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Yang M, Peng Z, van Pul C, Andriessen P, Dong K, Silvertand D, Li J, Liu C, Long X. Continuous prediction and clinical alarm management of late-onset sepsis in preterm infants using vital signs from a patient monitor. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2024; 255:108335. [PMID: 39047574 DOI: 10.1016/j.cmpb.2024.108335] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/11/2024] [Revised: 06/14/2024] [Accepted: 07/17/2024] [Indexed: 07/27/2024]
Abstract
BACKGROUND AND OBJECTIVE Continuous prediction of late-onset sepsis (LOS) could be helpful for improving clinical outcomes in neonatal intensive care units (NICU). This study aimed to develop an artificial intelligence (AI) model for assisting the bedside clinicians in successfully identifying infants at risk for LOS using non-invasive vital signs monitoring. METHODS In a retrospective study from the NICU of the Máxima Medical Center in Veldhoven, the Netherlands, a total of 492 preterm infants less than 32 weeks gestation were included between July 2016 and December 2018. Data on heart rate (HR), respiratory rate (RR), and oxygen saturation (SpO2) at 1 Hz were extracted from the patient monitor. We developed multiple AI models using 102 extracted features or raw time series to provide hourly LOS risk prediction. Shapley values were used to explain the model. For the best performing model, the effect of different vital signs and also the input type of signals on model performance was tested. To further assess the performance of applying the best performing model in a real-world clinical setting, we performed a simulation using four different alarm policies on continuous real-time predictions starting from three days after birth. RESULTS A total of 51 LOS patients and 68 controls were finally included according to the patient inclusion and exclusion criteria. When tested by seven-fold cross-validations, the mean (standard deviation) area under the receiver operating characteristic curve (AUC) six hours before CRASH was 0.875 (0.072) for the best performing model, compared to the other six models with AUC ranging from 0.782 (0.089) to 0.846 (0.083). The best performing model performed only slightly worse than the model learning from raw physiological waveforms (0.886 [0.068]), successfully detecting 96.1 % of LOS patients before CRASH. When setting the expected alarm window to 24 h and using a multi-threshold alarm policy, the sensitivity metric was 71.6 %, while the positive predictive value was 9.9 %, resulting in an average of 1.15 alarms per day per patient. CONCLUSIONS The proposed AI model, which learns from routinely collected vital signs, has the potential to assist clinicians in the early detection of LOS. Combined with interpretability and clinical alarm management, this model could be better translated into medical practice for future clinical implementation.
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Affiliation(s)
- Meicheng Yang
- State Key Laboratory of Digital Medical Engineering, School of Instrument Science and Engineering, Southeast University, Nanjing, China; Department of Electrical Engineering, Eindhoven University of Technology, Eindhoven, the Netherlands
| | - Zheng Peng
- Department of Electrical Engineering, Eindhoven University of Technology, Eindhoven, the Netherlands; Department of Clinical Physics, Máxima Medical Centre, Veldhoven, the Netherlands
| | - Carola van Pul
- Department of Electrical Engineering, Eindhoven University of Technology, Eindhoven, the Netherlands; 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 Applied Physics, Eindhoven University of Technology, Eindhoven, the Netherlands; Department of Pediatrics, Máxima Medical Centre, Veldhoven, the Netherlands
| | - Kejun Dong
- Nell Hodgson Woodruff School of Nursing, Emory University, Atlanta, United States of America
| | - Demi Silvertand
- Department of Pediatrics, Máxima Medical Centre, Veldhoven, the Netherlands
| | - Jianqing Li
- State Key Laboratory of Digital Medical Engineering, School of Instrument Science and Engineering, Southeast University, Nanjing, China; School of Biomedical Engineering and Informatics, Nanjing Medical University, Nanjing, China
| | - Chengyu Liu
- State Key Laboratory of Digital Medical Engineering, School of Instrument Science and Engineering, Southeast University, Nanjing, China
| | - Xi Long
- Department of Electrical Engineering, Eindhoven University of Technology, Eindhoven, the Netherlands.
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Cramer SJE, Dekker J, Croughan MK, Lee KL, Crossley KJ, McGillick EV, Martherus T, Thio M, Wallace MJ, Kitchen MJ, Hooper SB, Te Pas AB. The effect of vibrotactile stimulation on hypoxia-induced irregular breathing and apnea in preterm rabbits. Pediatr Res 2024; 96:325-331. [PMID: 38356025 DOI: 10.1038/s41390-024-03061-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/27/2023] [Revised: 12/22/2023] [Accepted: 12/29/2023] [Indexed: 02/16/2024]
Abstract
BACKGROUND Manual tactile stimulation is used to counteract apnea in preterm infants, but it is unknown when this intervention should be applied. We compared an anticipatory to a reactive approach using vibrotactile stimulation to prevent hypoxia induced apneas. METHODS Preterm rabbit kittens were prematurely delivered and randomized to either group. All kittens breathed spontaneously with a positive airway pressure of 8 cmH2O while they were imaged using phase contrast X-ray. Irregular breathing (IB) was induced using gradual hypoxia. The anticipatory group received stimulation at the onset of IB and the reactive group if IB transitioned into apnea. Breathing rate (BR), heart rate (HR) and functional residual capacity (FRC) were compared. RESULTS Anticipatory stimulation significantly reduced apnea incidence and maximum inter-breath intervals and increased BR following IB, compared to reactive stimulation. Recovery in BR but not HR was more likely with anticipatory stimulation, although both BR and HR were significantly higher at 120 s after stimulation onset. FRC values and variability were not different. CONCLUSIONS Anticipated vibrotactile stimulation is more effective in preventing apnea and enhancing breathing when compared to reactive stimulation in preterm rabbits. Stimulation timing is likely to be a key factor in reducing the incidence and duration of apnea. IMPACT Anticipated vibrotactile stimulation can prevent apnea and stimulate breathing effort in preterm rabbits. Anticipated vibrotactile stimulation increases the likelihood of breathing rate recovery following hypoxia induced irregular breathing, when compared to reactive stimulation. Automated stimulation in combination with predictive algorithms may improve the treatment of apnea in preterm infants.
