The value of cardiorespiratory parameters for sleep state classification in preterm infants: A systematic review

Groot, Eline R. de; Knoop, Marit S.; Hoogen, Agnes van den; Wang, Xiaowan; Long, Xi; Pillen, Sigrid; Benders, Manon J. N. L.; Dudink, Jeroen

doi:10.1016/j.smrv.2021.101462

Cited by 22 publications

(12 citation statements)

References 78 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…The performances of ‘removing CRI’ and ‘CRI’ are better than the other benchmark papers, such as [ 34 , 35 , 36 , 37 , 38 ]. These papers start with the heart rate and body movement signals, try to find features that can characterize sleep stages.…”

Section: Discussionmentioning

confidence: 92%

“…The F1 score of disease diagnosis is 71.6%. Besides, as comparison, features in references [ 34 , 35 , 36 , 37 , 38 ] are used in our scheme instead of CRI. Meanwhile, research on the feature extraction methods of ECG in recent ten years [ 65 , 66 , 67 , 68 ] are also considered.…”

Section: Discussionmentioning

confidence: 99%

“…Martin Oswaldo Mendez et al extracted HRV and body movement using a time-varying auto-regression model, and classified REM and NREM using hidden Markov model [ 36 ], and the time-varying relationship between EEG and HRV are studied [ 37 ]. Eline R.de Groot et al researched on the value of cardiorespiratory parameters for sleep state classification in preterm infants [ 38 ].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Automatic Detection of the Cyclic Alternating Pattern of Sleep and Diagnosis of Sleep-Related Pathologies Based on Cardiopulmonary Resonance Indices

Cui

Huang

Wu³

2022

Sensors

View full text Add to dashboard Cite

The cyclic alternating pattern is the periodic electroencephalogram activity occurring during non-rapid eye movement sleep. It is a marker of sleep instability and is correlated with several sleep-related pathologies. Considering the connection between the human heart and brain, our study explores the feasibility of using cardiopulmonary features to automatically detect the cyclic alternating pattern of sleep and hence diagnose sleep-related pathologies. By statistically analyzing and comparing the cardiopulmonary characteristics of a healthy group and groups with sleep-related diseases, an automatic recognition scheme of the cyclic alternating pattern is proposed based on the cardiopulmonary resonance indices. Using the Hidden Markov and Random Forest, the scheme combines the variation and stability of measurements of the coupling state of the cardiopulmonary system during sleep. In this research, the F1 score of the sleep-wake classification reaches 92.0%. In terms of the cyclic alternating pattern, the average recognition rate of A-phase reaches 84.7% on the CAP Sleep Database of 108 cases of people. The F1 score of disease diagnosis is 87.8% for insomnia and 90.0% for narcolepsy.

show abstract

Section: Discussionmentioning

confidence: 92%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Automatic Detection of the Cyclic Alternating Pattern of Sleep and Diagnosis of Sleep-Related Pathologies Based on Cardiopulmonary Resonance Indices

Cui

Huang

Wu³

2022

Sensors

View full text Add to dashboard Cite

show abstract

“…Our group has demonstrated that cardiorespiratory characteristics, especially heart rate (HR) and respiration frequency, play an important role in distinguishing between sleep–wake states [ 27 ]. Until now, however, a reliable automated sleep staging model based on these parameters has not been readily available for preterm infants admitted to the NICU.…”

Section: Introductionmentioning

confidence: 99%

The Sleep Well Baby project: an automated real-time sleep–wake state prediction algorithm in preterm infants

Sentner

Wang

Groot

et al. 2022

Sleep

Self Cite

View full text Add to dashboard Cite

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 used (n = 9). 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, non-obtrusive, 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.

show abstract

“…However, sleep problems are relatively common among young children, and according to parent reports, it has been suggested that approximately 20–30% of young children have various sleep problems [ 2 ]. In preterm infants younger than 1 year old, sleep problems are thought to be even more prevalent [ 3 , 4 ].…”

Section: Introductionmentioning

confidence: 99%

Associations between gestational age and childhood sleep: a national retrospective cohort study

Lyu

Groeger

Barnett

et al. 2022

BMC Med

View full text Add to dashboard Cite

Background Both sleep quality and quantity are essential for normal brain development throughout childhood; however, the association between preterm birth and sleep problems in preschoolers is not yet clear, and the effects of gestational age across the full range from preterm to post-term have not been examined. Our study investigated the sleep outcomes of children born at very-preterm (<31 weeks), moderate-preterm (32–33 weeks), late-preterm (34–36 weeks), early-term (37–38 weeks), full-term (39–40 weeks), late-term (41 weeks) and post-term (>41 weeks). Methods A national retrospective cohort study was conducted with 114,311 children aged 3–5 years old in China. Children’s daily sleep hours and pediatric sleep disorders defined by the Children’s Sleep Habits Questionnaire (CSHQ) were reported by parents. Linear regressions and logistic regression models were applied to examine gestational age at birth with the sleep outcomes of children. Results Compared with full-term children, a significantly higher CSHQ score, and hence worse sleep, was observed in very-preterm (β = 1.827), moderate-preterm (β = 1.409), late-preterm (β = 0.832), early-term (β = 0.233) and post-term (β = 0.831) children, all p<0.001. The association of pediatric sleep disorder (i.e. CSHQ scores>41) was also seen in very-preterm (adjusted odds ratio [AOR] = 1.287 95% confidence interval [CI] (1.157, 1.433)), moderate-preterm (AOR = 1.249 95% CI (1.110, 1.405)), late-preterm (AOR = 1.111 95% CI (1.052, 1.174)) and post-term (AOR = 1.139 95% CI (1.061, 1.222)), all p<0.001. Shorter sleep duration was also found in very-preterm (β = −0.303), moderate-preterm (β = −0.282), late-preterm (β = −0.201), early-term (β = −0.068) and post-term (β = −0.110) compared with full-term children, all p<0.01. Preterm and post-term-born children had different sleep profiles as suggested by subscales of the CSHQ. Conclusions Every degree of premature, early-term and post-term birth, compared to full-term, has an association with sleep disorders and shortened daily sleep duration. Preterm, early-term, and post-term should therefore all be monitored with an increased threat of sleep disorder that requires long-term monitoring for adverse sleep outcomes in preschoolers.

show abstract

The value of cardiorespiratory parameters for sleep state classification in preterm infants: A systematic review

Cited by 22 publications

References 78 publications

Automatic Detection of the Cyclic Alternating Pattern of Sleep and Diagnosis of Sleep-Related Pathologies Based on Cardiopulmonary Resonance Indices

Automatic Detection of the Cyclic Alternating Pattern of Sleep and Diagnosis of Sleep-Related Pathologies Based on Cardiopulmonary Resonance Indices

The Sleep Well Baby project: an automated real-time sleep–wake state prediction algorithm in preterm infants

Associations between gestational age and childhood sleep: a national retrospective cohort study

Contact Info

Product

Resources

About