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Cairo B, Bari V, Gelpi F, De Maria B, Porta A. Assessing cardiorespiratory interactions via lagged joint symbolic dynamics during spontaneous and controlled breathing. FRONTIERS IN NETWORK PHYSIOLOGY 2023; 3:1211848. [PMID: 37602202 PMCID: PMC10436098 DOI: 10.3389/fnetp.2023.1211848] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/25/2023] [Accepted: 07/13/2023] [Indexed: 08/22/2023]
Abstract
Introduction: Joint symbolic analysis (JSA) can be utilized to describe interactions between time series while accounting for time scales and nonlinear features. JSA is based on the computation of the rate of occurrence of joint patterns built after symbolization. Lagged JSA (LJSA) is obtained from the more classical JSA by introducing a delay/lead between patterns built over the two series and combined to form the joint scheme, thus monitoring coordinated patterns at different lags. Methods: In the present study, we applied LJSA for the assessment of cardiorespiratory coupling (CRC) from heart period (HP) variability and respiratory activity (R) in 19 healthy subjects (age: 27-35 years; 8 males, 11 females) during spontaneous breathing (SB) and controlled breathing (CB). The R rate of CB was selected to be indistinguishable from that of SB, namely, 15 breaths·minute-1 (CB15), or slower than SB, namely, 10 breaths·minute-1 (CB10), but in both cases, very rapid interactions between heart rate and R were known to be present. The ability of the LJSA approach to follow variations of the coupling strength was tested over a unidirectionally or bidirectionally coupled stochastic process and using surrogate data to test the null hypothesis of uncoupling. Results: We found that: i) the analysis of surrogate data proved that HP and R were significantly coupled in any experimental condition, and coupling was not more likely to occur at a specific time lag; ii) CB10 reduced CRC strength at the fastest time scales while increasing that at intermediate time scales, thus leaving the overall CRC strength unvaried; iii) despite exhibiting similar R rates and respiratory sinus arrhythmia, SB and CB15 induced different cardiorespiratory interactions; iv) no dominant temporal scheme was observed with relevant contributions of HP patterns either leading or lagging R. Discussion: LJSA is a useful methodology to explore HP-R dynamic interactions while accounting for time shifts and scales.
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Affiliation(s)
- Beatrice Cairo
- Department of Biomedical Sciences for Health, University of Milan, Milan, Italy
| | - Vlasta Bari
- Department of Biomedical Sciences for Health, University of Milan, Milan, Italy
- Department of Cardiothoracic, Vascular Anesthesia and Intensive Care, IRCCS Policlinico San Donato Milanese, Milan, Italy
| | - Francesca Gelpi
- Department of Biomedical Sciences for Health, University of Milan, Milan, Italy
| | | | - Alberto Porta
- Department of Biomedical Sciences for Health, University of Milan, Milan, Italy
- Department of Cardiothoracic, Vascular Anesthesia and Intensive Care, IRCCS Policlinico San Donato Milanese, Milan, Italy
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Sentner T, Wang X, de Groot ER, van Schaijk L, Tataranno ML, Vijlbrief DC, Benders MJNL, Bartels R, Dudink J. The Sleep Well Baby project: an automated real-time sleep–wake state prediction algorithm in preterm infants. Sleep 2022; 45:6617657. [PMID: 35749799 PMCID: PMC9548667 DOI: 10.1093/sleep/zsac143] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2022] [Revised: 05/31/2022] [Indexed: 11/29/2022] Open
Abstract
Study Objectives Sleep is an important driver of early brain development. However, sleep is often disturbed in preterm infants admitted to the neonatal intensive care unit (NICU). We aimed to develop an automated algorithm based on routinely measured vital parameters to classify sleep–wake states of preterm infants in real-time at the bedside. Methods In this study, sleep–wake state observations were obtained in 1-minute epochs using a behavioral scale developed in-house while vital signs were recorded simultaneously. Three types of vital parameter data, namely, heart rate, respiratory rate, and oxygen saturation, were collected at a low-frequency sampling rate of 0.4 Hz. A supervised machine learning workflow was used to train a classifier to predict sleep–wake states. Independent training (n = 37) and validation datasets were validation n = 9) datasets were used. Finally, a setup was designed for real-time implementation at the bedside. Results The macro-averaged area-under-the-receiver-operator-characteristic (AUROC) of the automated sleep staging algorithm ranged between 0.69 and 0.82 for the training data, and 0.61 and 0.78 for the validation data. The algorithm provided the most accurate prediction for wake states (AUROC = 0.80). These findings were well validated on an independent sample (AUROC = 0.77). Conclusions With this study, to the best of our knowledge, a reliable, nonobtrusive, and real-time sleep staging algorithm was developed for the first time for preterm infants. Deploying this algorithm in the NICU environment may assist and adapt bedside clinical work based on infants’ sleep–wake states, potentially promoting the early brain development and well-being of preterm infants.
