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Martín-González S, Ravelo-García AG, Navarro-Mesa JL, Hernández-Pérez E. Combining Heart Rate Variability and Oximetry to Improve Apneic Event Screening in Non-Desaturating Patients. SENSORS (BASEL, SWITZERLAND) 2023; 23:s23094267. [PMID: 37177472 PMCID: PMC10181515 DOI: 10.3390/s23094267] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/19/2022] [Revised: 04/17/2023] [Accepted: 04/18/2023] [Indexed: 05/15/2023]
Abstract
In this paper, we thoroughly analyze the detection of sleep apnea events in the context of Obstructive Sleep Apnea (OSA), which is considered a public health problem because of its high prevalence and serious health implications. We especially evaluate patients who do not always show desaturations during apneic episodes (non-desaturating patients). For this purpose, we use a database (HuGCDN2014-OXI) that includes desaturating and non-desaturating patients, and we use the widely used Physionet Apnea Dataset for a meaningful comparison with prior work. Our system combines features extracted from the Heart-Rate Variability (HRV) and SpO2, and it explores their potential to characterize desaturating and non-desaturating events. The HRV-based features include spectral, cepstral, and nonlinear information (Detrended Fluctuation Analysis (DFA) and Recurrence Quantification Analysis (RQA)). SpO2-based features include temporal (variance) and spectral information. The features feed a Linear Discriminant Analysis (LDA) classifier. The goal is to evaluate the effect of using these features either individually or in combination, especially in non-desaturating patients. The main results for the detection of apneic events are: (a) Physionet success rate of 96.19%, sensitivity of 95.74% and specificity of 95.25% (Area Under Curve (AUC): 0.99); (b) HuGCDN2014-OXI of 87.32%, 83.81% and 88.55% (AUC: 0.934), respectively. The best results for the global diagnosis of OSA patients (HuGCDN2014-OXI) are: success rate of 95.74%, sensitivity of 100%, and specificity of 89.47%. We conclude that combining both features is the most accurate option, especially when there are non-desaturating patterns among the recordings under study.
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Affiliation(s)
- Sofía Martín-González
- Institute for Technological Development and Innovation in Communications, Universidad de Las Palmas de Gran Canaria, 35017 Las Palmas de Gran Canaria, Spain
| | - Antonio G Ravelo-García
- Institute for Technological Development and Innovation in Communications, Universidad de Las Palmas de Gran Canaria, 35017 Las Palmas de Gran Canaria, Spain
- Interactive Technologies Institute (ITI/LARSyS and ARDITI), 9020-105 Funchal, Portugal
| | - Juan L Navarro-Mesa
- Institute for Technological Development and Innovation in Communications, Universidad de Las Palmas de Gran Canaria, 35017 Las Palmas de Gran Canaria, Spain
| | - Eduardo Hernández-Pérez
- Institute for Technological Development and Innovation in Communications, Universidad de Las Palmas de Gran Canaria, 35017 Las Palmas de Gran Canaria, Spain
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Dell’Aquila CR, Cañadas GE, Laciar E. A New Algorithm to Score Apnea/Hypopnea Events based on Respiratory Effort Signal and Oximeter Sensors. J Med Biol Eng 2020. [DOI: 10.1007/s40846-020-00549-0] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/28/2022]
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de Chazal P, Sutherland K, Cistulli PA. Advanced polysomnographic analysis for OSA: A pathway to personalized management? Respirology 2019; 25:251-258. [PMID: 31038827 DOI: 10.1111/resp.13564] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2019] [Accepted: 03/11/2019] [Indexed: 12/15/2022]
Abstract
Obstructive sleep apnea (OSA) is a highly heterogeneous disorder, with diverse pathways to disease, expression of disease, susceptibility to co-morbidities and response to therapy, and is ideally suited to precision medicine approaches. Clinically, the content of the information-rich polysomnogram (PSG) is not currently fully utilized in determining patient management. Novel PSG parameters such as hypoxic burden, pulse transit time, cardiopulmonary coupling and the frequency representations of PSG sensor signals could predict a variety of cardiovascular disease, cancer and neurodegeneration co-morbidities. The PSG can also be used to identify key pathophysiological parameters such as loop gain, arousal threshold and muscle compensation which can enhance understanding of the causes of OSA in an individual, and thereby guide choices on therapy. Machine learning methods performing their own parameter extraction coupled with large PSG data sets offer an exciting opportunity for discovering new links between the PSG variables and disease outcomes. By exploiting existing and emerging analytical methods, the PSG may offer a pathway to personalized management for OSA.
