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Huang D, Yu D, Zeng Y, Song X, Pan L, He J, Ren L, Yang J, Lu H, Wang W. Generalized Camera-Based Infant Sleep-Wake Monitoring in NICUs: A Multi-Center Clinical Trial. IEEE J Biomed Health Inform 2024; 28:3015-3028. [PMID: 38446652 DOI: 10.1109/jbhi.2024.3371687] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/08/2024]
Abstract
The infant sleep-wake behavior is an essential indicator of physiological and neurological system maturity, the circadian transition of which is important for evaluating the recovery of preterm infants from inadequate physiological function and cognitive disorders. Recently, camera-based infant sleep-wake monitoring has been investigated, but the challenges of generalization caused by variance in infants and clinical environments are not addressed for this application. In this paper, we conducted a multi-center clinical trial at four hospitals to improve the generalization of camera-based infant sleep-wake monitoring. Using the face videos of 64 term and 39 preterm infants recorded in NICUs, we proposed a novel sleep-wake classification strategy, called consistent deep representation constraint (CDRC), that forces the convolutional neural network (CNN) to make consistent predictions for the samples from different conditions but with the same label, to address the variances caused by infants and environments. The clinical validation shows that by using CDRC, all CNN backbones obtain over 85% accuracy, sensitivity, and specificity in both the cross-age and cross-environment experiments, improving the ones without CDRC by almost 15% in all metrics. This demonstrates that by improving the consistency of the deep representation of samples with the same state, we can significantly improve the generalization of infant sleep-wake classification.
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Huang X, Schmelter F, Irshad MT, Piet A, Nisar MA, Sina C, Grzegorzek M. Optimizing sleep staging on multimodal time series: Leveraging borderline synthetic minority oversampling technique and supervised convolutional contrastive learning. Comput Biol Med 2023; 166:107501. [PMID: 37742416 DOI: 10.1016/j.compbiomed.2023.107501] [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: 07/03/2023] [Revised: 08/15/2023] [Accepted: 09/15/2023] [Indexed: 09/26/2023]
Abstract
Sleep is an important research area in nutritional medicine that plays a crucial role in human physical and mental health restoration. It can influence diet, metabolism, and hormone regulation, which can affect overall health and well-being. As an essential tool in the sleep study, the sleep stage classification provides a parsing of sleep architecture and a comprehensive understanding of sleep patterns to identify sleep disorders and facilitate the formulation of targeted sleep interventions. However, the class imbalance issue is typically salient in sleep datasets, which severely affects classification performances. To address this issue and to extract optimal multimodal features of EEG, EOG, and EMG that can improve the accuracy of sleep stage classification, a Borderline Synthetic Minority Oversampling Technique (B-SMOTE)-Based Supervised Convolutional Contrastive Learning (BST-SCCL) is proposed, which can avoid the risk of data mismatch between various sleep knowledge domains (varying health conditions and annotation rules) and strengthening learning characteristics of the N1 stage from the pair-wise segments comparison strategy. The lightweight residual network architecture with a novel truncated cross-entropy loss function is designed to accommodate multimodal time series and boost the training speed and performance stability. The proposed model has been validated on four well-known public sleep datasets (Sleep-EDF-20, Sleep-EDF-78, ISRUC-1, and ISRUC-3) and its superior performance (overall accuracy of 91.31-92.34%, MF1 of 88.21-90.08%, and Cohen's Kappa coefficient k of 0.87-0.89) has further demonstrated its effectiveness. It shows the great potential of contrastive learning for cross-domain knowledge interaction in precision medicine.
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Affiliation(s)
- Xinyu Huang
- Institute of Medical Informatics, University of Lübeck, Germany.
| | - Franziska Schmelter
- Institute of Nutritional Medicine, University of Lübeck and University Medical Center Schleswig-Holstein, Lübeck, Germany.
| | | | - Artur Piet
- Institute of Medical Informatics, University of Lübeck, Germany.
| | | | - Christian Sina
- Institute of Nutritional Medicine, University of Lübeck and University Medical Center Schleswig-Holstein, Lübeck, Germany; Fraunhofer Research Institution for Individualized and Cell-Based Medical Engineering (IMTE), Lübeck, Germany.
| | - Marcin Grzegorzek
- Institute of Medical Informatics, University of Lübeck, Germany; Fraunhofer Research Institution for Individualized and Cell-Based Medical Engineering (IMTE), Lübeck, Germany.
