a good performance, with a sensitivity of 86.4 %, and a positive predictive value of 84.5 % for detection of apnea and hypopnea regardless of AHI severity. Our results indicated a high correlation with the manually labeled apnea-hypopnea events during PSG, with a correlation coefficient of r = 0.94 (p < 0.0001) and a mean difference of -2.9 ± 11.6 per hour. The proposed new algorithm could provide significant clinical and computational insights to design a PSG analysis system and a continuous positive airway pressure (CPAP) device for screening sleep quality related in patients with sleep apnea-hypopnea syndrome.
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Affiliation(s)
- Hyoki Lee
- Interdisciplinary Consortium on Advanced Motion Performance, Michael E. DeBakey Department of Surgery, Baylor College of Medicine, Houston, TX, USA
| | - Jonguk Park
- Department of Biomedical Engineering, Yonsei University, Wonju, Republic of Korea
| | - Hojoong Kim
- Division of Pulmonary and Critical Care Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea
| | - Kyoung-Joung Lee
- Department of Biomedical Engineering, Yonsei University, Wonju, Republic of Korea.
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Gutierrez-Tobal GC, Alvarez D, del Campo F, Hornero R. Utility of AdaBoost to Detect Sleep Apnea-Hypopnea Syndrome From Single-Channel Airflow. IEEE Trans Biomed Eng 2016; 63:636-46. [DOI: 10.1109/tbme.2015.2467188] [Citation(s) in RCA: 41] [Impact Index Per Article: 5.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
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26
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Kagawa M, Tojima H, Matsui T. Non-contact diagnostic system for sleep apnea–hypopnea syndrome based on amplitude and phase analysis of thoracic and abdominal Doppler radars. Med Biol Eng Comput 2015; 54:789-98. [DOI: 10.1007/s11517-015-1370-z] [Citation(s) in RCA: 27] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2015] [Accepted: 08/07/2015] [Indexed: 12/01/2022]
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Alvarez-Estevez D, Moret-Bonillo V. Computer-assisted diagnosis of the Sleep Apnea-Hypopnea Syndrome: An overview of different approaches. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2015; 2015:4544-9. [PMID: 26737305 DOI: 10.1109/embc.2015.7319405] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/05/2023]
Abstract
Automatic diagnosis of the Sleep Apnea-Hypopnea Syndrome (SAHS) has become an important area of research due to the growing interest in the field of sleep medicine, and the costs associated to its manual diagnosis. The increment and heterogeneity of the different techniques, however, makes somewhat difficult to adequately follow recent developments. In this paper an overview within the area of computer-assisted diagnosis of SAHS has been performed. This overview of the different methods is presented together with a critical discussion of the current state-of-the-art.
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Alvarez-Estevez D, Moret-Bonillo V. Computer-Assisted Diagnosis of the Sleep Apnea-Hypopnea Syndrome: A Review. SLEEP DISORDERS 2015; 2015:237878. [PMID: 26266052 PMCID: PMC4523666 DOI: 10.1155/2015/237878] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Received: 05/01/2015] [Revised: 06/15/2015] [Accepted: 06/21/2015] [Indexed: 02/07/2023]
Abstract
Automatic diagnosis of the Sleep Apnea-Hypopnea Syndrome (SAHS) has become an important area of research due to the growing interest in the field of sleep medicine and the costs associated with its manual diagnosis. The increment and heterogeneity of the different techniques, however, make it somewhat difficult to adequately follow the recent developments. A literature review within the area of computer-assisted diagnosis of SAHS has been performed comprising the last 15 years of research in the field. Screening approaches, methods for the detection and classification of respiratory events, comprehensive diagnostic systems, and an outline of current commercial approaches are reviewed. An overview of the different methods is presented together with validation analysis and critical discussion of the current state of the art.