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Affiliation(s)
- Philip de Chazal
- Charles Perkins Centre, Faculty of Engineering and I.T., University of Sydney, Sydney, NSW, Australia
| | - Kate Sutherland
- Charles Perkins Centre and Northern Clinical School, Faculty of Medicine and Health, University of Sydney, Sydney, NSW, Australia
| | - Peter A Cistulli
- Charles Perkins Centre and Northern Clinical School, Faculty of Medicine and Health, University of Sydney, Sydney, NSW, Australia
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Deviaene M, Testelmans D, Buyse B, Borzee P, Van Huffel S, Varon C. Automatic Screening of Sleep Apnea Patients Based on the SpO2 Signal. IEEE J Biomed Health Inform 2019; 23:607-617. [DOI: 10.1109/jbhi.2018.2817368] [Citation(s) in RCA: 31] [Impact Index Per Article: 6.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Affiliation(s)
- Margot Deviaene
- Department of Electrical Engineering-ESAT, STADIUS Center for Dynamical Systems, Signal Processing and Data Analytics, and imec, KU Leuven, Leuven, Belgium
| | | | - Bertien Buyse
- Department of Pneumology, UZ Leuven, Leuven, Belgium
| | - Pascal Borzee
- Department of Pneumology, UZ Leuven, Leuven, Belgium
| | - Sabine Van Huffel
- Department of Electrical Engineering-ESAT, STADIUS Center for Dynamical Systems, Signal Processing and Data Analytics, and imec, KU Leuven, Leuven, Belgium
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Chazal PD, Jayawardhana M, Sadr N. Optimising the Apnoea Classification Performance of a Neural Network Classifier Processing ECG-Oximetry Signals. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2018; 2018:6026-6029. [PMID: 30441710 DOI: 10.1109/embc.2018.8513626] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
In this paper we investigate using principal components analysis to optimize the performance of a neural network system processing simultaneously acquired electrocardiogram (ECG) and oximetry signals. The algorithm identifies epochs of normal breathing, central apnoea (CA), and obstructive apnoea (OA) by processing a pooled feature set containing information capturing the desaturations from the oximeter sensor as well as time and spectral features from the ECG. Training and testing of the system was facilitated with a dataset of 125 scored polysomnogram recordings with accompanying respiratory event annotations. When classifying the three epoch types, our system achieved a specificity of 91%, a sensitivity to CA of 28% and sensitivity to OA of 63%. A sensitivity of 81% was achieved when the CA and OA epochs were combined into one class.
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Mazzotti DR, Lim DC, Sutherland K, Bittencourt L, Mindel JW, Magalang U, Pack AI, de Chazal P, Penzel T. Opportunities for utilizing polysomnography signals to characterize obstructive sleep apnea subtypes and severity. Physiol Meas 2018; 39:09TR01. [PMID: 30047487 DOI: 10.1088/1361-6579/aad5fe] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
Abstract
BACKGROUND Obstructive sleep apnea (OSA) is a heterogeneous sleep disorder with many pathophysiological pathways to disease. Currently, the diagnosis and classification of OSA is based on the apnea-hypopnea index, which poorly correlates to underlying pathology and clinical consequences. A large number of in-laboratory sleep studies are performed around the world every year, already collecting an enormous amount of physiological data within an individual. Clinically, we have not yet fully taken advantage of this data, but combined with existing analytical approaches, we have the potential to transform the way OSA is managed within an individual patient. Currently, respiratory signals are used to count apneas and hypopneas, but patterns such as inspiratory flow signals can be used to predict optimal OSA treatment. Electrocardiographic data can reveal arrhythmias, but patterns such as heart rate variability can also be used to detect and classify OSA. Electroencephalography is used to score sleep stages and arousals, but specific patterns such as the odds-ratio product can be used to classify how OSA patients responds differently to arousals. OBJECTIVE In this review, we examine these and many other existing computer-aided polysomnography signal processing algorithms and how they can reflect an individual's manifestation of OSA. SIGNIFICANCE Together with current technological advance, it is only a matter of time before advanced automatic signal processing and analysis is widely applied to precision medicine of OSA in the clinical setting.