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Affiliation(s)
- Thom Sentner
- Digital Health, University Medical Center Utrecht , Utrecht , The Netherlands
| | - Xiaowan Wang
- Department of Neonatology, Wilhelmina Children’s Hospital, University Medical Center Utrecht , Utrecht , The Netherlands
| | - Eline R de Groot
- Department of Neonatology, Wilhelmina Children’s Hospital, University Medical Center Utrecht , Utrecht , The Netherlands
| | - Lieke van Schaijk
- Digital Health, University Medical Center Utrecht , Utrecht , The Netherlands
| | - Maria Luisa Tataranno
- Department of Neonatology, Wilhelmina Children’s Hospital, University Medical Center Utrecht , Utrecht , The Netherlands
- Brain Center Rudolf Magnus, University Medical Center Utrecht , Utrecht , The Netherlands
| | - Daniel C Vijlbrief
- Department of Neonatology, Wilhelmina Children’s Hospital, University Medical Center Utrecht , Utrecht , The Netherlands
| | - Manon J N L Benders
- Department of Neonatology, Wilhelmina Children’s Hospital, University Medical Center Utrecht , Utrecht , The Netherlands
- Brain Center Rudolf Magnus, University Medical Center Utrecht , Utrecht , The Netherlands
| | - Richard Bartels
- Digital Health, University Medical Center Utrecht , Utrecht , The Netherlands
| | - Jeroen Dudink
- Department of Neonatology, Wilhelmina Children’s Hospital, University Medical Center Utrecht , Utrecht , The Netherlands
- Brain Center Rudolf Magnus, University Medical Center Utrecht , Utrecht , The Netherlands
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Latremouille S, Lam J, Shalish W, Sant'Anna G. Neonatal heart rate variability: a contemporary scoping review of analysis methods and clinical applications. BMJ Open 2021; 11:e055209. [PMID: 34933863 PMCID: PMC8710426 DOI: 10.1136/bmjopen-2021-055209] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/21/2022] Open
Abstract
BACKGROUND Neonatal heart rate variability (HRV) is widely used as a research tool. However, HRV calculation methods are highly variable making it difficult for comparisons between studies. OBJECTIVES To describe the different types of investigations where neonatal HRV was used, study characteristics, and types of analyses performed. ELIGIBILITY CRITERIA Human neonates ≤1 month of corrected age. SOURCES OF EVIDENCE A protocol and search strategy of the literature was developed in collaboration with the McGill University Health Center's librarians and articles were obtained from searches in the Biosis, Cochrane, Embase, Medline and Web of Science databases published between 1 January 2000 and 1 July 2020. CHARTING METHODS A single reviewer screened for eligibility and data were extracted from the included articles. Information collected included the study characteristics and population, type of HRV analysis used (time domain, frequency domain, non-linear, heart rate characteristics (HRC) parameters) and clinical applications (physiological and pathological conditions, responses to various stimuli and outcome prediction). RESULTS Of the 286 articles included, 171 (60%) were small single centre studies (sample size <50) performed on term infants (n=136). There were 138 different types of investigations reported: physiological investigations (n=162), responses to various stimuli (n=136), pathological conditions (n=109) and outcome predictor (n=30). Frequency domain analyses were used in 210 articles (73%), followed by time domain (n=139), non-linear methods (n=74) or HRC analyses (n=25). Additionally, over 60 different measures of HRV were reported; in the frequency domain analyses alone there were 29 different ranges used for the low frequency band and 46 for the high frequency band. CONCLUSIONS Neonatal HRV has been used in diverse types of investigations with significant lack of consistency in analysis methods applied. Specific guidelines for HRV analyses in neonates are needed to allow for comparisons between studies.
