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Abbasi SF, Abbas A, Ahmad I, Alshehri MS, Almakdi S, Ghadi YY, Ahmad J. Automatic neonatal sleep stage classification: A comparative study. Heliyon 2023; 9:e22195. [PMID: 38058619 PMCID: PMC10695968 DOI: 10.1016/j.heliyon.2023.e22195] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2023] [Revised: 09/21/2023] [Accepted: 11/06/2023] [Indexed: 12/08/2023] Open
Abstract
Sleep is an essential feature of living beings. For neonates, it is vital for their mental and physical development. Sleep stage cycling is an important parameter to assess neonatal brain and physical development. Therefore, it is crucial to administer newborn's sleep in the neonatal intensive care unit (NICU). Currently, Polysomnography (PSG) is used as a gold standard method for classifying neonatal sleep patterns, but it is expensive and requires a lot of human involvement. Over the last two decades, multiple researchers are working on automatic sleep stage classification algorithms using electroencephalography (EEG), electrocardiography (ECG), and video. In this study, we present a comprehensive review of existing algorithms for neonatal sleep, their limitations and future recommendations. Additionally, a brief comparison of the extracted features, classification algorithms and evaluation parameters is reported in the proposed study.
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Affiliation(s)
- Saadullah Farooq Abbasi
- Department of Electronic, Electrical and System Engineering, University of Birmingham, Birmingham, United Kingdom
| | - Awais Abbas
- Department of Electronic, Electrical and System Engineering, University of Birmingham, Birmingham, United Kingdom
| | - Iftikhar Ahmad
- James Watt School of Engineering, University of Glasgow, United Kingdom
| | - Mohammed S. Alshehri
- Department of Computer Science, College of Computer Science and Information Systems, Najran University, Najran, Saudi Arabia
| | - Sultan Almakdi
- Department of Computer Science, College of Computer Science and Information Systems, Najran University, Najran, Saudi Arabia
| | - Yazeed Yasin Ghadi
- Department of Computer Science, Al Ain University, Abu Dhabi P.O. Box 112612, United Arab Emirates
| | - Jawad Ahmad
- School of Computing, Engineering and the Built Environment, Edinburgh Napier University, Edinburgh EH10 5DT, UK
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Bandyopadhyay A, Goldstein C. Clinical applications of artificial intelligence in sleep medicine: a sleep clinician's perspective. Sleep Breath 2023; 27:39-55. [PMID: 35262853 PMCID: PMC8904207 DOI: 10.1007/s11325-022-02592-4] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2021] [Revised: 01/25/2022] [Accepted: 03/02/2022] [Indexed: 12/27/2022]
Abstract
BACKGROUND The past few years have seen a rapid emergence of artificial intelligence (AI)-enabled technology in the field of sleep medicine. AI refers to the capability of computer systems to perform tasks conventionally considered to require human intelligence, such as speech recognition, decision-making, and visual recognition of patterns and objects. The practice of sleep tracking and measuring physiological signals in sleep is widely practiced. Therefore, sleep monitoring in both the laboratory and ambulatory environments results in the accrual of massive amounts of data that uniquely positions the field of sleep medicine to gain from AI. METHOD The purpose of this article is to provide a concise overview of relevant terminology, definitions, and use cases of AI in sleep medicine. This was supplemented by a thorough review of relevant published literature. RESULTS Artificial intelligence has several applications in sleep medicine including sleep and respiratory event scoring in the sleep laboratory, diagnosing and managing sleep disorders, and population health. While still in its nascent stage, there are several challenges which preclude AI's generalizability and wide-reaching clinical applications. Overcoming these challenges will help integrate AI seamlessly within sleep medicine and augment clinical practice. CONCLUSION Artificial intelligence is a powerful tool in healthcare that may improve patient care, enhance diagnostic abilities, and augment the management of sleep disorders. However, there is a need to regulate and standardize existing machine learning algorithms prior to its inclusion in the sleep clinic.