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Affiliation(s)
- Diego R Mazzotti
- Center for Sleep and Circadian Neurobiology, University of Pennsylvania, Philadelphia, United States of America
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Jayawardhana M, de Chazal P. Enhanced detection of sleep apnoea using heart-rate, respiration effort and oxygen saturation derived from a photoplethysmography sensor. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2018; 2017:121-124. [PMID: 29059825 DOI: 10.1109/embc.2017.8036777] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Abstract
This paper presents a study on identifying sleep apnoea using the photoplethysmography (PPG) measurements, which is obtained from the SpO2 sensor. Using a database of polysomnogram (PSG) records of 52 patients, the heart rate and breathing effort information was derived from the PPG measurements and then features are extracted and processed by a classifier to detect one-minute epochs of sleep apnoea. The ground truth labels for the epochs were determined by trained technicians using the full PSG signal. Pulse oximetry (SpO2) measurements from the same sensor are also used in the classification process for comparison and in combination with the PPG results. The results show that both the heart rate and respiratory effort information derived from the PPG signal were able to detect apnoeic epochs with some success. The best classification performance of 87% for correctly labelling the epochs was obtained when the SpO2 features and the PPG features were combined.
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Hua CC, Yu CC. Detrended Fluctuation Analysis of Oxyhemoglobin Saturation by Pulse Oximetry in Sleep Apnea Syndrome. J Med Biol Eng 2017. [DOI: 10.1007/s40846-017-0251-3] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
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Penzel T, Kantelhardt JW, Bartsch RP, Riedl M, Kraemer JF, Wessel N, Garcia C, Glos M, Fietze I, Schöbel C. Modulations of Heart Rate, ECG, and Cardio-Respiratory Coupling Observed in Polysomnography. Front Physiol 2016; 7:460. [PMID: 27826247 PMCID: PMC5078504 DOI: 10.3389/fphys.2016.00460] [Citation(s) in RCA: 99] [Impact Index Per Article: 12.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2016] [Accepted: 09/23/2016] [Indexed: 11/13/2022] Open
Abstract
The cardiac component of cardio-respiratory polysomnography is covered by ECG and heart rate recordings. However, their evaluation is often underrepresented in summarizing reports. As complements to EEG, EOG, and EMG, these signals provide diagnostic information for autonomic nervous activity during sleep. This review presents major methodological developments in sleep research regarding heart rate, ECG, and cardio-respiratory couplings in a chronological (historical) sequence. It presents physiological and pathophysiological insights related to sleep medicine obtained by new technical developments. Recorded nocturnal ECG facilitates conventional heart rate variability (HRV) analysis, studies of cyclical variations of heart rate, and analysis of ECG waveform. In healthy adults, the autonomous nervous system is regulated in totally different ways during wakefulness, slow-wave sleep, and REM sleep. Analysis of beat-to-beat heart-rate variations with statistical methods enables us to estimate sleep stages based on the differences in autonomic nervous system regulation. Furthermore, up to some degree, it is possible to track transitions from wakefulness to sleep by analysis of heart-rate variations. ECG and heart rate analysis allow assessment of selected sleep disorders as well. Sleep disordered breathing can be detected reliably by studying cyclical variation of heart rate combined with respiration-modulated changes in ECG morphology (amplitude of R wave and T wave).