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Huang X, Shirahama K, Irshad MT, Nisar MA, Piet A, Grzegorzek M. Sleep Stage Classification in Children Using Self-Attention and Gaussian Noise Data Augmentation. SENSORS (BASEL, SWITZERLAND) 2023; 23:3446. [PMID: 37050506 PMCID: PMC10098613 DOI: 10.3390/s23073446] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/27/2023] [Revised: 03/20/2023] [Accepted: 03/22/2023] [Indexed: 06/19/2023]
Abstract
The analysis of sleep stages for children plays an important role in early diagnosis and treatment. This paper introduces our sleep stage classification method addressing the following two challenges: the first is the data imbalance problem, i.e., the highly skewed class distribution with underrepresented minority classes. For this, a Gaussian Noise Data Augmentation (GNDA) algorithm was applied to polysomnography recordings to seek the balance of data sizes for different sleep stages. The second challenge is the difficulty in identifying a minority class of sleep stages, given their short sleep duration and similarities to other stages in terms of EEG characteristics. To overcome this, we developed a DeConvolution- and Self-Attention-based Model (DCSAM) which can inverse the feature map of a hidden layer to the input space to extract local features and extract the correlations between all possible pairs of features to distinguish sleep stages. The results on our dataset show that DCSAM based on GNDA obtains an accuracy of 90.26% and a macro F1-score of 86.51% which are higher than those of our previous method. We also tested DCSAM on a well-known public dataset-Sleep-EDFX-to prove whether it is applicable to sleep data from adults. It achieves a comparable performance to state-of-the-art methods, especially accuracies of 91.77%, 92.54%, 94.73%, and 95.30% for six-stage, five-stage, four-stage, and three-stage classification, respectively. These results imply that our DCSAM based on GNDA has a great potential to offer performance improvements in various medical domains by considering the data imbalance problems and correlations among features in time series data.
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Affiliation(s)
- Xinyu Huang
- Institute of Medical Informatics, University of Lübeck, Ratzeburger Allee 160, 23562 Lübeck, Germany
| | - Kimiaki Shirahama
- Department of Informatics, Kindai University, 3-4-1 Kowakae, Higashiosaka City 577-8502, Osaka, Japan
| | - Muhammad Tausif Irshad
- Institute of Medical Informatics, University of Lübeck, Ratzeburger Allee 160, 23562 Lübeck, Germany
- Department of IT, University of the Punjab, Lahore 54000, Pakistan
| | | | - Artur Piet
- Institute of Medical Informatics, University of Lübeck, Ratzeburger Allee 160, 23562 Lübeck, Germany
| | - Marcin Grzegorzek
- Institute of Medical Informatics, University of Lübeck, Ratzeburger Allee 160, 23562 Lübeck, Germany
- Department of Knowledge Engineering, University of Economics, Bogucicka 3, 40287 Katowice, Poland
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Chen Y, Zhou E, Wang Y, Wu Y, Xu G, Chen L. The past, present, and future of sleep quality assessment and monitoring. Brain Res 2023; 1810:148333. [PMID: 36931581 DOI: 10.1016/j.brainres.2023.148333] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2023] [Revised: 03/09/2023] [Accepted: 03/12/2023] [Indexed: 03/17/2023]
Abstract
Sleep quality is considered to be an individual's self-satisfaction with all aspects of the sleep experience. Good sleep not only improves a person's physical, mental and daily functional health, but also improves the quality-of-life level to some extent. In contrast, chronic sleep deprivation can increase the risk of diseases such as cardiovascular diseases, metabolic dysfunction and cognitive and emotional dysfunction, and can even lead to increased mortality. The scientific evaluation and monitoring of sleep quality is an important prerequisite for safeguarding and promoting the physiological health of the body. Therefore, we have compiled and reviewed the existing methods and emerging technologies commonly used for subjective and objective evaluation and monitoring of sleep quality, and found that subjective sleep evaluation is suitable for clinical screening and large-scale studies, while objective evaluation results are more intuitive and scientific, and in the comprehensive evaluation of sleep, if we want to get more scientific monitoring results, we should combine subjective and objective monitoring and dynamic monitoring.