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Affiliation(s)
- Anuja Bandyopadhyay
- Department of Pediatrics, Indiana University School of Medicine, Indianapolis, IN, USA.
| | - Cathy Goldstein
- Department of Neurology, University of Michigan, Ann Arbor, MI, USA
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Artificial Intelligence and Women Researchers in the Czech Republic. APPLIED SCIENCES-BASEL 2022. [DOI: 10.3390/app12031465] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/04/2023]
Abstract
Artificial intelligence as a research area has been continuously growing for several decades. Many applications were developed in various domains. Medicine and health care have attracted more intensive attention thanks to rapid technological development that has accelerated generation of large volumes of data requiring intelligent analysis and evaluation. This article illustrates, through examples of women researchers and selected AI projects in medicine, the wide spectrum of applications developed during the last fifteen years in the Czech Republic, and in particular at the Czech Technical University in Prague. Women researchers played an important and irreplaceable role since the advent of AI research in the Czech Republic. By their example, they motivated many young female students to join the community and start their research career in the AI area. They frequently participated in research projects led by the senior women researchers. The presented overview of projects illustrates the diversity of the medical area and the potential of AI methods that can be used for solving data- and knowledge-intensive problems. We briefly touch on the AI study programs. In conclusion, we point out the future challenges in AI and its applications in medicine and health care.
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Zanker A, Wöhr AC, Reese S, Erhard M. Qualitative and quantitative analyses of polysomnographic measurements in foals. Sci Rep 2021; 11:16288. [PMID: 34381127 PMCID: PMC8357810 DOI: 10.1038/s41598-021-95770-5] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2020] [Accepted: 07/28/2021] [Indexed: 11/09/2022] Open
Abstract
Veterinary and human medicine are still seeking a conclusive explanation of the function of sleep, including the change in sleep behaviour over the course of an individual's lifetime. In human medicine, sleep disorders and abnormalities in the electroencephalogram are used for prognostic statements, therapeutic means and diagnoses. To facilitate such use in foal medicine, we monitored 10 foals polysomnographically for 48 h. Via 10 attached cup electrodes, brain waves were recorded by electroencephalography, eye movements by electrooculography and muscle activity by electromyography. Wireless polysomnographs allowed us to measure the foals in their home stables. In addition, each foal was simultaneously monitored with infrared video cameras. By combining the recorded data, we determined the time budgeting of the foals over 48 h, whereby the states of vigilance were divided into wakefulness, light sleep, slow-wave sleep and rapid-eye-movement sleep, and the body positions into standing, suckling, sternal recumbency and lateral recumbency. The results of the qualitative analyses showed that the brain waves of the foals differ in their morphology from those previously reported for adult horses. The quantitative data analyses revealed that foals suckle throughout all periods of the day, including night-time. The results of our combined measurements allow optimizing the daily schedule of the foals according to their sleep and activity times. We recommend that stall rest should begin no later than 9.00 p.m. and daily stable work should be done in the late afternoon.
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Affiliation(s)
- Antonia Zanker
- Tierärztliche Klinik Für Pferde Wolfesing, Wolfesing 12, 85604, Zorneding, Germany.
| | - Anna-Caroline Wöhr
- Chair of Animal Welfare, Animal Behaviour, Animal Hygiene and Animal Husbandry, Department of Veterinary Sciences, Faculty of Veterinary Medicine, LMU Munich, Veterinärstraße 13 R, 80539, München, Germany
| | - Sven Reese
- Chair of Anatomy, Histology and Embryology, Department of Veterinary Sciences, Faculty of Veterinary Medicine, LMU Munich, Veterinärstraße 13 R, 80539, München, Germany
| | - Michael Erhard
- Chair of Animal Welfare, Animal Behaviour, Animal Hygiene and Animal Husbandry, Department of Veterinary Sciences, Faculty of Veterinary Medicine, LMU Munich, Veterinärstraße 13 R, 80539, München, Germany
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Ranta J, Airaksinen M, Kirjavainen T, Vanhatalo S, Stevenson NJ. An Open Source Classifier for Bed Mattress Signal in Infant Sleep Monitoring. Front Neurosci 2021; 14:602852. [PMID: 33519357 PMCID: PMC7840576 DOI: 10.3389/fnins.2020.602852] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2020] [Accepted: 12/15/2020] [Indexed: 01/23/2023] Open
Abstract
Objective To develop a non-invasive and clinically practical method for a long-term monitoring of infant sleep cycling in the intensive care unit. Methods Forty three infant polysomnography recordings were performed at 1–18 weeks of age, including a piezo element bed mattress sensor to record respiratory and gross-body movements. The hypnogram scored from polysomnography signals was used as the ground truth in training sleep classifiers based on 20,022 epochs of movement and/or electrocardiography signals. Three classifier designs were evaluated in the detection of deep sleep (N3 state): support vector machine (SVM), Long Short-Term Memory neural network, and convolutional neural network (CNN). Results Deep sleep was accurately identified from other states with all classifier variants. The SVM classifier based on a combination of movement and electrocardiography features had the highest performance (AUC 97.6%). A SVM classifier based on only movement features had comparable accuracy (AUC 95.0%). The feature-independent CNN resulted in roughly comparable accuracy (AUC 93.3%). Conclusion Automated non-invasive tracking of sleep state cycling is technically feasible using measurements from a piezo element situated under a bed mattress. Significance An open source infant deep sleep detector of this kind allows quantitative, continuous bedside assessment of infant’s sleep cycling.