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Affiliation(s)
- Sophie J E Cramer
- Willem-Alexander Children's Hospital, Department of Pediatrics, Division of Neonatology, Leiden University Medical Center, Leiden, the Netherlands.
| | - Janneke Dekker
- Willem-Alexander Children's Hospital, Department of Pediatrics, Division of Neonatology, Leiden University Medical Center, Leiden, the Netherlands
| | - Michelle K Croughan
- School of Physics and Astronomy, Monash University, Melbourne, VIC, Australia
| | - Katie L Lee
- School of Physics and Astronomy, Monash University, Melbourne, VIC, Australia
- School of Earth and Environmental Science, University of Queensland, Brisbane, QLD, Australia
| | - Kelly J Crossley
- The Ritchie Centre, Hudson Institute of Medical Research, Melbourne, VIC, Australia
- Department of Obstetrics and Gynecology, Monash University, Melbourne, VIC, Australia
| | - Erin V McGillick
- The Ritchie Centre, Hudson Institute of Medical Research, Melbourne, VIC, Australia
- Department of Obstetrics and Gynecology, Monash University, Melbourne, VIC, Australia
| | - Tessa Martherus
- Willem-Alexander Children's Hospital, Department of Pediatrics, Division of Neonatology, Leiden University Medical Center, Leiden, the Netherlands
| | - Martha Thio
- Women's Newborn Research Centre, The Royal Women's Hospital, Melbourne, VIC, Australia
- Centre of Research Excellence in Newborn Medicine, Murdoch Children's Research Institute, Melbourne, VIC, Australia
- Department of Obstetrics and Gynecology, University of Melbourne, Melbourne, VIC, Australia
| | - Megan J Wallace
- The Ritchie Centre, Hudson Institute of Medical Research, Melbourne, VIC, Australia
- Department of Obstetrics and Gynecology, Monash University, Melbourne, VIC, Australia
| | - Marcus J Kitchen
- School of Physics and Astronomy, Monash University, Melbourne, VIC, Australia
- The Ritchie Centre, Hudson Institute of Medical Research, Melbourne, VIC, Australia
| | - Stuart B Hooper
- The Ritchie Centre, Hudson Institute of Medical Research, Melbourne, VIC, Australia
- Department of Obstetrics and Gynecology, Monash University, Melbourne, VIC, Australia
| | - Arjan B Te Pas
- Willem-Alexander Children's Hospital, Department of Pediatrics, Division of Neonatology, Leiden University Medical Center, Leiden, the Netherlands
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Song YM, Jeong J, de Los Reyes AA, Lim D, Cho CH, Yeom JW, Lee T, Lee JB, Lee HJ, Kim JK. Causal dynamics of sleep, circadian rhythm, and mood symptoms in patients with major depression and bipolar disorder: insights from longitudinal wearable device data. EBioMedicine 2024; 103:105094. [PMID: 38579366 PMCID: PMC11002811 DOI: 10.1016/j.ebiom.2024.105094] [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: 10/02/2023] [Revised: 03/14/2024] [Accepted: 03/17/2024] [Indexed: 04/07/2024] Open
Abstract
BACKGROUND Sleep and circadian rhythm disruptions are common in patients with mood disorders. The intricate relationship between these disruptions and mood has been investigated, but their causal dynamics remain unknown. METHODS We analysed data from 139 patients (76 female, mean age = 23.5 ± 3.64 years) with mood disorders who participated in a prospective observational study in South Korea. The patients wore wearable devices to monitor sleep and engaged in smartphone-delivered ecological momentary assessment of mood symptoms. Using a mathematical model, we estimated their daily circadian phase based on sleep data. Subsequently, we obtained daily time series for sleep/circadian phase estimates and mood symptoms spanning >40,000 days. We analysed the causal relationship between the time series using transfer entropy, a non-linear causal inference method. FINDINGS The transfer entropy analysis suggested causality from circadian phase disturbance to mood symptoms in both patients with MDD (n = 45) and BD type I (n = 35), as 66.7% and 85.7% of the patients with a large dataset (>600 days) showed causality, but not in patients with BD type II (n = 59). Surprisingly, no causal relationship was suggested between sleep phase disturbances and mood symptoms. INTERPRETATION Our findings suggest that in patients with mood disorders, circadian phase disturbances directly precede mood symptoms. This underscores the potential of targeting circadian rhythms in digital medicine, such as sleep or light exposure interventions, to restore circadian phase and thereby manage mood disorders effectively. FUNDING Institute for Basic Science, the Human Frontiers Science Program Organization, the National Research Foundation of Korea, and the Ministry of Health & Welfare of South Korea.
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Affiliation(s)
- Yun Min Song
- Department of Mathematical Sciences, KAIST, Daejeon, 34141, Republic of Korea; Biomedical Mathematics Group, Pioneer Research Center for Mathematical and Computational Sciences, Institute for Basic Science, Daejeon, 34126, Republic of Korea
| | - Jaegwon Jeong
- Department of Psychiatry, Korea University College of Medicine, Seoul, 02841, Republic of Korea; Chronobiology Institute, Korea University, Seoul, 02841, Republic of Korea
| | - Aurelio A de Los Reyes
- Biomedical Mathematics Group, Pioneer Research Center for Mathematical and Computational Sciences, Institute for Basic Science, Daejeon, 34126, Republic of Korea; Institute of Mathematics, University of the Philippines Diliman, Quezon City, 1101, Philippines
| | - Dongju Lim
- Department of Mathematical Sciences, KAIST, Daejeon, 34141, Republic of Korea; Biomedical Mathematics Group, Pioneer Research Center for Mathematical and Computational Sciences, Institute for Basic Science, Daejeon, 34126, Republic of Korea
| | - Chul-Hyun Cho
- Department of Psychiatry, Korea University College of Medicine, Seoul, 02841, Republic of Korea; Chronobiology Institute, Korea University, Seoul, 02841, Republic of Korea
| | - Ji Won Yeom
- Department of Psychiatry, Korea University College of Medicine, Seoul, 02841, Republic of Korea; Chronobiology Institute, Korea University, Seoul, 02841, Republic of Korea
| | - Taek Lee
- Division of Computer Science and Engineering, Sun Moon University, Asan, 31460, Republic of Korea
| | - Jung-Been Lee
- Division of Computer Science and Engineering, Sun Moon University, Asan, 31460, Republic of Korea
| | - Heon-Jeong Lee
- Department of Psychiatry, Korea University College of Medicine, Seoul, 02841, Republic of Korea; Chronobiology Institute, Korea University, Seoul, 02841, Republic of Korea.
| | - Jae Kyoung Kim
- Department of Mathematical Sciences, KAIST, Daejeon, 34141, Republic of Korea; Biomedical Mathematics Group, Pioneer Research Center for Mathematical and Computational Sciences, Institute for Basic Science, Daejeon, 34126, Republic of Korea.