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Affiliation(s)
- Samantha Latremouille
- Division of Experimental Medicine, McGill University Health Centre, Montreal, Québec, Canada
| | - Justin Lam
- Medicine, Griffith University, Nathan, Queensland, Australia
| | - Wissam Shalish
- Division of Neonatology, McGill University Health Center, Montreal, Québec, Canada
| | - Guilherme Sant'Anna
- Division of Neonatology, McGill University Health Center, Montreal, Québec, Canada
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Zuzarte I, Sternad D, Paydarfar D. Predicting apneic events in preterm infants using cardio-respiratory and movement features. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2021; 209:106321. [PMID: 34380078 PMCID: PMC8898595 DOI: 10.1016/j.cmpb.2021.106321] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/12/2021] [Accepted: 07/25/2021] [Indexed: 06/13/2023]
Abstract
BACKGROUND AND OBJECTIVE Preterm neonates are prone to episodes of apnea, bradycardia and hypoxia (ABH) that can lead to neurological morbidities or even death. There is broad interest in developing methods for real-time prediction of ABH events to inform interventions that prevent or reduce their incidence and severity. Using advances in machine learning methods, this study develops an algorithm to predict ABH events. METHODS Following previous studies showing that respiratory instabilities are closely associated with bouts of movement, we present a modeling framework that can predict ABH events using both movement and cardio-respiratory features derived from routine clinical recordings. In 10 preterm infants, movement onsets and durations were estimated with a wavelet-based algorithm that quantified artifactual distortions of the photoplethysmogram signal. For prediction, cardio-respiratory features were created from time-delayed correlations of inter-beat and inter-breath intervals with past values; movement features were derived from time-delayed correlations with inter-breath intervals. Gaussian Mixture Models and Logistic Regression were used to develop predictive models of apneic events. Performance of the models was evaluated with ROC curves. RESULTS Performance of the prediction framework (mean AUC) was 0.77 ± 0.04 for 66 ABH events on training data from 7 infants. When grouped by the severity of the associated bradycardia during the ABH event, the framework was able to predict 83% and 75% of the most severe episodes in the 7-infant training set and 3-infant test set, respectively. Notably, inclusion of movement features significantly improved the predictions compared with modeling with only cardio-respiratory signals. CONCLUSIONS Our findings suggest that recordings of movement provide important information for predicting ABH events in preterm infants, and can inform preemptive interventions designed to reduce the incidence and severity of ABH events.
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Affiliation(s)
- Ian Zuzarte
- Department of Bioengineering, Northeastern University, Boston, MA 02115, United States
| | - Dagmar Sternad
- Departments of Biology, Electrical and Computer Engineering & Physics, Northeastern University, Boston, MA 02115, United States
| | - David Paydarfar
- Department of Neurology, Dell Medical School, Austin, TX 78712, United States; Oden Institute for Computational Sciences and Engineering, The University of Texas at Austin, Austin, TX 78712, United States.
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5
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Prone sleeping affects cardiovascular control in preterm infants in NICU. Pediatr Res 2021; 90:197-204. [PMID: 33173173 DOI: 10.1038/s41390-020-01254-z] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/31/2020] [Revised: 09/28/2020] [Accepted: 10/06/2020] [Indexed: 11/08/2022]
Abstract
BACKGROUND Prone sleeping is used in preterm infants undergoing intensive care to improve respiratory function, but evidence suggests that this position may compromise autonomic cardiovascular control. To test this hypothesis, this study assessed the effects of the prone sleeping position on cardiovascular control in preterm infants undergoing intensive care treatment during early postnatal life. METHODS Fifty-six preterm infants, divided into extremely preterm (gestational age (GA) 24-28 weeks, n = 23) and very preterm (GA 29-34 weeks, n = 33) groups, were studied weekly for 3 weeks in prone and supine positions, during quiet and active sleep. Heart rate (HR) and non-invasive blood pressure (BP) were recorded and autonomic measures of HR variability (HRV), BP variability (BPV), and baroreflex sensitivity (BRS) using frequency analysis in low (LF) and high (HF) bands were assessed. RESULTS During the first 3 weeks, prone sleeping increased HR, reduced BRS, and increased HF BPV compared to supine. LF and HF HRV were also lower prone compared to supine in very preterm infants. Extremely preterm infants had the lowest HRV and BRS measures, and the highest HF BPV. CONCLUSIONS Prone sleeping dampens cardiovascular control in early postnatal life in preterm infants, having potential implications for BP regulation in infants undergoing intensive care.