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Affiliation(s)
- Yanyan Chen
- School of Physical Education, Jianghan University, Wuhan Hubei, 430056, China
| | - Enyuan Zhou
- School of Physical Education, Jianghan University, Wuhan Hubei, 430056, China
| | - Yu Wang
- School of Physical Education, Jianghan University, Wuhan Hubei, 430056, China
| | - Yuxiang Wu
- School of Physical Education, Jianghan University, Wuhan Hubei, 430056, China
| | - Guodong Xu
- School of Physical Education, Jianghan University, Wuhan Hubei, 430056, China
| | - Lin Chen
- School of Physical Education, Jianghan University, Wuhan Hubei, 430056, China.
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Kim J, Lee WH, Kim SH, Na JY, Lim YH, Cho SH, Cho SH, Park HK. Preclinical trial of noncontact anthropometric measurement using IR-UWB radar. Sci Rep 2022; 12:8174. [PMID: 35581250 PMCID: PMC9112269 DOI: 10.1038/s41598-022-12209-1] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2022] [Accepted: 05/06/2022] [Indexed: 11/08/2022] Open
Abstract
Anthropometric profiles are important indices for assessing medical conditions, including malnutrition, obesity, and growth disorders. Noncontact methods for estimating those parameters could have considerable value in many practical situations, such as the assessment of young, uncooperative infants or children and the prevention of infectious disease transmission. The purpose of this study was to investigate the feasibility of obtaining noncontact anthropometric measurements using the impulse-radio ultrawideband (IR-UWB) radar sensor technique. A total of 45 healthy adults were enrolled, and a convolutional neural network (CNN) algorithm was implemented to analyze data extracted from IR-UWB radar. The differences (root-mean-square error, RMSE) between values from the radar and bioelectrical impedance analysis (BIA) as a reference in the measurement of height, weight, and body mass index (BMI) were 2.78, 5.31, and 2.25, respectively; predicted data from the radar highly agreed with those from the BIA. The intraclass correlation coefficients (ICCs) were 0.93, 0.94, and 0.83. In conclusion, IR-UWB radar can provide accurate estimates of anthropometric parameters in a noncontact manner; this study is the first to support the radar sensor as an applicable method in clinical situations.
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Affiliation(s)
- Jinsup Kim
- Department of Pediatrics, Hanyang University College of Medicine, Seoul, 04763, Republic of Korea
| | - Won Hyuk Lee
- Department of Electronics and Computer Engineering, Hanyang University, Seoul, 04763, Republic of Korea
| | - Seung Hyun Kim
- Department of Pediatrics, Hanyang University College of Medicine, Seoul, 04763, Republic of Korea
| | - Jae Yoon Na
- Department of Pediatrics, Hanyang University College of Medicine, Seoul, 04763, Republic of Korea
| | - Young-Hyo Lim
- Division of Cardiology, Department of Internal Medicine, Hanyang University College of Medicine, Seoul, 04763, Republic of Korea
| | - Seok Hyun Cho
- Department of Otorhinolaryngology, Hanyang University College of Medicine, Seoul, 04763, Republic of Korea
| | - Sung Ho Cho
- Department of Electronics and Computer Engineering, Hanyang University, Seoul, 04763, Republic of Korea.
| | - Hyun-Kyung Park
- Department of Pediatrics, Hanyang University College of Medicine, Seoul, 04763, Republic of Korea.
