1
|
van Twist E, Hiemstra FW, Cramer AB, Verbruggen SC, Tax DM, Joosten K, Louter M, Straver DC, de Hoog M, Kuiper JW, de Jonge RC. An electroencephalography-based sleep index and supervised machine learning as a suitable tool for automated sleep classification in children. J Clin Sleep Med 2024; 20:389-397. [PMID: 37869968 PMCID: PMC11019221 DOI: 10.5664/jcsm.10880] [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: 08/22/2023] [Revised: 10/18/2023] [Accepted: 10/18/2023] [Indexed: 10/24/2023]
Abstract
STUDY OBJECTIVES Although sleep is frequently disrupted in the pediatric intensive care unit, it is currently not possible to perform real-time sleep monitoring at the bedside. In this study, spectral band powers of electroencephalography data are used to derive a simple index for sleep classification. METHODS Retrospective study at Erasmus MC Sophia Children's Hospital, using hospital-based polysomnography recordings obtained in non-critically ill children between 2017 and 2021. Six age categories were defined: 6-12 months, 1-3 years, 3-5 years, 5-9 years, 9-13 years, and 13-18 years. Candidate index measures were derived by calculating spectral band powers in different frequent frequency bands of smoothed electroencephalography. With the best performing index, sleep classification models were developed for two, three, and four states via decision tree and five-fold nested cross-validation. Model performance was assessed across age categories and electroencephalography channels. RESULTS In total 90 patients with polysomnography were included, with a mean (standard deviation) recording length of 10.3 (1.1) hours. The best performance was obtained with the gamma to delta spectral power ratio of the F4-A1 and F3-A1 channels with smoothing. Balanced accuracy was 0.88, 0.74, and 0.57 for two-, three-, and four-state classification. Across age categories, balanced accuracy ranged between 0.83 and 0.92 and 0.72 and 0.77 for two- and three-state classification, respectively. CONCLUSIONS We propose an interpretable and generalizable sleep index derived from single-channel electroencephalography for automated sleep monitoring at the bedside in non-critically ill children ages 6 months to 18 years, with good performance for two- and three-state classification. CITATION van Twist E, Hiemstra FW, Cramer ABG, et al. An electroencephalography-based sleep index and supervised machine learning as a suitable tool for automated sleep classification in children. J Clin Sleep Med. 2024;20(3):389-397.
Collapse
Affiliation(s)
- Eris van Twist
- Department of Neonatal and Pediatric Intensive Care, Division of Pediatric Intensive Care, Erasmus MC Sophia Children’s Hospital, Rotterdam, The Netherlands
| | - Floor W. Hiemstra
- Department of Intensive Care, Leiden University Medical Centre, Leiden, The Netherlands
- Laboratory for Neurophysiology, Department of Cellular and Chemical Biology, Leiden University Medical Center, Leiden, The Netherlands
| | - Arnout B.G. Cramer
- Department of Neonatal and Pediatric Intensive Care, Division of Pediatric Intensive Care, Erasmus MC Sophia Children’s Hospital, Rotterdam, The Netherlands
| | - Sascha C.A.T. Verbruggen
- Department of Neonatal and Pediatric Intensive Care, Division of Pediatric Intensive Care, Erasmus MC Sophia Children’s Hospital, Rotterdam, The Netherlands
| | - David M.J. Tax
- Pattern Recognition Laboratory, Delft University of Technology, Delft, The Netherlands
| | - Koen Joosten
- Department of Neonatal and Pediatric Intensive Care, Division of Pediatric Intensive Care, Erasmus MC Sophia Children’s Hospital, Rotterdam, The Netherlands
| | - Maartje Louter
- Division of Clinical Neurophysiology, Department of Neurology, Erasmus MC, Rotterdam, The Netherlands
| | - Dirk C.G. Straver
- Division of Clinical Neurophysiology, Department of Neurology, Erasmus MC, Rotterdam, The Netherlands
| | - Matthijs de Hoog