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Joint symbolic dynamics identifies differences in the maternal-fetal cardiac coupling between nonlaboring and laboring women. Biomed Signal Process Control 2021. [DOI: 10.1016/j.bspc.2021.102769] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
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Zhang D, Long X, Xu L, Werth J, Wijshoff R, Aarts RM, Andriessen P. Characterizing cardiorespiratory interaction in preterm infants across sleep states using visibility graph analysis. J Appl Physiol (1985) 2021; 130:1015-1024. [PMID: 33539263 DOI: 10.1152/japplphysiol.00333.2020] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022] Open
Abstract
Cardiorespiratory interaction (CRI) has been intensively studied in adult sleep, yet not in preterm infants, in particular across different sleep states including wake (W), active sleep (AS), and quiet sleep (QS). The aim of this study was to quantify the interaction between cardiac and respiratory activities in different sleep states of preterm infants. The postmenstrual age (PMA) of preterm infants was also taken into consideration. The CRI during sleep was analyzed using a visibility graph (VG) method, enabling the nonlinear analysis of CRI in a complex network. For each sleep state, parameters quantifying various aspects of the CRI characteristics from constructed VG network including mean degree (Dm) and its variability (Dsd), clustering coefficient (CCm) and its variability (CCsd), assortativity coefficient (AC), and complexity (DSE) were extracted from the CRI networks. The interaction effect of sleep state and PMA was found to be statistically significant on all CRI parameters except for AC and DSE. The main effect between sleep state and CRI parameters was statistically significant except for CCm, and that between PMA and CRI parameters was statistically significant except for DSE. In conclusion, the CRI of preterm infants is associated with sleep states and PMA in general. For preterm infants with a larger PMA, CRI has a more clustered pattern during different sleep states, where QS shows a more regular, stratified, and stronger CRI than other states. In the future, these parameters can be potentially used to separate sleep states in preterm infants.NEW & NOTEWORTHY The interaction between cardiac and respiratory activities is investigated in preterm infant sleep using an advanced nonlinear method (visibility graph) and some important characteristics are shown to be significantly different across sleep states, which has not been studied before.
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Affiliation(s)
- Dandan Zhang
- Department of Electrical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands.,Philips Research, Eindhoven, The Netherlands
| | - Xi Long
- Department of Electrical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands.,Philips Research, Eindhoven, The Netherlands
| | - Lin Xu
- School of Information Science and Technology, ShanghaiTech University, Shanghai, China
| | - Jan Werth
- Department of Electrical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands
| | | | - Ronald M Aarts
- Department of Electrical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands
| | - Peter Andriessen
- Department of Neonatology, Máxima Medical Centre, Veldhoven, The Netherlands.,Department of Applied Physics, Eindhoven University of Technology, Eindhoven, The Netherlands
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8
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The value of cardiorespiratory parameters for sleep state classification in preterm infants: A systematic review. Sleep Med Rev 2021; 58:101462. [PMID: 33826975 DOI: 10.1016/j.smrv.2021.101462] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2020] [Revised: 02/24/2021] [Accepted: 03/03/2021] [Indexed: 11/23/2022]
Abstract
Cardiorespiratory activity is highly associated with infants' sleep duration and quality. We performed a systematic literature search of PubMed and EMBASE databases to investigate if and how cardiorespiratory parameters can be used for sleep state classification in preterm infants and in what way maturation influences this relation. All retrieved citations were screened against predetermined inclusion and exclusion criteria. Only studies of preterm infants (<37 wk postmenstrual age during sleep state classification) admitted to a neonatal ward and of whom at least one sleep state and one cardiorespiratory parameter was measured, were included. Two researchers independently reviewed the included studies on methodological quality. Of the 1097 initially retrieved studies, 23 were included for analysis. Heart rate and respiration frequency are strongly correlated with active sleep and quiet sleep. In quiet sleep, as compared to active sleep, respiratory frequency is more stable, and the heart rate is lower and less variable. This association, however, differed across preterm birth subtypes (i.e., extremely, very or late preterm), indicating that maturation - in the form of both gestational and postnatal age - influences the cardiorespiratory characteristics of preterm sleep states. The knowledge gained from this review can help improve behavioral sleep classification and automated sleep classification algorithms for preterm infants.