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Porta-García MÁ, Quiroz-Salazar A, Abarca-Castro EA, Reyes-Lagos JJ. Bradycardia May Decrease Cardiorespiratory Coupling in Preterm Infants. ENTROPY (BASEL, SWITZERLAND) 2023; 25:1616. [PMID: 38136496 PMCID: PMC10743269 DOI: 10.3390/e25121616] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/07/2023] [Revised: 11/22/2023] [Accepted: 11/24/2023] [Indexed: 12/24/2023]
Abstract
Bradycardia, frequently observed in preterm infants, presents significant risks due to the immaturity of their autonomic nervous system (ANS) and respiratory systems. These infants may face cardiorespiratory events, leading to severe complications like hypoxemia and neurodevelopmental disorders. Although neonatal care has advanced, the influence of bradycardia on cardiorespiratory coupling (CRC) remains elusive. This exploratory study delves into CRC in preterm infants, emphasizing disparities between events with and without bradycardia. Using the Preterm Infant Cardio-Respiratory Signals (PICS) database, we analyzed interbeat (R-R) and inter-breath intervals (IBI) from 10 preterm infants. The time series were segmented into bradycardic (B) and non-bradycardic (NB) segments. Employing information theory measures, we quantified the irregularity of cardiac and respiratory time series. Notably, B segments had significantly lower entropy values for R-R and IBI than NB segments, while mutual information was higher in NB segments. This could imply a reduction in the complexity of respiratory and cardiac dynamics during bradycardic events, potentially indicating weaker CRC. Building on these insights, this research highlights the distinctive physiological characteristics of preterm infants and underscores the potential of emerging non-invasive diagnostic tools.
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Affiliation(s)
- Miguel Ángel Porta-García
- Center of Research and Innovation in Information Technology and Communication—INFOTEC, Mexico City 14050, Mexico;
- School of Medicine, Autonomous University of the State of Mexico (UAEMéx), Toluca de Lerdo 50180, Mexico;
| | - Alberto Quiroz-Salazar
- School of Medicine, Autonomous University of the State of Mexico (UAEMéx), Toluca de Lerdo 50180, Mexico;
| | - Eric Alonso Abarca-Castro
- Department of Health Sciences, Metropolitan Autonomous University-Lerma (UAM-L), Lerma de Villada 52005, Mexico;
| | - José Javier Reyes-Lagos
- School of Medicine, Autonomous University of the State of Mexico (UAEMéx), Toluca de Lerdo 50180, Mexico;
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Bester M, Almario Escorcia MJ, Fonseca P, Mollura M, van Gilst MM, Barbieri R, Mischi M, van Laar JOEH, Vullings R, Joshi R. The impact of healthy pregnancy on features of heart rate variability and pulse wave morphology derived from wrist-worn photoplethysmography. Sci Rep 2023; 13:21100. [PMID: 38036597 PMCID: PMC10689737 DOI: 10.1038/s41598-023-47980-2] [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: 04/21/2023] [Accepted: 11/20/2023] [Indexed: 12/02/2023] Open
Abstract
Due to the association between dysfunctional maternal autonomic regulation and pregnancy complications, tracking non-invasive features of autonomic regulation derived from wrist-worn photoplethysmography (PPG) measurements may allow for the early detection of deteriorations in maternal health. However, even though a plethora of these features-specifically, features describing heart rate variability (HRV) and the morphology of the PPG waveform (morphological features)-exist in the literature, it is unclear which of these may be valuable for tracking maternal health. As an initial step towards clarity, we compute comprehensive sets of HRV and morphological features from nighttime PPG measurements. From these, using logistic regression and stepwise forward feature elimination, we identify the features that best differentiate healthy pregnant women from non-pregnant women, since these likely capture physiological adaptations necessary for sustaining healthy pregnancy. Overall, morphological features were more valuable for discriminating between pregnant and non-pregnant women than HRV features (area under the receiver operating characteristics curve of 0.825 and 0.74, respectively), with the systolic pulse wave deterioration being the most valuable single feature, followed by mean heart rate (HR). Additionally, we stratified the analysis by sleep stages and found that using features calculated only from periods of deep sleep enhanced the differences between the two groups. In conclusion, we postulate that in addition to HRV features, morphological features may also be useful in tracking maternal health and suggest specific features to be included in future research concerning maternal health.
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Affiliation(s)
- M Bester
- Department of Electrical Engineering, Eindhoven University of Technology, 5612 AZ, Eindhoven, The Netherlands.
- Patient Care and Monitoring, Philips Research, 5656 AE, Eindhoven, The Netherlands.
| | - M J Almario Escorcia
- Patient Care and Monitoring, Philips Research, 5656 AE, Eindhoven, The Netherlands
- Department of Electronics, Information and Bioengineering, Politecnico di Milano, 20133, Milan, MI, Italy
| | - P Fonseca
- Department of Electrical Engineering, Eindhoven University of Technology, 5612 AZ, Eindhoven, The Netherlands
- Patient Care and Monitoring, Philips Research, 5656 AE, Eindhoven, The Netherlands
| | - M Mollura
- Department of Electronics, Information and Bioengineering, Politecnico di Milano, 20133, Milan, MI, Italy
| | - M M van Gilst
- Department of Electrical Engineering, Eindhoven University of Technology, 5612 AZ, Eindhoven, The Netherlands
- Sleep Medicine Center Kempenhaeghe, 5591 VE, Heeze, The Netherlands
| | - R Barbieri
- Department of Electronics, Information and Bioengineering, Politecnico di Milano, 20133, Milan, MI, Italy
| | - M Mischi
- Department of Electrical Engineering, Eindhoven University of Technology, 5612 AZ, Eindhoven, The Netherlands
| | - J O E H van Laar
- Department of Electrical Engineering, Eindhoven University of Technology, 5612 AZ, Eindhoven, The Netherlands
- Department of Obstetrics and Gynecology, Máxima Medical Centrum, De Run 4600, 5504 DB, Veldhoven, The Netherlands
| | - R Vullings
- Department of Electrical Engineering, Eindhoven University of Technology, 5612 AZ, Eindhoven, The Netherlands
| | - R Joshi
- Patient Care and Monitoring, Philips Research, 5656 AE, Eindhoven, The Netherlands
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Kloonen RMJS, Varisco G, de Kort E, Andriessen P, Niemarkt HJ, van Pul C. Predicting CPAP failure after less invasive surfactant administration (LISA) in preterm infants by machine learning model on vital parameter data: a pilot study. Physiol Meas 2023; 44:115005. [PMID: 37939392 DOI: 10.1088/1361-6579/ad0ab6] [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: 06/16/2023] [Accepted: 11/07/2023] [Indexed: 11/10/2023]
Abstract
Objective. Less invasive surfactant administration (LISA) has been introduced to preterm infants with respiratory distress syndrome on continuous positive airway pressure (CPAP) support in order to avoid intubation and mechanical ventilation. However, after this LISA procedure, a significant part of infants fails CPAP treatment (CPAP-F) and requires intubation in the first 72 h of life, which is associated with worse complication free survival chances. The aim of this study was to predict CPAP-F after LISA, based on machine learning (ML) analysis of high resolution vital parameter monitoring data surrounding the LISA procedure.Approach. Patients with a gestational age (GA) <32 weeks receiving LISA were included. Vital parameter data was obtained from a data warehouse. Physiological features (HR, RR, peripheral oxygen saturation (SpO2) and body temperature) were calculated in eight 0.5 h windows throughout a period 1.5 h before to 2.5 h after LISA. First, physiological data was analyzed to investigate differences between the CPAP-F and CPAP-Success (CPAP-S) groups. Next, the performance of two types of ML models (logistic regression: LR, support vector machine: SVM) for the prediction of CPAP-F were evaluated.Main results. Of 51 included patients, 18 (35%) had CPAP-F. Univariate analysis showed lower SpO2, temperature and heart rate variability (HRV) before and after the LISA procedure. The best performing ML model showed an area under the curve of 0.90 and 0.93 for LR and SVM respectively in the 0.5 h window directly after LISA, with GA, HRV, respiration rate and SpO2as most important features. Excluding GA decreased performance in both models.Significance. In this pilot study we were able to predict CPAP-F with a ML model of patient monitor signals, with best performance in the first 0.5 h after LISA. Using ML to predict CPAP-F based on vital signals gains insight in (possibly modifiable) factors that are associated with LISA failure and can help to guide personalized clinical decisions in early respiratory management.