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Affiliation(s)
| | - Vicente Moret-Bonillo
- Laboratory for Research and Development in Artificial Intelligence (LIDIA), Department of Computer Science, University of A Coruña, 15071 A Coruña, Spain
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29
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Martínez A, Abásolo D, Alcaraz R, Rieta JJ. Alteration of the P-wave non-linear dynamics near the onset of paroxysmal atrial fibrillation. Med Eng Phys 2015; 37:692-7. [PMID: 25956053 DOI: 10.1016/j.medengphy.2015.03.021] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2015] [Revised: 02/23/2015] [Accepted: 03/29/2015] [Indexed: 11/28/2022]
Abstract
The analysis of P-wave variability from the electrocardiogram (ECG) has been suggested as an early predictor of the onset of paroxysmal atrial fibrillation (PAF). Hence, a preventive treatment could be used to avoid the loss of normal sinus rhythm, thus minimising health risks and improving the patient's quality of life. In these previous studies the variability of different temporal and morphological P-wave features has been only analysed in a linear fashion. However, the electrophysiological alteration occurring in the atria before the onset of PAF has to be considered as an inherently complex, chaotic and non-stationary process. This work analyses the presence of non-linear dynamics in the P-wave progression before the onset of PAF through the application of the central tendency measure (CTM), which is a non-linear metric summarising the degree of variability in a time series. Two hour-length ECG intervals just before the arrhythmia onset belonging to 46 different PAF patients were analysed. In agreement with the invasively observed inhomogeneous atrial conduction preceding the onset of PAF, CTM for all the considered P-wave features showed higher variability when the arrhythmia was closer to its onset. A diagnostic accuracy around 80% to discern between ECG segments far from PAF and close to PAF was obtained with the CTM of the metrics considered. This result was similar to previous P-wave variability methods based on linear approaches. However, the combination of linear and non-linear methods with a decision tree improved considerably their discriminant ability up to 90%, thus suggesting that both dynamics could coexist at the same time in the fragmented depolarisation of the atria preceding the arrhythmia.
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Affiliation(s)
- Arturo Martínez
- Research Group in Electronic, Biomedical and Telecommunication Engineering, University of Castilla-La Mancha, Spain
| | - Daniel Abásolo
- Centre for Biomedical Engineering, Department of Mechanical Engineering Sciences, University of Surrey, UK
| | - Raúl Alcaraz
- Research Group in Electronic, Biomedical and Telecommunication Engineering, University of Castilla-La Mancha, Spain.
| | - José J Rieta
- Biomedical Synergy, Electronic Engineering Department, Universidad Politécnica de Valencia, Spain
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30
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Gutiérrez-Tobal GC, Alonso-Álvarez ML, Álvarez D, del Campo F, Terán-Santos J, Hornero R. Diagnosis of pediatric obstructive sleep apnea: Preliminary findings using automatic analysis of airflow and oximetry recordings obtained at patients’ home. Biomed Signal Process Control 2015. [DOI: 10.1016/j.bspc.2015.02.014] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/23/2022]
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31
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Assessment of Time and Frequency Domain Entropies to Detect Sleep Apnoea in Heart Rate Variability Recordings from Men and Women. ENTROPY 2015. [DOI: 10.3390/e17010123] [Citation(s) in RCA: 30] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/03/2023]
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32
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Gutiérrez-Tobal GC, Álvarez D, Gómez J, del Campo F, Hornero R. Assessment of spectral bands of interest in airflow signal to assist in 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 2013; 2013:5021-4. [PMID: 24110863 DOI: 10.1109/embc.2013.6610676] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Abstract
In this work, we analyze power spectral density (PSD) from single-channel airflow (AF) in the context of sleep apnea-hypopnea syndrome (SAHS) diagnosis. PSDs from SAHS-positive and SAHS-negative subjects were compared through Mann-Whitney test to find bands of interest. Thereby, we characterized three spectral bands (BW1-BW3) by their relative power (P(R1)-P(R3)) and established relationships with apneas and hypopneas. Then, the single and joint diagnostic ability of P(R1)-P(R3) was assessed by means of K-nearest neighbours (KNN), Fisher's linear discriminant (FLD), and logistic regression (LR) classifiers. The KNN and LR models, obtained from P(R1)-P(R3), showed the best diagnostic ability after a leave-one-out cross-validation procedure. 87.7%-84.2% accuracy and 0.799-0.853 area under receiver operating characteristics curve (AROC) were achieved, respectively. Our results suggest that the bands of interest we defined are related to apneas and hypopneas and, therefore, can be useful in SAHS diagnosis.