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Affiliation(s)
- Jukka Ranta
- Department of Clinical Neurophysiology, BABA Center, Children's Hospital, Helsinki University Hospital and University of Helsinki, Helsinki, Finland.,Department of Signal Processing and Acoustics, Aalto University, Espoo, Finland
| | - Manu Airaksinen
- Department of Clinical Neurophysiology, BABA Center, Children's Hospital, Helsinki University Hospital and University of Helsinki, Helsinki, Finland.,Department of Signal Processing and Acoustics, Aalto University, Espoo, Finland
| | - Turkka Kirjavainen
- Department of Paediatrics, Children's Hospital Helsinki University Hospital, Helsinki, Finland
| | - Sampsa Vanhatalo
- Department of Clinical Neurophysiology, BABA Center, Children's Hospital, Helsinki University Hospital and University of Helsinki, Helsinki, Finland.,Neuroscience Center, Helsinki Institute of Life Science, University of Helsinki, Helsinki, Finland
| | - Nathan J Stevenson
- Brain Modeling Group, QIMR Berghofer Medical Research Institute, Brisbane, QLD, Australia
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Gerla V, Kremen V, Covassin N, Lhotska L, Saifutdinova E, Bukartyk J, Marik V, Somers V. Automatic identification of artifacts and unwanted physiologic signals in EEG and EOG during wakefulness. Biomed Signal Process Control 2017. [DOI: 10.1016/j.bspc.2016.09.006] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
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Werth J, Atallah L, Andriessen P, Long X, Zwartkruis-Pelgrim E, Aarts RM. Unobtrusive sleep state measurements in preterm infants - A review. Sleep Med Rev 2016; 32:109-122. [PMID: 27318520 DOI: 10.1016/j.smrv.2016.03.005] [Citation(s) in RCA: 54] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/12/2015] [Revised: 03/25/2016] [Accepted: 03/29/2016] [Indexed: 01/26/2023]
Abstract
Sleep is important for the development of preterm infants. During sleep, neural connections are formed and the development of brain regions is triggered. In general, various rudimentary sleep states can be identified in the preterm infant, namely active sleep (AS), quiet sleep (QS) and intermediate sleep (IS). As the infant develops, sleep states change in length and organization, with these changes as important indicators of brain development. As a result, several methods have been deployed to distinguish between the different preterm infant sleep states, among which polysomnography (PSG) is the most frequently used. However, this method is limited by the use of adhesive electrodes or patches that are attached to the body by numerous cables that can disturb sleep. Given the importance of sleep, this review explores more unobtrusive methods that can identify sleep states without disturbing the infant. To this end, after a brief introduction to preterm sleep states, an analysis of the physiological characteristics associated with the different sleep states is provided and various methods of measuring these physiological characteristics are explored. Finally, the advantages and disadvantages of each of these methods are evaluated and recommendations for neonatal sleep monitoring proposed.
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Affiliation(s)
- Jan Werth
- Department of Electrical Engineering, University of Technology Eindhoven, De Zaale, 5612 AJ Eindhoven, The Netherlands; Philips Research, High Tech Campus 34, 5656 AE Eindhoven, The Netherlands.
| | - Louis Atallah
- Philips Research, High Tech Campus 34, 5656 AE Eindhoven, The Netherlands
| | - Peter Andriessen
- Neonatal Intensive Care Unit, Maxima Medical Center, De Run 4600, 5504 DB Veldhoven, The Netherlands; Faculty of Health, Medicine, and Life Science, Maastricht University, Minderbroedersberg 4-6, 6211 LK Maastricht, The Netherlands
| | - Xi Long
- Department of Electrical Engineering, University of Technology Eindhoven, De Zaale, 5612 AJ Eindhoven, The Netherlands; Philips Research, High Tech Campus 34, 5656 AE Eindhoven, The Netherlands.