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Affiliation(s)
- R M J S Kloonen
- Eindhoven University of Technology, PO Box 513, 5600 MB Eindhoven, The Netherlands
- Maxima Medical Centre Veldhoven, Department of Clinical Physics, Po Box 7777, 5600 MB, The Netherlands
| | - G Varisco
- Eindhoven University of Technology, PO Box 513, 5600 MB Eindhoven, The Netherlands
| | - E de Kort
- Maxima Medical Centre Veldhoven, Department of Pediatrics, Po Box 7777, 5600 MB, The Netherlands
| | - P Andriessen
- Maxima Medical Centre Veldhoven, Department of Pediatrics, Po Box 7777, 5600 MB, The Netherlands
| | - H J Niemarkt
- Eindhoven University of Technology, PO Box 513, 5600 MB Eindhoven, The Netherlands
- Maxima Medical Centre Veldhoven, Department of Pediatrics, Po Box 7777, 5600 MB, The Netherlands
| | - C van Pul
- Eindhoven University of Technology, PO Box 513, 5600 MB Eindhoven, The Netherlands
- Maxima Medical Centre Veldhoven, Department of Clinical Physics, Po Box 7777, 5600 MB, The Netherlands
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Bester M, Perciballi G, Fonseca P, van Gilst MM, Mischi M, van Laar JO, Vullings R, Joshi R. Maternal cardiorespiratory coupling: differences between pregnant and nonpregnant women are further amplified by sleep-stage stratification. J Appl Physiol (1985) 2023; 135:1199-1212. [PMID: 37767554 PMCID: PMC10979799 DOI: 10.1152/japplphysiol.00296.2023] [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: 05/09/2023] [Revised: 08/22/2023] [Accepted: 09/22/2023] [Indexed: 09/29/2023] Open
Abstract
Pregnancy complications are associated with abnormal maternal autonomic regulation. Subsequently, thoroughly understanding maternal autonomic regulation during healthy pregnancy may enable the earlier detection of complications, in turn allowing for the improved management thereof. Under healthy autonomic regulation, reciprocal interactions occur between the cardiac and respiratory systems, i.e., cardiorespiratory coupling (CRC). Here, we investigate, for the first time, the differences in CRC between healthy pregnant and nonpregnant women. We apply two algorithms, namely, synchrograms and bivariate phase-rectified signal averaging, to nighttime recordings of ECG and respiratory signals. We find that CRC is present in both groups. Significantly less (P < 0.01) cardiorespiratory synchronization occurs in pregnant women (11% vs. 15% in nonpregnant women). Moreover, there is a smaller response in the heart rate of pregnant women corresponding to respiratory inhalations and exhalations. In addition, we stratified these analyses by sleep stages. As each sleep stage is governed by different autonomic states, this stratification not only amplified some of the differences between groups but also brought out differences that remained hidden when analyzing the full-night recordings. Most notably, the known positive relationship between CRC and deep sleep is less prominent in pregnant women than in their nonpregnant counterparts. The decrease in CRC during healthy pregnancy may be attributable to decreased maternal parasympathetic activity, anatomical changes to the maternal respiratory system, and the increased physiological stress accompanying pregnancy. This work offers novel insight into the physiology of healthy pregnancy and forms part of the base knowledge needed to detect abnormalities in pregnancy.NEW & NOTEWORTHY We compare CRC, i.e., the reciprocal interaction between the cardiac and respiratory systems, between healthy pregnant and nonpregnant women for the first time. Although CRC is present in both groups, CRC is reduced during healthy pregnancy; there is less synchronization between maternal cardiac and respiratory activity and a smaller response in maternal heart rate to respiratory inhalations and exhalations. Stratifying this analysis by sleep stages reveals that differences are most prominent during deep sleep.