- Department of Neonatal and Pediatric Intensive Care, Division of Pediatric Intensive Care, Erasmus MC Sophia Children’s Hospital, Rotterdam, The Netherlands
| | - Jan Willem Kuiper
- Department of Neonatal and Pediatric Intensive Care, Division of Pediatric Intensive Care, Erasmus MC Sophia Children’s Hospital, Rotterdam, The Netherlands
| | - Rogier C.J. de Jonge
- Department of Neonatal and Pediatric Intensive Care, Division of Pediatric Intensive Care, Erasmus MC Sophia Children’s Hospital, Rotterdam, The Netherlands
| |
Collapse
|
2
|
Nilashi M, Abumalloh RA, Ahmadi H, Samad S, Alghamdi A, Alrizq M, Alyami S, Nayer FK. Electroencephalography (EEG) eye state classification using learning vector quantization and bagged trees. Heliyon 2023; 9:e15258. [PMID: 37101630 PMCID: PMC10123194 DOI: 10.1016/j.heliyon.2023.e15258] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2023] [Revised: 03/30/2023] [Accepted: 03/31/2023] [Indexed: 04/08/2023] Open
Abstract
The analysis of Electroencephalography (EEG) signals has been an effective way of eye state identification. Its significance is highlighted by studies that examined the classification of eye states using machine learning techniques. In previous studies, supervised learning techniques have been widely used in EEG signals analysis for eye state classification. Their main goal has been the improvement of classification accuracy through the use of novel algorithms. The trade-off between classification accuracy and computation complexity is an important task in EEG signals analysis. In this paper, a hybrid method that can handle multivariate signals and non-linear is proposed with supervised and un-supervised learning to achieve a fast EEG eye state classification with high prediction accuracy to provide real-time decision-making applicability. We use the Learning Vector Quantization (LVQ) technique and bagged tree techniques. The method was evaluated on a real-world EEG dataset which included 14976 instances after the removal of outlier instances. Using LVQ, 8 clusters were generated from the data. The bagged tree was applied on 8 clusters and compared with other classifiers. Our experiments revealed that LVQ combined with the bagged tree provides the best results (Accuracy = 0.9431) compared with the bagged tree, CART (Classification And Regression Tree) (Accuracy = 0.8200), LDA (Linear Discriminant Analysis) (Accuracy = 0.7931), Random Trees (Accuracy = 0.8311), Naïve Bayes (Accuracy = 0.8331) and Multilayer Perceptron (Accuracy = 0.7718), which demonstrates the effectiveness of incorporating ensemble learning and clustering approaches in the analysis of EEG signals. We also provided the time complexity of the methods for prediction speed (Observation/Second). The result showed that LVQ + Bagged Tree provides the best result for prediction speed (58942 Obs/Sec) in relation to Bagged Tree (28453 Obs/Sec), CART (27784 Obs/Sec), LDA (26435 Obs/Sec), Random Trees (27921), Naïve Bayes (27217) and Multilayer Perceptron (24163).
Collapse
|
3
|
Pauletto P, Polmann H, Conti Réus J, Massignan C, de Souza BDM, Gozal D, Lavigne G, Flores-Mir C, De Luca Canto G. Sleep bruxism and obstructive sleep apnea: association, causality or spurious finding? A scoping review. Sleep 2022; 45:6571501. [DOI: 10.1093/sleep/zsac073] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2021] [Revised: 02/26/2022] [Indexed: 11/14/2022] Open
Abstract
Abstract
Study Objectives
To evaluate the available evidence on the putative relationships between sleep bruxism (SB) and, obstructive sleep apnea (OSA) to assess the extent of research on this topic, and to formulate suggestions for future research.
Methods
A scoping review including studies examining temporal and overall association and prevalence of SB and OSA was performed. Six main databases and gray literature were searched. The studies selection was conducted by three independent reviewers. A narrative synthesis of the results was carried out.