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33
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Pattern recognition in airflow recordings to assist in the sleep apnoea-hypopnoea syndrome diagnosis. Med Biol Eng Comput 2013; 51:1367-80. [PMID: 24057145 DOI: 10.1007/s11517-013-1109-7] [Citation(s) in RCA: 18] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2013] [Accepted: 09/01/2013] [Indexed: 10/26/2022]
Abstract
This paper aims at detecting sleep apnoea-hypopnoea syndrome (SAHS) from single-channel airflow (AF) recordings. The study involves 148 subjects. Our proposal is based on estimating the apnoea-hypopnoea index (AHI) after global analysis of AF, including the investigation of respiratory rate variability (RRV). We exhaustively characterize both AF and RRV by extracting spectral, nonlinear, and statistical features. Then, the fast correlation-based filter is used to select those relevant and non-redundant. Multiple linear regression, multi-layer perceptron (MLP), and radial basis functions are fed with the features to estimate AHI. A conventional approach, based on scoring apnoeas and hypopnoeas, is also assessed for comparison purposes. An MLP model trained with AF and RRV selected features achieved the highest agreement with the true AHI (intra-class correlation coefficient = 0.849). It also showed the highest diagnostic ability, reaching 92.5 % sensitivity, 89.5 % specificity and 91.5 % accuracy. This suggests that AF and RRV can complement each other to estimate AHI and help in SAHS diagnosis.
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34
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Alvarez D, Hornero R, Marcos JV, Wessel N, Penzel T, Glos M, Del Campo F. Assessment of feature selection and classification approaches to enhance information from overnight oximetry in the context of apnea diagnosis. Int J Neural Syst 2013; 23:1350020. [PMID: 23924411 DOI: 10.1142/s0129065713500202] [Citation(s) in RCA: 46] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/18/2023]
Abstract
This study is aimed at assessing the usefulness of different feature selection and classification methodologies in the context of sleep apnea hypopnea syndrome (SAHS) detection. Feature extraction, selection and classification stages were applied to analyze blood oxygen saturation (SaO2) recordings in order to simplify polysomnography (PSG), the gold standard diagnostic methodology for SAHS. Statistical, spectral and nonlinear measures were computed to compose the initial feature set. Principal component analysis (PCA), forward stepwise feature selection (FSFS) and genetic algorithms (GAs) were applied to select feature subsets. Fisher's linear discriminant (FLD), logistic regression (LR) and support vector machines (SVMs) were applied in the classification stage. Optimum classification algorithms from each combination of these feature selection and classification approaches were prospectively validated on datasets from two independent sleep units. FSFS + LR achieved the highest diagnostic performance using a small feature subset (4 features), reaching 83.2% accuracy in the validation set and 88.7% accuracy in the test set. Similarly, GAs + SVM also achieved high generalization capability using a small number of input features (7 features), with 84.2% accuracy on the validation set and 84.5% accuracy in the test set. Our results suggest that reduced subsets of complementary features (25% to 50% of total features) and classifiers with high generalization ability could provide high-performance screening tools in the context of SAHS.
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Affiliation(s)
- Daniel Alvarez
- Biomedical Engineering Group (GIB), University of Valladolid, Paseo Belén 15, 47011, Valladolid, Spain.
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