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Affiliation(s)
- Maretha Bester
- Department of Electrical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands
- Patient Care and Monitoring, Philips Research, Eindhoven, The Netherlands
| | - Giulia Perciballi
- Patient Care and Monitoring, Philips Research, Eindhoven, The Netherlands
- Department of Electronics, Information and Bioengineering, Politecnico di Milano, Milano, Italy
| | - Pedro Fonseca
- Department of Electrical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands
- Patient Care and Monitoring, Philips Research, Eindhoven, The Netherlands
| | - Merel M van Gilst
- Department of Electrical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands
- Sleep Medicine Center Kempenhaeghe, Heeze, The Netherlands
| | - Massimo Mischi
- Department of Electrical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands
| | - Judith Oeh van Laar
- Department of Electrical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands
- Department of Obstetrics and Gynecology, Máxima Medical Centrum, Veldhoven, The Netherlands
| | - Rik Vullings
- Department of Electrical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands
| | - Rohan Joshi
- Patient Care and Monitoring, Philips Research, Eindhoven, The Netherlands
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Pichardo-Carmona EY, Reyes-Lagos JJ, Ceballos-Juárez RG, Ledesma-Ramírez CI, Mendieta-Zerón H, Peña-Castillo MÁ, Nsugbe E, Porta-García MÁ, Mina-Paz Y. Changes in the autonomic cardiorespiratory activity in parturient women with severe and moderate features of preeclampsia. Front Immunol 2023; 14:1190699. [PMID: 37724103 PMCID: PMC10505439 DOI: 10.3389/fimmu.2023.1190699] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2023] [Accepted: 08/03/2023] [Indexed: 09/20/2023] Open
Abstract
Background Cardiorespiratory coupling (CRC) is a physiological phenomenon that reflects the mutual interaction between the cardiac and respiratory control systems. It is mainly associated with efferent vagal activity from the central autonomic network. Few studies have explored the autonomic changes of CRC in preeclampsia, a critical obstetric complication related to possible autonomic dysfunctions and inflammatory disturbances. This study examined the autonomic mechanisms of CRC in women with severe and moderate preeclampsia and healthy controls by applying nonlinear methods based on information theory, such as mutual information (MI) and Renyi's mutual information (RMI) and the linear and nonlinear analysis of the Pulse-Respiration Quotient (PRQ). Methods We studied three groups of parturient women in the third trimester of pregnancy with a clinical diagnosis of preeclampsia without severe symptoms (P, 38.5 ± 1.4 weeks of pregnancy, n=19), preeclampsia with severe symptoms (SP, 37.5 ± 0.9 weeks of pregnancy, n=22), and normotensive control women (C, 39.1 ± 1.3 weeks of pregnancy, n=20). 10-minutes of abdominal electrocardiograms (ECG) and respiratory signals (RESP) were recorded in all the participants. Subsequently, we obtained the maternal beat-to-beat (RR) and breath-to-breath (BB) time series from ECG and RESP, respectively. The CRC between RR and BB was quantified by nonlinear methods based on information theory, such as MI and RMI, along with the analysis of the novel index of PRQ. Subsequently, we computed the mean PRQ (mPRQ) and the normalized permutation entropy (nPermEn_PRQ) from the PRQ time series generated from BB and RR. In addition, we examined the vagal activity in the three groups by the logarithm of the median of the distribution of the absolute values of successive RR differences (logRSA). Results The MI and RMI values were significantly lower (p<0.05) in the preeclamptic groups compared to the control group. However, no significant differences were found between the preeclamptic groups. The logRSA and nPermEn_PRQ indices were significantly lower (p<0.05) in SP compared to C and P. Conclusion Our data suggest that parturient women with severe and mild preeclampsia may manifest an altered cardiorespiratory coupling compared with normotensive control women. Disrupted CRC in severe preeclampsia could be associated with vagal withdrawal and less complex cardiorespiratory dynamics. The difference in vagal activity between the preeclamptic groups may suggest a further reduction in vagal activity associated with the severity of the disease.
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Affiliation(s)
| | | | | | | | - Hugo Mendieta-Zerón
- School of Medicine, Autonomous University of the State of Mexico (UAEMéx), Toluca, Mexico
- Mónica Pretelini Sáenz Maternal-Perinatal Hospital, Health Institute of the State of Mexico (ISEM), Toluca, Mexico
| | | | - Ejay Nsugbe
- Nsugbe Research Labs, Swindon, United Kingdom
| | | | - Yecid Mina-Paz
- Faculty of Health Sciences, Universidad Libre Seccional Cali, Cali, Colombia
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Walker SB, Badke CM, Carroll MS, Honegger KS, Fawcett A, Weese-Mayer DE, Sanchez-Pinto LN. Novel approaches to capturing and using continuous cardiorespiratory physiological data in hospitalized children. Pediatr Res 2023; 93:396-404. [PMID: 36329224 DOI: 10.1038/s41390-022-02359-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/02/2022] [Revised: 08/16/2022] [Accepted: 10/11/2022] [Indexed: 11/06/2022]
Abstract
Continuous cardiorespiratory physiological monitoring is a cornerstone of care in hospitalized children. The data generated by monitoring devices coupled with machine learning could transform the way we provide care. This scoping review summarizes existing evidence on novel approaches to continuous cardiorespiratory monitoring in hospitalized children. We aimed to identify opportunities for the development of monitoring technology and the use of machine learning to analyze continuous physiological data to improve the outcomes of hospitalized children. We included original research articles published on or after January 1, 2001, involving novel approaches to collect and use continuous cardiorespiratory physiological data in hospitalized children. OVID Medline, PubMed, and Embase databases were searched. We screened 2909 articles and performed full-text extraction of 105 articles. We identified 58 articles describing novel devices or approaches, which were generally small and single-center. In addition, we identified 47 articles that described the use of continuous physiological data in prediction models, but only 7 integrated multidimensional data (e.g., demographics, laboratory results). We identified three areas for development: (1) further validation of promising novel devices; (2) more studies of models integrating multidimensional data with continuous cardiorespiratory data; and (3) further dissemination, implementation, and validation of prediction models using continuous cardiorespiratory data. IMPACT: We performed a comprehensive scoping review of novel approaches to capture and use continuous cardiorespiratory physiological data for monitoring, diagnosis, providing care, and predicting events in hospitalized infants and children, from novel devices to machine learning-based prediction models. We identified three key areas for future development: (1) further validation of promising novel devices; (2) more studies of models integrating multidimensional data with continuous cardiorespiratory data; and (3) further dissemination, implementation, and validation of prediction models using cardiorespiratory data.
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Affiliation(s)
- Sarah B Walker
- Department of Pediatrics, Northwestern University Feinberg School of Medicine, Chicago, IL, USA. .,Stanley Manne Children's Research Institute, Ann & Robert H. Lurie Children's Hospital of Chicago, Chicago, IL, USA.