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Affiliation(s)
- Thomas Penzel
- Interdisziplinäres Schlafmedizinisches Zentrum, Charité – Universitätsmedizin BerlinBerlin, Germany
- International Clinical Research Center, St. Anne's University Hospital BrnoBrno, Czech Republic
| | - Jan W. Kantelhardt
- Naturwissenschaftliche Fakultät II – Chemie, Physik und Mathematik, Institut für Physik, Martin-Luther Universität Halle-WittenbergHalle, Germany
- Kardiovaskuläre Physik, Arbeitsgruppe Nichtlineare Dynamik, Fachbereich Physik, Humboldt-Universität BerlinBerlin, Germany
| | | | - Maik Riedl
- Kardiovaskuläre Physik, Arbeitsgruppe Nichtlineare Dynamik, Fachbereich Physik, Humboldt-Universität BerlinBerlin, Germany
| | - Jan F. Kraemer
- Kardiovaskuläre Physik, Arbeitsgruppe Nichtlineare Dynamik, Fachbereich Physik, Humboldt-Universität BerlinBerlin, Germany
| | - Niels Wessel
- Kardiovaskuläre Physik, Arbeitsgruppe Nichtlineare Dynamik, Fachbereich Physik, Humboldt-Universität BerlinBerlin, Germany
| | - Carmen Garcia
- Interdisziplinäres Schlafmedizinisches Zentrum, Charité – Universitätsmedizin BerlinBerlin, Germany
| | - Martin Glos
- Interdisziplinäres Schlafmedizinisches Zentrum, Charité – Universitätsmedizin BerlinBerlin, Germany
| | - Ingo Fietze
- Interdisziplinäres Schlafmedizinisches Zentrum, Charité – Universitätsmedizin BerlinBerlin, Germany
| | - Christoph Schöbel
- Interdisziplinäres Schlafmedizinisches Zentrum, Charité – Universitätsmedizin BerlinBerlin, Germany
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de Chazal P, Sadr N, Jayawardhana M. An ECG oximetry system for identifying obstructive and central apnoea events. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2016; 2015:7671-4. [PMID: 26738069 DOI: 10.1109/embc.2015.7320169] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
An automatic algorithm for processing simultaneously acquired electrocardiogram (ECG) and oximetry signals that identifies epochs of pure central apnoea, epochs containing obstructive apnoea and epochs of normal breathing is presented. The algorithm uses time and spectral features from the ECG derived heart-rate and respiration information, as well as features capturing desaturations from the oximeter sensor. Evaluation of performance of the system was achieved by using leave-one-record-out cross validation on the St. Vincent's University Hospital / University College Dublin Sleep Apnea Database from the Physionet collections of recorded physiologic signals. When classifying the three epoch types, our system achieved a specificity of 80%, a sensitivity to central apnoea of 44% and sensitivity to obstructive apnoea of 35%. A sensitivity of 81% was achieved when the central and obstructive epochs were combined into one class.
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11
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Herzfrequenz und EKG in der Polysomnographie. SOMNOLOGIE 2015. [DOI: 10.1007/s11818-015-0014-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/23/2022]
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Wu MN, Lai CL, Liu CK, Liou LM, Yen CW, Chen SCJ, Hsieh CF, Hsieh SW, Lin FC, Hsu CY. More severe hypoxemia is associated with better subjective sleep quality in obstructive sleep apnea. BMC Pulm Med 2015; 15:117. [PMID: 26459357 PMCID: PMC4604104 DOI: 10.1186/s12890-015-0112-1] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2014] [Accepted: 09/28/2015] [Indexed: 11/12/2022] Open
Abstract
Background Perceived sleep quality may play an important role in diagnosis and therapy for obstructive sleep apnea (OSA). However, few studies have assessed factors that are associated with perceived sleep quality in OSA patients. Hypoxemia depresses the central nervous system and attenuates the perceived respiratory load in asthmatic patients. This study aimed to investigate the factors related to perceived sleep quality, focusing on the role of hypoxemia. Methods Polysomnography studies of 156 OSA patients were reviewed. Traditional polysomnographic parameters, including parameters of oxy-hemoglobin saturation (SpO2), were calculated, and the sleep questionnaire and scales were used. Considering the possible pitfalls of absolute values of SpO2 and individualized responses to hypoxemia, the amplitude of desaturation was further computed as “median SpO2 minus lowest 5 % SpO2 “and “highest 5 % SpO2 minus median 5 % SpO2”. Correlations between these parameters and perceived sleep quality, represented as the Pittsburgh sleep quality index (PSQI), were performed. Multiple linear regression analysis was also conducted to investigate the factors associated with the PSQI. Results Although the PSQI was not correlated with the apnea-hypopnea index (r = −0.113, p = 0.162) and oxygen desaturation index (r = −0.085, p = 0.291), the PSQI was negatively correlated with “median SpO2 minus lowest 5 % SpO2” (r = −0.161, p = 0.045). After adjusting for age, total sleep time, the periodic limb movements index, tendency of depression, and the lowest 5 % SpO2, the “median SpO2 minus lowest SpO2” was still a significant predictor for a lower PSQI (β = −0.357, p = 0.015). Conclusions More severe hypoxemia is associated with better perceived sleep quality among OSA patients. This paradox may be associated with hypoxemia-related impairment of perception. The effect of hypoxemia did not appear to be significant in relatively mild hypoxemia but become significant in severe hypoxemia.” Median SpO2 minus lowest 5 % SpO2” may also be a better predictor of perceived sleep quality than the apnea-hypopnea index because of the disproportionate effects of hypoxemia. Additionally, further studies are necessary to confirm the role of hypoxemia on perceived sleep quality and identify the possible threshold of hypoxemia in OSA patients.