| | | | - Ronald M Aarts
- Department of Electrical Engineering, University of Technology Eindhoven, De Zaale, 5612 AJ Eindhoven, The Netherlands; Philips Research, High Tech Campus 34, 5656 AE Eindhoven, The Netherlands
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Matic V, Cherian PJ, Koolen N, Naulaers G, Swarte RM, Govaert P, Van Huffel S, De Vos M. Holistic approach for automated background EEG assessment in asphyxiated full-term infants. J Neural Eng 2014; 11:066007. [DOI: 10.1088/1741-2560/11/6/066007] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
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Čić M, Šoda J, Bonković M. Automatic classification of infant sleep based on instantaneous frequencies in a single-channel EEG signal. Comput Biol Med 2013; 43:2110-7. [DOI: 10.1016/j.compbiomed.2013.10.002] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/23/2013] [Revised: 09/30/2013] [Accepted: 10/03/2013] [Indexed: 10/26/2022]
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Zima M, Tichavský P, Paul K, Krajča V. Robust removal of short-duration artifacts in long neonatal EEG recordings using wavelet-enhanced ICA and adaptive combining of tentative reconstructions. Physiol Meas 2012; 33:N39-49. [DOI: 10.1088/0967-3334/33/8/n39] [Citation(s) in RCA: 34] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
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Piryatinska A, Woyczynski WA, Scher MS, Loparo KA. Optimal channel selection for analysis of EEG-sleep patterns of neonates. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2012; 106:14-26. [PMID: 22000642 DOI: 10.1016/j.cmpb.2011.08.002] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/04/2011] [Revised: 07/05/2011] [Accepted: 08/11/2011] [Indexed: 05/31/2023]
Abstract
This paper extends our previous work on automated detection and classification of neonate EEG sleep stages. In [19] we adapted and integrated a range of computational, mathematical and statistical tools for the analysis of neonatal electroencephalogram (EEG) sleep recordings with the aim of facilitating the assessment of neonatal brain maturation and dismaturity by studying the structure and temporal patterns of their sleep. That work relied on algorithms using a single channel of EEG. The present paper builds on our previous work by incorporating a larger selection of EEG channels that capture both the spatial distribution and temporal patterns of EEG during sleep. Using a multivariate analysis approach, we obtain the "optimal" selection of the EEG channels and characteristics that are most suitable for EEG sleep state separation.
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Korotchikova I, Stevenson N, Walsh B, Murray D, Boylan G. Quantitative EEG analysis in neonatal hypoxic ischaemic encephalopathy. Clin Neurophysiol 2011; 122:1671-8. [DOI: 10.1016/j.clinph.2010.12.059] [Citation(s) in RCA: 27] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2010] [Revised: 12/17/2010] [Accepted: 12/18/2010] [Indexed: 10/18/2022]
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Löfhede J, Thordstein M, Löfgren N, Flisberg A, Rosa-Zurera M, Kjellmer I, Lindecrantz K. Automatic classification of background EEG activity in healthy and sick neonates. J Neural Eng 2010; 7:16007. [PMID: 20075506 DOI: 10.1088/1741-2560/7/1/016007] [Citation(s) in RCA: 51] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
Abstract
The overall aim of our research is to develop methods for a monitoring system to be used at neonatal intensive care units. When monitoring a baby, a range of different types of background activity needs to be considered. In this work, we have developed a scheme for automatic classification of background EEG activity in newborn babies. EEG from six full-term babies who were displaying a burst suppression pattern while suffering from the after-effects of asphyxia during birth was included along with EEG from 20 full-term healthy newborn babies. The signals from the healthy babies were divided into four behavioural states: active awake, quiet awake, active sleep and quiet sleep. By using a number of features extracted from the EEG together with Fisher's linear discriminant classifier we have managed to achieve 100% correct classification when separating burst suppression EEG from all four healthy EEG types and 93% true positive classification when separating quiet sleep from the other types. The other three sleep stages could not be classified. When the pathological burst suppression pattern was detected, the analysis was taken one step further and the signal was segmented into burst and suppression, allowing clinically relevant parameters such as suppression length and burst suppression ratio to be calculated. The segmentation of the burst suppression EEG works well, with a probability of error around 4%.
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Affiliation(s)
- Johan Löfhede
- School of Engineering, University College of Borås, Borås, Sweden.
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Fotiadis D, Pattichis CS. Guest editorial: introduction to the special section on biomedical informatics. IEEE TRANSACTIONS ON INFORMATION TECHNOLOGY IN BIOMEDICINE : A PUBLICATION OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY 2009; 13:415-418. [PMID: 19586810 DOI: 10.1109/titb.2009.2025118] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/28/2023]
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