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Joshi R, Kommers D, Long X, Feijs L, Van Huffel S, van Pul C, Andriessen P. Cardiorespiratory coupling in preterm infants. J Appl Physiol (1985) 2018; 126:202-213. [PMID: 30382810 DOI: 10.1152/japplphysiol.00722.2018] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/26/2023] Open
Abstract
In preterm infants, a better understanding and quantification of cardiorespiratory coupling may help improve caregiving by enabling the tracking of maturational changes and subclinical signatures of disease. Therefore, in a study of 20 preterm infants admitted to a neonatal intensive care unit, we analyzed the cardiac and respiratory regulatory mechanisms as well as the coupling between them. In particular, we selectively analyzed coupling from changes in heart rate to respiratory oscillations as well as coupling from respiratory oscillations to the heart rate. Furthermore, we stratified this coupling based on decelerations and accelerations of the heart rate and by inspiration and expiration during respiration while contrasting periods of kangaroo care, an intervention known to enhance autonomic regulation, with periods in the incubator. We identified that preterm infants exhibit cardiorespiratory coupling that is nonsymmetric with regard to the direction of coupling. We demonstrate coupling from decelerations and accelerations of the heart rate to exhalation and inhalation, respectively, both on a beat-to-beat basis as well as with sustained decelerations and accelerations. On the other hand, on average, we also observed coupling from both inspiration and expiration to marginal decelerations in the heart rate. These phenomena, especially coupling from the changes in the heart rate to respiratory oscillations, were sensitive to whether the infant was receiving kangaroo care. NEW & NOTEWORTHY Preterm infants exhibit cardiorespiratory coupling that is nonsymmetric with regard to the direction of coupling; coupling from fluctuations in the heart rate to respiratory oscillations and vice versa are asymmetric. On average, coupling is observable from decelerations or accelerations in the heart rate to inhalation or exhalation, respectively, whereas, on average, both peaks and troughs of respiration exhibit coupling to marginal decelerations in the heart rate.
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Affiliation(s)
- Rohan Joshi
- Department of Industrial Design, Eindhoven University of Technology , Eindhoven , The Netherlands.,Department of Clinical Physics, Máxima Medical Centre , Veldhoven , The Netherlands.,Department of Fertility, Pregnancy and Parenting Solutions, Philips Research, Eindhoven , The Netherlands
| | - Deedee Kommers
- Department of Neonatology, Máxima Medical Centre , Veldhoven , The Netherlands.,Department of Applied Physics, Eindhoven University of Technology , Eindhoven , The Netherlands
| | - Xi Long
- Department of Fertility, Pregnancy and Parenting Solutions, Philips Research, Eindhoven , The Netherlands
| | - Loe Feijs
- Department of Industrial Design, Eindhoven University of Technology , Eindhoven , The Netherlands
| | - Sabine Van Huffel
- KU Leuven, Department of Electrical Engineering, Division Stadius, and IMEC, Leuven , Belgium
| | - Carola van Pul
- Department of Clinical Physics, Máxima Medical Centre , Veldhoven , The Netherlands.,Department of Applied Physics, Eindhoven University of Technology , Eindhoven , The Netherlands
| | - Peter Andriessen
- Department of Neonatology, Máxima Medical Centre , Veldhoven , The Netherlands
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Bennet L, Walker DW, Horne RSC. Waking up too early - the consequences of preterm birth on sleep development. J Physiol 2018; 596:5687-5708. [PMID: 29691876 DOI: 10.1113/jp274950] [Citation(s) in RCA: 63] [Impact Index Per Article: 10.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2017] [Accepted: 03/16/2017] [Indexed: 12/12/2022] Open
Abstract
Good quality sleep of sufficient duration is vital for optimal physiological function and our health. Sleep deprivation is associated with impaired neurocognitive function and emotional control, and increases the risk for cardiometabolic diseases, obesity and cancer. Sleep develops during fetal life with the emergence of a recognisable pattern of sleep states in the preterm fetus associated with the development, maturation and connectivity within neural networks in the brain. Despite the physiological importance of sleep, surprisingly little is known about how sleep develops in individuals born preterm. Globally, an estimated 15 million babies are born preterm (<37 weeks gestation) each year, and these babies are at significant risk of neural injury and impaired brain development. This review discusses how sleep develops during fetal and neonatal life, how preterm birth impacts on sleep development to adulthood, and the factors which may contribute to impaired brain and sleep development, leading to altered neurocognitive, behavioural and motor capabilities in the infant and child. Going forward, the challenge is to identify specific risk factors for impaired sleep development in preterm babies to allow for the design of interventions that will improve the quality and quantity of sleep throughout life.