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Affiliation(s)
- Meng-Ni Wu
- Department of Neurology, Kaohsiung Medical University Hospital, Kaohsiung, Taiwan. .,Department of Master's Program in Neurology, Faculty of Medicine, College of Medicine, Kaohsiung Medical University, No. 100 Tzyou 1st Road, Kaohsiung, 807, Taiwan.
| | - Chiou-Lian Lai
- Department of Neurology, Kaohsiung Medical University Hospital, Kaohsiung, Taiwan. .,Department of Master's Program in Neurology, Faculty of Medicine, College of Medicine, Kaohsiung Medical University, No. 100 Tzyou 1st Road, Kaohsiung, 807, Taiwan.
| | - Ching-Kuan Liu
- Department of Neurology, Kaohsiung Medical University Hospital, Kaohsiung, Taiwan. .,Department of Master's Program in Neurology, Faculty of Medicine, College of Medicine, Kaohsiung Medical University, No. 100 Tzyou 1st Road, Kaohsiung, 807, Taiwan.
| | - Li-Min Liou
- Department of Neurology, Kaohsiung Medical University Hospital, Kaohsiung, Taiwan. .,Department of Master's Program in Neurology, Faculty of Medicine, College of Medicine, Kaohsiung Medical University, No. 100 Tzyou 1st Road, Kaohsiung, 807, Taiwan. .,Department of Neurology, Kaohsiung Municipal Hsiao-Kang Hospital, No. 482 Shanming Rd., Siaogang Dist., Kaohsiung City, 812, Taiwan.
| | - Chen-Wen Yen
- Department of Mechanical and Electro-mechanical Engineering, National Sun Yat-Sen University, No. 70 Lienhai Rd., Kaohsiung, 80424, Taiwan.
| | - Sharon Chia-Ju Chen
- Department of Medical Imaging and Radiation Sciences, Kaohsiung Medical University, No. 100 Tzyou 1st Road, Kaohsiung, 807, Taiwan.
| | - Cheng-Fang Hsieh
- Department of Neurology, Kaohsiung Medical University Hospital, Kaohsiung, Taiwan. .,Division of Geriatrics and Gerontology, Department of Internal Medicine, Kaohsiung Medical University Hospital, Kaohsiung, Taiwan. .,Graduate Institute of Medicine, College of Medicine, Kaohsiung Medical University, No. 100, Tzyou 1st Road, Kaohsiung, 807, Taiwan.
| | - Sun-Wung Hsieh
- Department of Master's Program in Neurology, Faculty of Medicine, College of Medicine, Kaohsiung Medical University, No. 100 Tzyou 1st Road, Kaohsiung, 807, Taiwan. .,Department of Neurology, Kaohsiung Municipal Hsiao-Kang Hospital, No. 482 Shanming Rd., Siaogang Dist., Kaohsiung City, 812, Taiwan.
| | - Feng-Cheng Lin
- Department of Neurology, Kaohsiung Medical University Hospital, Kaohsiung, Taiwan. .,Department of Health, Pingtung Hospital, No. 270, Ziyou Rd., Pingtung City, Pingtung County, 900, Taiwan.
| | - Chung-Yao Hsu
- Department of Neurology, Kaohsiung Medical University Hospital, Kaohsiung, Taiwan. .,Department of Master's Program in Neurology, Faculty of Medicine, College of Medicine, Kaohsiung Medical University, No. 100 Tzyou 1st Road, Kaohsiung, 807, Taiwan.