| | - Colleen M Badke
- Department of Pediatrics, Northwestern University Feinberg School of Medicine, Chicago, IL, USA.,Stanley Manne Children's Research Institute, Ann & Robert H. Lurie Children's Hospital of Chicago, Chicago, IL, USA
| | - Michael S Carroll
- Department of Pediatrics, Northwestern University Feinberg School of Medicine, Chicago, IL, USA.,Stanley Manne Children's Research Institute, Ann & Robert H. Lurie Children's Hospital of Chicago, Chicago, IL, USA
| | - Kyle S Honegger
- Department of Pediatrics, Northwestern University Feinberg School of Medicine, Chicago, IL, USA.,Stanley Manne Children's Research Institute, Ann & Robert H. Lurie Children's Hospital of Chicago, Chicago, IL, USA
| | - Andrea Fawcett
- Department of Pediatrics, Northwestern University Feinberg School of Medicine, Chicago, IL, USA.,Stanley Manne Children's Research Institute, Ann & Robert H. Lurie Children's Hospital of Chicago, Chicago, IL, USA
| | - Debra E Weese-Mayer
- Department of Pediatrics, Northwestern University Feinberg School of Medicine, Chicago, IL, USA.,Stanley Manne Children's Research Institute, Ann & Robert H. Lurie Children's Hospital of Chicago, Chicago, IL, USA
| | - L Nelson Sanchez-Pinto
- Department of Pediatrics, Northwestern University Feinberg School of Medicine, Chicago, IL, USA.,Stanley Manne Children's Research Institute, Ann & Robert H. Lurie Children's Hospital of Chicago, Chicago, IL, USA
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10
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Varisco G, Peng Z, Kommers D, Zhan Z, Cottaar W, Andriessen P, Long X, van Pul C. Central apnea detection in premature infants using machine learning. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2022; 226:107155. [PMID: 36215858 DOI: 10.1016/j.cmpb.2022.107155] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/01/2022] [Revised: 09/13/2022] [Accepted: 09/23/2022] [Indexed: 06/16/2023]
Abstract
BACKGROUND AND OBJECTIVE Apnea of prematurity is one of the most common diagnosis in neonatal intensive care units. Apneas can be classified as central, obstructive or mixed. According to the current international standards, minimal fluctuations or absence of fluctuations in the chest impedance (CI) suggest a central apnea (CA). However, automatic detection of reduced CI fluctuations leads to a high number of central apnea-suspected events (CASEs), the majority being false alarms. We aim to improve automatic detection of CAs by using machine learning to optimize detection of CAs among CASEs. METHODS Using an optimized algorithm for automated detection, all CASEs were detected in a population of 10 premature infants developing late-onset sepsis and 10 age-matched control patients. CASEs were inspected by two clinical experts and annotated as CAs or rejections in two rounds of annotations. A total of 47 features were extracted from the ECG, CI and oxygen saturation signals considering four 30 s-long moving windows, from 30 s before to 15 s after the onset of each CASE, using a moving step size of 5 s. Consecutively, new CA detection models were developed based on logistic regression with elastic net penalty, random forest and support vector machines. Performance was evaluated using both leave-one-patient-out and 10-fold cross-validation considering the mean area under the receiver-operating-characteristic curve (AUROC). RESULTS The CA detection model based on logistic regression with elastic net penalty returned the highest mean AUROC when features extracted from all four time windows were included, both using leave-one-patient-out and 10-fold cross-validation (mean AUROC of 0.88 and 0.90, respectively). Feature relevance was found to be the highest for features derived from the CI. A threshold for the false positive rate in the mean receiver-operating-characteristic curve equal to 0.3 led to a high percentage of correct detections for all CAs (78.2%) and even higher for CAs followed by a bradycardia (93.4%) and CAs followed by both a bradycardia and a desaturation (95.2%), which are more critical for the well-being of premature infants. CONCLUSIONS Models based on machine learning can lead to improved CA detection with fewer false alarms.
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Affiliation(s)
- Gabriele Varisco
- Applied Physics, Eindhoven University of Technology, Eindhoven, the Netherlands; Clinical Physics, Máxima Medical Center, Veldhoven, the Netherlands.
| | - Zheng Peng
- Applied Physics, Eindhoven University of Technology, Eindhoven, the Netherlands; Clinical Physics, Máxima Medical Center, Veldhoven, the Netherlands
| | - Deedee Kommers
- Applied Physics, Eindhoven University of Technology, Eindhoven, the Netherlands; Pediatrics, Máxima Medical Center, Veldhoven, the Netherlands
| | - Zhuozhao Zhan
- Mathematics and Computer Science, Eindhoven University of Technology, Eindhoven, the Netherlands
| | - Ward Cottaar
- Applied Physics, Eindhoven University of Technology, Eindhoven, the Netherlands
| | - Peter Andriessen
- Applied Physics, Eindhoven University of Technology, Eindhoven, the Netherlands; Pediatrics, Máxima Medical Center, Veldhoven, the Netherlands
| | - Xi Long
- Philips Research, Eindhoven, the Netherlands; Electrical Engineering, Eindhoven University of Technology, Eindhoven, the Netherlands.
| | - Carola van Pul
- Applied Physics, Eindhoven University of Technology, Eindhoven, the Netherlands; Clinical Physics, Máxima Medical Center, Veldhoven, the Netherlands
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11
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Bester M, Rizea R, Joshi R, Mischi M, Van Laar J, Vullings R. Maternal autonomic responsiveness is attenuated in healthy pregnancy: a phase rectified signal averaging analysis. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2022; 2022:4982-4986. [PMID: 36085954 DOI: 10.1109/embc48229.2022.9870894] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
Autonomic regulation is essential in enabling a healthy pregnancy. In fact, several pregnancy complications are associated with autonomic dysfunction. Better understanding of the maternal autonomic state during healthy pregnancy may aid in the early detection of such complications. One aspect of autonomic regulation is autonomic responsiveness, which can by assessed by phase rectified signal averaging (PRSA). While other areas of research have found blunted physiological responses in pregnancy, this paper presents the first investigation of maternal autonomic responsiveness as assessed by PRSA. We find significantly reduced rates of responses, as well as an attenuated capacity for heart rate acceleration when comparing pregnant women to non-pregnant controls. We hypothesize that this attenuated autonomic control may serve to protect the mother against her imbalanced autonomic state, as increased sympathetic and decreased parasympathetic modulation accompany healthy pregnancies. Clinical Relevance- Maternal autonomic responsiveness is attenuated in pregnancy in comparison to non-pregnant women. Understanding maternal autonomic state not only improves our knowledge of gestational physiology but also forms the basis for the early detection of pregnancy complications associated with maternal autonomic dysfunction.