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Affiliation(s)
- Laura Bennet
- Department of Physiology, The University of Auckland, Auckland, New Zealand
| | - David W Walker
- School of Health and Biomedical Sciences, RMIT University, Bundoora, Victoria, Australia
| | - Rosemary S C Horne
- The Ritchie Centre, Department of Paediatrics, Monash University and Hudson Institute of Medical Research, Melbourne, Victoria, Australia
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Werth J, Long X, Zwartkruis-Pelgrim E, Niemarkt H, Chen W, Aarts RM, Andriessen P. Unobtrusive assessment of neonatal sleep state based on heart rate variability retrieved from electrocardiography used for regular patient monitoring. Early Hum Dev 2017; 113:104-113. [PMID: 28733087 DOI: 10.1016/j.earlhumdev.2017.07.004] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
Abstract
As an approach of unobtrusive assessment of neonatal sleep state we aimed at an automated sleep state coding based only on heart rate variability obtained from electrocardiography used for regular patient monitoring. We analyzed active and quiet sleep states of preterm infants between 30 and 37weeks postmenstrual age. To determine the sleep states we used a nonlinear kernel support vector machine for sleep state separation based on known heart rate variability features. We used unweighted and weighted misclassification penalties for the imbalanced distribution between sleep states. The validation was performed with leave-one-out-cross-validation based on the annotations of three independent observers. We analyzed the classifier performance with receiver operating curves leading to a maximum mean value for the area under the curve of 0.87. Using this sleep state separation methods, we show that automated active and quiet sleep state separation based on heart rate variability in preterm infants is feasible.
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Affiliation(s)
- Jan Werth
- Department of Electrical Engineering, University of Technology Eindhoven, De Zaale, 5612 AJ, Eindhoven, The Netherlands; Philips Research, High Tech Campus 34, 5656 AE, Eindhoven, The Netherlands
| | - Xi Long
- Department of Electrical Engineering, University of Technology Eindhoven, De Zaale, 5612 AJ, Eindhoven, The Netherlands; Philips Research, High Tech Campus 34, 5656 AE, Eindhoven, The Netherlands.
| | | | - Hendrik Niemarkt
- Neonatal Intensive Care Unit, Maxima Medical Center, De Run 4600, 5504 DB, Veldhoven, The Netherlands
| | - Wei Chen
- Center for Intelligent Medical Electronics (CIME), School of Information Science and Technology, Department of Electronic Engineering, Shanghai Key Laboratory of Medical Imaging Computing and Computer Assisted Intervention, Fudan University, Shanghai 200433, China
| | - Ronald M Aarts
- Department of Electrical Engineering, University of Technology Eindhoven, De Zaale, 5612 AJ, Eindhoven, The Netherlands; Philips Research, High Tech Campus 34, 5656 AE, Eindhoven, The Netherlands
| | - Peter Andriessen
- Neonatal Intensive Care Unit, Maxima Medical Center, De Run 4600, 5504 DB, Veldhoven, The Netherlands; Faculty of Health, Medicine and Life Science, Maastricht University, Minderbroedersberg 4-6, 6211 LK Maastricht, The Netherlands.
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12
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Wang FT, Chan HL, Wang CL, Jian HM, Lin SH. Instantaneous Respiratory Estimation from Thoracic Impedance by Empirical Mode Decomposition. SENSORS 2015. [PMID: 26198231 PMCID: PMC4541883 DOI: 10.3390/s150716372] [Citation(s) in RCA: 26] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Impedance plethysmography provides a way to measure respiratory activity by sensing the change of thoracic impedance caused by inspiration and expiration. This measurement imposes little pressure on the body and uses the human body as the sensor, thereby reducing the need for adjustments as body position changes and making it suitable for long-term or ambulatory monitoring. The empirical mode decomposition (EMD) can decompose a signal into several intrinsic mode functions (IMFs) that disclose nonstationary components as well as stationary components and, similarly, capture respiratory episodes from thoracic impedance. However, upper-body movements usually produce motion artifacts that are not easily removed by digital filtering. Moreover, large motion artifacts disable the EMD to decompose respiratory components. In this paper, motion artifacts are detected and replaced by the data mirrored from the prior and the posterior before EMD processing. A novel intrinsic respiratory reconstruction index that considers both global and local properties of IMFs is proposed to define respiration-related IMFs for respiration reconstruction and instantaneous respiratory estimation. Based on the experiments performing a series of static and dynamic physical activates, our results showed the proposed method had higher cross correlations between respiratory frequencies estimated from thoracic impedance and those from oronasal airflow based on small window size compared to the Fourier transform-based method.