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Park JU, Lee HK, Lee J, Urtnasan E, Kim H, Lee KJ. Automatic classification of apnea/hypopnea events through sleep/wake states and severity of SDB from a pulse oximeter. Physiol Meas 2015; 36:2009-25. [DOI: 10.1088/0967-3334/36/9/2009] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
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Development of a screening tool for sleep disordered breathing in children using the phone Oximeter™. PLoS One 2014; 9:e112959. [PMID: 25401696 PMCID: PMC4234680 DOI: 10.1371/journal.pone.0112959] [Citation(s) in RCA: 79] [Impact Index Per Article: 7.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/02/2014] [Accepted: 10/13/2014] [Indexed: 11/24/2022] Open
Abstract
Background Sleep disordered breathing (SDB) can lead to daytime sleepiness, growth failure and developmental delay in children. Polysomnography (PSG), the gold standard to diagnose SDB, is a highly resource-intensive test, confined to the sleep laboratory. Aim To combine the blood oxygen saturation (SpO2) characterization and cardiac modulation, quantified by pulse rate variability (PRV), to identify children with SDB using the Phone Oximeter, a device integrating a pulse oximeter with a smartphone. Methods Following ethics approval and informed consent, 160 children referred to British Columbia Children's Hospital for overnight PSG were recruited. A second pulse oximeter sensor applied to the finger adjacent to the one used for standard PSG was attached to the Phone Oximeter to record overnight pulse oximetry (SpO2 and photoplethysmogram (PPG)) alongside the PSG. Results We studied 146 children through the analysis of the SpO2 pattern, and PRV as an estimate of heart rate variability calculated from the PPG. SpO2 variability and SpO2 spectral power at low frequency, was significantly higher in children with SDB due to the modulation provoked by airway obstruction during sleep (p-value ). PRV analysis reflected a significant augmentation of sympathetic activity provoked by intermittent hypoxia in SDB children. A linear classifier was trained with the most discriminating features to identify children with SDB. The classifier was validated with internal and external cross-validation, providing a high negative predictive value (92.6%) and a good balance between sensitivity (88.4%) and specificity (83.6%). Combining SpO2 and PRV analysis improved the classification performance, providing an area under the receiver operating characteristic curve of 88%, beyond the 82% achieved using SpO2 analysis alone. Conclusions These results demonstrate that the implementation of this algorithm in the Phone Oximeter will provide an improved portable, at-home screening tool, with the capability of monitoring patients over multiple nights.
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Schlotthauer G, Di Persia LE, Larrateguy LD, Milone DH. Screening of obstructive sleep apnea with empirical mode decomposition of pulse oximetry. Med Eng Phys 2014; 36:1074-80. [PMID: 24931493 DOI: 10.1016/j.medengphy.2014.05.008] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2013] [Revised: 04/25/2014] [Accepted: 05/11/2014] [Indexed: 10/25/2022]
Abstract
Detection of desaturations on the pulse oximetry signal is of great importance for the diagnosis of sleep apneas. Using the counting of desaturations, an index can be built to help in the diagnosis of severe cases of obstructive sleep apnea-hypopnea syndrome. It is important to have automatic detection methods that allows the screening for this syndrome, reducing the need of the expensive polysomnography based studies. In this paper a novel recognition method based on the empirical mode decomposition of the pulse oximetry signal is proposed. The desaturations produce a very specific wave pattern that is extracted in the modes of the decomposition. Using this information, a detector based on properly selected thresholds and a set of simple rules is built. The oxygen desaturation index constructed from these detections produces a detector for obstructive sleep apnea-hypopnea syndrome with high sensitivity (0.838) and specificity (0.855) and yields better results than standard desaturation detection approaches.
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Affiliation(s)
- Gastón Schlotthauer
- Lab. of Signals and Nonlinear Dynamics, Facultad de Ingeniería, Universidad Nacional de Entre Ríos, Argentina; National Council of Scientific and Technical Research (CONICET), Argentina.