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Trickett J, Hill C, Austin T, Johnson S. The Impact of Preterm Birth on Sleep through Infancy, Childhood and Adolescence and Its Implications. CHILDREN 2022; 9:children9050626. [PMID: 35626803 PMCID: PMC9139673 DOI: 10.3390/children9050626] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/10/2022] [Revised: 04/12/2022] [Accepted: 04/19/2022] [Indexed: 11/16/2022]
Abstract
There is emergent literature on the relationship between the development of sleep-wake cycles, sleep architecture, and sleep duration during the neonatal period on neurodevelopmental outcomes among children born preterm. There is also a growing literature on techniques to assess sleep staging in preterm neonates using either EEG methods or heart and respiration rate. Upon discharge from hospital, sleep in children born preterm has been assessed using parent report, actigraphy, and polysomnography. This review describes the ontogeny and measurement of sleep in the neonatal period, the current evidence on the impact of preterm birth on sleep both in the NICU and in childhood and adolescence, and the interaction between sleep, cognition, and social-emotional outcomes in this population.
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Affiliation(s)
- Jayne Trickett
- Department of Neuroscience, Psychology and Behaviour, University of Leicester, Leicester LE1 7RH, UK
- Correspondence:
| | - Catherine Hill
- School of Clinical Experimental Sciences, Faculty of Medicine, University of Southampton, Southampton SO16 6YD, UK;
- Department of Sleep Medicine, Southampton Children’s Hospital, Southampton SO17 1BJ, UK
| | - Topun Austin
- Neonatal Intensive Care Unit, Rosie Hospital, Cambridge University Hospitals NHS Foundation Trust, Cambridge Biomedical Campus, Cambridge CB2 0QQ, UK;
| | - Samantha Johnson
- Department of Health Sciences, University of Leicester, Leicester LE1 7RH, UK;
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Na JY, Lee WH, Lim YH, Cho SH, Cho SH, Park HK. Early screening tool for developmental delay in infancy: Quantified assessment of movement asymmetry using IR-UWB radar. Front Pediatr 2022; 10:731534. [PMID: 36313883 PMCID: PMC9614076 DOI: 10.3389/fped.2022.731534] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/27/2021] [Accepted: 09/26/2022] [Indexed: 12/01/2022] Open
Abstract
In the untact COVID-19 era, the feasibility of a noncontact, impulse-radio ultrawideband (IR-UWB) radar sensor has important medical implications. Premature birth is a major risk factor for brain injury and developmental delay; therefore, early intervention is crucial for potentially achieving better developmental outcomes. Early detection and screening tests in infancy are limited to the quantification of differences between normal and spastic movements. This study investigated the quantified asymmetry in the general movements of an infant with hydrocephalus and proposes IR-UWB radar as a novel, early screening tool for developmental delay. To support this state-of-the-art technology, data from actigraphy and video camcorder recordings were adopted simultaneously to compare relevant time series as the infant grew. The data from the three different methods were highly concordant; specifically, the ρz values comparing radar and actigraphy, which served as the reference for measuring movements, showed excellent agreement, with values of 0.66 on the left and 0.56 on the right. The total amount of movement measured by radar over time increased overall; movements were almost dominant on the left at first (75.2% of total movements), but following shunt surgery, the frequency of movement on both sides was similar (54.8% of total movements). As the hydrocephalus improved, the lateralization of movement on radar began to coincide with the clinical features. These results support the important complementary role of this radar system in predicting motor disorders very early in life.
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Affiliation(s)
- Jae Yoon Na
- Department of Pediatrics, Hanyang University College of Medicine, Seoul, South Korea
| | - Won Hyuk Lee
- Department of Electronics and Computer Engineering, Hanyang University, Seoul, South Korea
| | - Young-Hyo Lim
- Division of Cardiology, Department of Internal Medicine, Hanyang University College of Medicine, Seoul, South Korea
| | - Seok Hyun Cho
- Department of Otorhinolaryngology-Head and Neck Surgery, Hanyang University College of Medicine, Seoul, South Korea
| | - Sung Ho Cho
- Department of Electronics and Computer Engineering, Hanyang University, Seoul, South Korea
| | - Hyun-Kyung Park
- Department of Pediatrics, Hanyang University College of Medicine, Seoul, South Korea
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