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Affiliation(s)
- Fu-Tai Wang
- Department of Electrical Engineering, Hwa Hsia University of Technology, 111, Gongzhuan Rd., Zhonghe, New Taipei City 23568, Taiwan.
| | - Hsiao-Lung Chan
- Department of Electrical Engineering, Chang Gung University, 259 Wenhwa 1st Road, Kweishan, Taoyuan 33302, Taiwan.
| | - Chun-Li Wang
- Department of Cardiology, Chang Gung Memorial Hospital, 5 Fu-Hsing Street, Kweishan, Taoyuan 33305, Taiwan.
| | - Hung-Ming Jian
- Department of Electrical Engineering, Chang Gung University, 259 Wenhwa 1st Road, Kweishan, Taoyuan 33302, Taiwan.
| | - Sheng-Hsiung Lin
- Department of Electrical Engineering, Chang Gung University, 259 Wenhwa 1st Road, Kweishan, Taoyuan 33302, Taiwan.
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Baumert M, Javorka M, Kabir M. Joint symbolic dynamics for the assessment of cardiovascular and cardiorespiratory interactions. PHILOSOPHICAL TRANSACTIONS. SERIES A, MATHEMATICAL, PHYSICAL, AND ENGINEERING SCIENCES 2015; 373:rsta.2014.0097. [PMID: 25548272 PMCID: PMC4281868 DOI: 10.1098/rsta.2014.0097] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/22/2023]
Abstract
Beat-to-beat variations in heart period provide information on cardiovascular control and are closely linked to variations in arterial pressure and respiration. Joint symbolic analysis of heart period, systolic arterial pressure and respiration allows for a simple description of their shared short-term dynamics that are governed by cardiac baroreflex control and cardiorespiratory coupling. In this review, we discuss methodology and research applications. Studies suggest that analysis of joint symbolic dynamics provides a powerful tool for identifying physiological and pathophysiological changes in cardiovascular and cardiorespiratory control.
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Affiliation(s)
- Mathias Baumert
- School of Electrical and Electronic Engineering, University of Adelaide, Adelaide, SA 5005, Australia
| | - Michal Javorka
- Department of Physiology, Jessenius Faculty of Medicine, Comenius University, Martin, Slovakia
| | - Muammar Kabir
- Knight Cardiovascular Institute, Oregon Health and Science University, Portland, OR 97239, USA
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The physiological determinants of sudden infant death syndrome. Respir Physiol Neurobiol 2013; 189:288-300. [PMID: 23735486 DOI: 10.1016/j.resp.2013.05.032] [Citation(s) in RCA: 67] [Impact Index Per Article: 6.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2013] [Revised: 05/19/2013] [Accepted: 05/27/2013] [Indexed: 01/08/2023]
Abstract
It is well-established that environmental and biological risk factors contribute to Sudden Infant Death Syndrome (SIDS). There is also growing consensus that SIDS requires the intersection of multiple risk factors that result in the failure of an infant to overcome cardio-respiratory challenges. Thus, the critical next steps in understanding SIDS are to unravel the physiological determinants that actually cause the sudden death, to synthesize how these determinants are affected by the known risk factors, and to develop novel ideas for SIDS prevention. In this review, we will examine current and emerging perspectives related to cardio-respiratory dysfunctions in SIDS. Specifically, we will review: (1) the role of the preBötzinger complex (preBötC) as a multi-functional network that is critically involved in the failure to adequately respond to hypoxic and hypercapnic challenges; (2) the potential involvement of the preBötC in the gender and age distributions that are characteristic for SIDS; (3) the link between SIDS and prematurity; and (4) the potential relationship between SIDS, auditory function, and central chemosensitivity. Each section underscores the importance of marrying the epidemiological and pathological data to experimental data in order to understand the physiological determinants of this syndrome. We hope that a better understanding will lead to novel ways to reduce the risk to succumb to SIDS.
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Macefield VG, Bornstein JC. Autonomic Neuroscience: articles of interest appearing in other Frontiers journals. Front Neurosci 2012; 6:184. [PMID: 23267313 PMCID: PMC3527993 DOI: 10.3389/fnins.2012.00184] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2012] [Accepted: 12/04/2012] [Indexed: 11/13/2022] Open
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