| | - Leandro E Di Persia
- Research Center for Signals, Systems and Computational Intelligence (sinc(i)), Facultad de Ingeniería y Ciencias Hídricas, Universidad Nacional del Litoral, Argentina; National Council of Scientific and Technical Research (CONICET), Argentina
| | | | - Diego H Milone
- Research Center for Signals, Systems and Computational Intelligence (sinc(i)), Facultad de Ingeniería y Ciencias Hídricas, Universidad Nacional del Litoral, Argentina; National Council of Scientific and Technical Research (CONICET), Argentina
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Jobert M, Wilson FJ, Roth T, Ruigt GSF, Anderer P, Drinkenburg WHIM, Bes FW, Brunovsky M, Danker-Hopfe H, Freeman J, van Gerven JMA, Gruber G, Kemp B, Klösch G, Ma J, Penzel T, Peterson BT, Schulz H, Staner L, Saletu B, Svetnik V. Guidelines for the recording and evaluation of pharmaco-sleep studies in man: the International Pharmaco-EEG Society (IPEG). Neuropsychobiology 2014; 67:127-67. [PMID: 23548759 DOI: 10.1159/000343449] [Citation(s) in RCA: 38] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/07/2012] [Accepted: 11/26/2012] [Indexed: 01/19/2023]
Abstract
The International Pharmaco-EEG Society (IPEG) presents guidelines summarising the requirements for the recording and computerised evaluation of pharmaco-sleep data in man. Over the past years, technical and data-processing methods have advanced steadily, thus enhancing data quality and expanding the palette of sleep assessment tools that can be used to investigate the activity of drugs on the central nervous system (CNS), determine the time course of effects and pharmacodynamic properties of novel therapeutics, hence enabling the study of the pharmacokinetic/pharmacodynamic relationship, and evaluate the CNS penetration or toxicity of compounds. However, despite the presence of robust guidelines on the scoring of polysomnography -recordings, a review of the literature reveals inconsistent -aspects in the operating procedures from one study to another. While this fact does not invalidate results, the lack of standardisation constitutes a regrettable shortcoming, especially in the context of drug development programmes. The present guidelines are intended to assist investigators, who are using pharmaco-sleep measures in clinical research, in an effort to provide clear and concise recommendations and thereby to standardise methodology and facilitate comparability of data across laboratories.
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Affiliation(s)
- Marc Jobert
- International Pharmaco-EEG Society, Berlin, Germany.
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Almazaydeh L, Elleithy K, Faezipour M, Abushakra A. Apnea Detection based on Respiratory Signal Classification. ACTA ACUST UNITED AC 2013. [DOI: 10.1016/j.procs.2013.09.041] [Citation(s) in RCA: 32] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
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Marcos JV, Hornero R, Álvarez D, Aboy M, Del Campo F. Automated Prediction of the Apnea-Hypopnea Index from Nocturnal Oximetry Recordings. IEEE Trans Biomed Eng 2012; 59:141-9. [DOI: 10.1109/tbme.2011.2167971] [Citation(s) in RCA: 43] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
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Maier C, Wenz H, Dickhaus H. Steps toward subject-specific classification in ECG-based detection of sleep apnea. Physiol Meas 2011; 32:1807-19. [DOI: 10.1088/0967-3334/32/11/s07] [Citation(s) in RCA: 9] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
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Bravi A, Longtin A, Seely AJE. Review and classification of variability analysis techniques with clinical applications. Biomed Eng Online 2011; 10:90. [PMID: 21985357 PMCID: PMC3224455 DOI: 10.1186/1475-925x-10-90] [Citation(s) in RCA: 143] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2011] [Accepted: 10/10/2011] [Indexed: 11/20/2022] Open
Abstract
Analysis of patterns of variation of time-series, termed variability analysis, represents a rapidly evolving discipline with increasing applications in different fields of science. In medicine and in particular critical care, efforts have focussed on evaluating the clinical utility of variability. However, the growth and complexity of techniques applicable to this field have made interpretation and understanding of variability more challenging. Our objective is to provide an updated review of variability analysis techniques suitable for clinical applications. We review more than 70 variability techniques, providing for each technique a brief description of the underlying theory and assumptions, together with a summary of clinical applications. We propose a revised classification for the domains of variability techniques, which include statistical, geometric, energetic, informational, and invariant. We discuss the process of calculation, often necessitating a mathematical transform of the time-series. Our aims are to summarize a broad literature, promote a shared vocabulary that would improve the exchange of ideas, and the analyses of the results between different studies. We conclude with challenges for the evolving science of variability analysis.
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Affiliation(s)
- Andrea Bravi
- Department of Cellular and Molecular Medicine, University of Ottawa, Ottawa, Ontario, Canada.
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Biosignalverarbeitung. BIOMED ENG-BIOMED TE 2010. [DOI: 10.1515/bmt.2010.705] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
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Laguna P, Sörnmo L. Introduction. Editorial on 'Signal processing in vital rhythms and signs'. PHILOSOPHICAL TRANSACTIONS. SERIES A, MATHEMATICAL, PHYSICAL, AND ENGINEERING SCIENCES 2009; 367:207-211. [PMID: 18936018 DOI: 10.1098/rsta.2008.0239] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/26/2023]
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