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Affiliation(s)
- M Bester
- Eindhoven University of Technology,Netherlands,5612 AZ
| | - R Rizea
- POLITEHNICA University of Bucharest,Bucharest,060042
| | - R Joshi
- Philips Research,Eindhoven,Netherlands,5656 AE
| | - M Mischi
- Eindhoven University of Technology,Netherlands,5612 AZ
| | - Joeh Van Laar
- Eindhoven University of Technology,Netherlands,5612 AZ
| | - R Vullings
- Eindhoven University of Technology,Netherlands,5612 AZ
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12
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Bester M, Joshi R, Mischi M, van Laar JOEH, Vullings R. Longitudinally Tracking Maternal Autonomic Modulation During Normal Pregnancy With Comprehensive Heart Rate Variability Analyses. Front Physiol 2022; 13:874684. [PMID: 35615673 PMCID: PMC9125027 DOI: 10.3389/fphys.2022.874684] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/12/2022] [Accepted: 03/31/2022] [Indexed: 12/28/2022] Open
Abstract
Changes in the maternal autonomic nervous system are essential in facilitating the physiological changes that pregnancy necessitates. Insufficient autonomic adaptation is linked to complications such as hypertensive diseases of pregnancy. Consequently, tracking autonomic modulation during progressing pregnancy could allow for the early detection of emerging deteriorations in maternal health. Autonomic modulation can be longitudinally and unobtrusively monitored by assessing heart rate variability (HRV). Yet, changes in maternal HRV (mHRV) throughout pregnancy remain poorly understood. In previous studies, mHRV is typically assessed only once per trimester with standard HRV features. However, since gestational changes are complex and dynamic, assessing mHRV comprehensively and more frequently may better showcase the changing autonomic modulation over pregnancy. Subsequently, we longitudinally (median sessions = 8) assess mHRV in 29 healthy pregnancies with features that assess sympathetic and parasympathetic activity, as well as heart rate (HR) complexity, HR responsiveness and HR fragmentation. We find that vagal activity, HR complexity, HR responsiveness, and HR fragmentation significantly decrease. Their associated effect sizes are small, suggesting that the increasing demands of advancing gestation are well tolerated. Furthermore, we find a notable change in autonomic activity during the transition from the second to third trimester, highlighting the dynamic nature of changes in pregnancy. Lastly, while we saw the expected rise in mean HR with gestational age, we also observed increased autonomic deceleration activity, seemingly to counter this rising mean HR. These results are an important step towards gaining insights into gestational physiology as well as tracking maternal health via mHRV.
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Affiliation(s)
- Maretha Bester
- Department of Electrical Engineering, Eindhoven University of Technology, Eindhoven, Netherlands
- Patient Care and Monitoring, Philips Research, Eindhoven, Netherlands
- *Correspondence: Maretha Bester,
| | - Rohan Joshi
- Patient Care and Monitoring, Philips Research, Eindhoven, Netherlands
| | - Massimo Mischi
- Department of Electrical Engineering, Eindhoven University of Technology, Eindhoven, Netherlands
| | - Judith O. E. H. van Laar
- Department of Electrical Engineering, Eindhoven University of Technology, Eindhoven, Netherlands
- Department of Obstetrics and Gynecology, Máxima Medical Centrum, Veldhoven, Netherlands
| | - Rik Vullings
- Department of Electrical Engineering, Eindhoven University of Technology, Eindhoven, Netherlands
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13
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Rozo A, Morales J, Moeyersons J, Joshi R, Caiani EG, Borzée P, Buyse B, Testelmans D, Van Huffel S, Varon C. Benchmarking Transfer Entropy Methods for the Study of Linear and Nonlinear Cardio-Respiratory Interactions. ENTROPY 2021; 23:e23080939. [PMID: 34441079 PMCID: PMC8394114 DOI: 10.3390/e23080939] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/08/2021] [Revised: 07/13/2021] [Accepted: 07/20/2021] [Indexed: 11/16/2022]
Abstract
Transfer entropy (TE) has been used to identify and quantify interactions between physiological systems. Different methods exist to estimate TE, but there is no consensus about which one performs best in specific applications. In this study, five methods (linear, k-nearest neighbors, fixed-binning with ranking, kernel density estimation and adaptive partitioning) were compared. The comparison was made on three simulation models (linear, nonlinear and linear + nonlinear dynamics). From the simulations, it was found that the best method to quantify the different interactions was adaptive partitioning. This method was then applied on data from a polysomnography study, specifically on the ECG and the respiratory signals (nasal airflow and respiratory effort around the thorax). The hypothesis that the linear and nonlinear components of cardio-respiratory interactions during light and deep sleep change with the sleep stage, was tested. Significant differences, after performing surrogate analysis, indicate an increased TE during deep sleep. However, these differences were found to be dependent on the type of respiratory signal and sampling frequency. These results highlight the importance of selecting the appropriate signals, estimation method and surrogate analysis for the study of linear and nonlinear cardio-respiratory interactions.
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Affiliation(s)
- Andrea Rozo
- STADIUS, Center of Dynamical Systems, Signal Processing and Data Analytics, Department of Electrical Engineering (ESAT), KU Leuven, 3001 Leuven, Belgium; (J.M.); (J.M.); (S.V.H.); (C.V.)
- Correspondence:
| | - John Morales
- STADIUS, Center of Dynamical Systems, Signal Processing and Data Analytics, Department of Electrical Engineering (ESAT), KU Leuven, 3001 Leuven, Belgium; (J.M.); (J.M.); (S.V.H.); (C.V.)
| | - Jonathan Moeyersons
- STADIUS, Center of Dynamical Systems, Signal Processing and Data Analytics, Department of Electrical Engineering (ESAT), KU Leuven, 3001 Leuven, Belgium; (J.M.); (J.M.); (S.V.H.); (C.V.)
| | - Rohan Joshi
- Department of Patient Care and Monitoring, Philips Research, 5656 AE Eindhoven, The Netherlands;
| | - Enrico G. Caiani
- Department of Electronics, Information and Bioengineering, Politecnico di Milano, 20133 Milan, Italy;
| | - Pascal Borzée
- Department of Pneumology, Leuven University Centre for Sleep and Wake Disorders, UZ Leuven, 3000 Leuven, Belgium; (P.B.); (B.B.); (D.T.)
| | - Bertien Buyse
- Department of Pneumology, Leuven University Centre for Sleep and Wake Disorders, UZ Leuven, 3000 Leuven, Belgium; (P.B.); (B.B.); (D.T.)
| | - Dries Testelmans
- Department of Pneumology, Leuven University Centre for Sleep and Wake Disorders, UZ Leuven, 3000 Leuven, Belgium; (P.B.); (B.B.); (D.T.)
| | - Sabine Van Huffel
- STADIUS, Center of Dynamical Systems, Signal Processing and Data Analytics, Department of Electrical Engineering (ESAT), KU Leuven, 3001 Leuven, Belgium; (J.M.); (J.M.); (S.V.H.); (C.V.)
| | - Carolina Varon
- STADIUS, Center of Dynamical Systems, Signal Processing and Data Analytics, Department of Electrical Engineering (ESAT), KU Leuven, 3001 Leuven, Belgium; (J.M.); (J.M.); (S.V.H.); (C.V.)
- Service de Chimie-Physique E.P., Université libre de Bruxelles, B-1050 Brussels, Belgium
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14
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Morales J, Moeyersons J, Armanac P, Orini M, Faes L, Overeem S, Van Gilst M, Van Dijk J, Van Huffel S, Bailon R, Varon C. Model-Based Evaluation of Methods for Respiratory Sinus Arrhythmia Estimation. IEEE Trans Biomed Eng 2021; 68:1882-1893. [PMID: 33001798 DOI: 10.1109/tbme.2020.3028204] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Abstract
OBJECTIVE Respiratory sinus arrhythmia (RSA) refers to heart rate oscillations synchronous with respiration, and it is one of the major representations of cardiorespiratory coupling. Its strength has been suggested as a biomarker to monitor different conditions, and diseases. Some approaches have been proposed to quantify the RSA, but it is unclear which one performs best in specific scenarios. The main objective of this study is to compare seven state-of-the-art methods for RSA quantification using data generated with a model proposed to simulate, and control the RSA. These methods are also compared, and evaluated on a real-life application, for their ability to capture changes in cardiorespiratory coupling during sleep. METHODS A simulation model is used to create a dataset of heart rate variability, and respiratory signals with controlled RSA, which is used to compare the RSA estimation approaches. To compare the methods objectively in real-life applications, regression models trained on the simulated data are used to map the estimates to the same measurement scale. Results, and conclusion: RSA estimates based on cross entropy, time-frequency coherence, and subspace projections showed the best performance on simulated data. In addition, these estimates captured the expected trends in the changes in cardiorespiratory coupling during sleep similarly. SIGNIFICANCE An objective comparison of methods for RSA quantification is presented to guide future analyses. Also, the proposed simulation model can be used to compare existing, and newly proposed RSA estimates. It is freely accessible online.
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15
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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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16
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Moors S, Joshi R, Bullens LM, van Oostrum NHM, Regis M, van den Heuvel ER, Oei SG, van Laar JOEH, van der Hout-van der Jagt MB. A randomized controlled trial studying the effect of maternal hyperoxygenation on fetal heart rate in suspected fetal distress. Physiol Meas 2020; 41:115002. [PMID: 33049730 DOI: 10.1088/1361-6579/abc0b6] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
Abstract
OBJECTIVE To investigate the effect of maternal hyperoxygenation on fetal heart rate (FHR) when applied for suspected fetal distress during the second stage of term labor. APPROACH A single-center randomized controlled trial was conducted in a tertiary care hospital in The Netherlands. Participants were included during the second stage of labor in case of an intermediary or abnormal FHR pattern. Patients were randomized to receive either 100% oxygen at 10 l/min until delivery, or conventional care without additional oxygen. The primary outcome was the change in FHR pattern before and after the onset of the study, measured as the change in depth and duration of FHR decelerations. Secondary outcome measures were features based on phase-rectified signal averaging (PRSA), baseline assignability, and deceleration characteristics of the FHR pattern. MAIN RESULTS Between March 2016 and April 2018, 117 women were included. The FHR pattern could be analyzed for 71 participants, the other 46 women delivered before the end of the post time-frame. A 2.3% reduction in depth and duration of FHR decelerations was found after maternal hyperoxygenation, compared to a 10% increase in the control group (p = 0.24). Maternal hyperoxygenation had a significantly positive effect on PRSA metrics, with a decrease in PRSA-acceleration capacity (p = 0.03) and PRSA-deceleration capacity (p = 0.02) in the intervention group compared to the control group. SIGNIFICANCE The difference in depth and duration of decelerations after the start of the study was not significantly different between both study groups. A statistically significant positive effect on PRSA-deceleration capacity and PRSA-acceleration capacity was found after maternal hyperoxygenation, which might be associated with a positive effect on neonatal outcome.
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Affiliation(s)
- S Moors
- Department of Obstetrics and Gynecology, Máxima Medical Center, Veldhoven, The Netherlands. Eindhoven MedTech Innovation Center (e/MTIC), Groene Loper 19, Eindhoven 5600 MB, The Netherlands. Department of Electrical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands
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17
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Joshi R, Kommers D, Oosterwijk L, Feijs L, van Pul C, Andriessen P. Predicting Neonatal Sepsis Using Features of Heart Rate Variability, Respiratory Characteristics, and ECG-Derived Estimates of Infant Motion. IEEE J Biomed Health Inform 2019; 24:681-692. [PMID: 31295130 DOI: 10.1109/jbhi.2019.2927463] [Citation(s) in RCA: 42] [Impact Index Per Article: 8.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
This study in preterm infants was designed to characterize the prognostic potential of several features of heart rate variability (HRV), respiration, and (infant) motion for the predictive monitoring of late-onset sepsis (LOS). In a neonatal intensive care setting, the cardiorespiratory waveforms of infants with blood-culture positive LOS were analyzed to characterize the prognostic potential of 22 features for discriminating control from sepsis-state, using the Naïve Bayes algorithm. Historical data of the subjects acquired from a period sufficiently before the clinical suspicion of LOS was used as control state, whereas data from the 24 h preceding the clinical suspicion of LOS were used as sepsis state (test data). The overall prognostic potential of all features was quantified at three-hourly intervals for the period corresponding to test data by calculating the area under the receiver operating characteristics curve. For the 49 infants studied, features of HRV, respiration, and movement showed characteristic changes in the hours leading up to the clinical suspicion of sepsis, namely, an increased propensity toward pathological heart rate decelerations, increased respiratory instability, and a decrease in spontaneous infant activity, i.e., lethargy. While features characterizing HRV and respiration can be used to probe the state of the autonomic nervous system, those characterizing movement probe the state of the motor system-dysregulation of both reflects an increased likelihood of sepsis. By using readily interpretable features derived from cardiorespiratory monitoring, opportunities for pre-emptively identifying and treating LOS can be developed.
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18
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Morales JF, Varon C, Deviaene M, Milagro J, Testelmans D, Buyse B, Willems R, Orini M, Van Huffel S, Bailon R. Evaluation of Methods to Characterize the Change of the Respiratory Sinus Arrhythmia with Age in Sleep Apnea Patients. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2019; 2019:1588-1591. [PMID: 31946199 DOI: 10.1109/embc.2019.8857957] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/10/2023]
Abstract
The High Frequency (HF) band of the power spectrum of the Heart Rate Variability (HRV) is widely accepted to contain information related to the respiration. However, it is known that this often results in misleading estimations of the strength of the Respiratory Sinus Arrhythmia (RSA). In this paper, different approaches to characterize the change of the RSA with age, combining HRV and respiratory signals, are studied. These approaches are the bandwidths in the power spectral density estimations, bivariate phase rectified signal averaging, information dynamics, a time-frequency representation, and a heart rate decomposition based on subspace projections. They were applied to a dataset of sleep apnea patients, specifically to periods without apneas and during NREM sleep. Each estimate reflected a different relationship between RSA and age, suggesting that they all capture the cardiorespiratory information in a different way. The comparison of the estimates indicates that the approaches based on the extraction of respiratory information from HRV provide a better characterization of the age-dependent degradation of the RSA.
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