Results
Thirteen studies in adults and eight studies in children were finally included. The median of concomitant conditions prevalence was 39.3% in adults and 26.1% in children. Marked methodological variability was identified among studies in adults and even more when we compared detection methods in children. No significant association between OSA and SB emerged in most studies in adults, while an association may be possible in children.
Conclusions
Based on the current literature, it is not possible to confirm that there is a relationship between SB and OSA in adults. In patients under pediatric care, although this association seems plausible, there is currently insufficient supportive evidence. Standardized validated methodologies for identifying SB should be consistently used in both populations before reaching any conclusion regarding such association. Furthermore, assessment of shared phenotypes between patients with SB and patients with OSA may reveal new insights that will contribute to personalized approaches aiming to optimize the management of such comorbidities.
Collapse
Affiliation(s)
- Patrícia Pauletto
- Department of Dentistry, Federal University of Santa Catarina , Florianópolis , Brazil
- Brazilian Centre for Evidence-Based Research (COBE), Federal University of Santa Catarina , Florianópolis , Brazil
| | - Helena Polmann
- Department of Dentistry, Federal University of Santa Catarina , Florianópolis , Brazil
- Brazilian Centre for Evidence-Based Research (COBE), Federal University of Santa Catarina , Florianópolis , Brazil
| | - Jéssica Conti Réus
- Department of Dentistry, Federal University of Santa Catarina , Florianópolis , Brazil
- Brazilian Centre for Evidence-Based Research (COBE), Federal University of Santa Catarina , Florianópolis , Brazil
| | - Carla Massignan
- Brazilian Centre for Evidence-Based Research (COBE), Federal University of Santa Catarina , Florianópolis , Brazil
- Department of Dentistry, University of Brasília , Brasília , Brazil
| | | | - David Gozal
- Department of Child Health, University of Missouri , Columbia, Missouri , United States
| | - Gilles Lavigne
- Department of Dentistry, Faculty of Dental Medicine, Université de Montreal, Montréal , Canada
| | | | - Graziela De Luca Canto
- Department of Dentistry, Federal University of Santa Catarina , Florianópolis , Brazil
- Brazilian Centre for Evidence-Based Research (COBE), Federal University of Santa Catarina , Florianópolis , Brazil
| |
Collapse
|
4
|
Ketu S, Mishra PK. Hybrid classification model for eye state detection using electroencephalogram signals. Cogn Neurodyn 2022; 16:73-90. [PMID: 35126771 PMCID: PMC8807771 DOI: 10.1007/s11571-021-09678-x] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2020] [Revised: 03/16/2021] [Accepted: 04/05/2021] [Indexed: 02/03/2023] Open
Abstract
The electroencephalography (EEG) signal is an essential source of Brain-Computer Interface (BCI) technology implementation. The BCI is nothing but a non-muscle communication medium among the external devices and the brain. The basic concept of BCI is to enable the interaction among the neurological ill patients to others with the help of brain signals. EEG signal classification is an essential requirement for various applications such as motor imagery classification, drug effects diagnosis, emotion classification, seizure prediction/detection, eye state prediction/detection, and so on. Thus, there is a need for an efficient classification model that can deal with the EEG datasets more adequately with better classification accuracy, which will further help in developing the automatic solution for the medical domain. In this paper, we have introduced a hybrid classification model for eye state detection using electroencephalogram (EEG) signals. This hybrid classification model has been evaluated with the other traditional machine learning models, eight classification models (Prepossessed + Hypertuned) and six state-of-the-art methods to assess its appropriateness and correctness. This proposed classification model establishes a machine learning-based hybrid model for the classification of eye state using EEG signals with greater exactness. It is also capable of solving the issue of outlier detection and removal to address the class imbalance problem, which will offer the solution toward building the robotic or smart machine-based solution for social well-being.
Collapse
Affiliation(s)
- Shwet Ketu
- Department of Computer Science, Institute of Science, Banaras Hindu University, Varanasi, India
| | - Pramod Kumar Mishra
- Department of Computer Science, Institute of Science, Banaras Hindu University, Varanasi, India
| |
Collapse
|
5
|
Review of Wearable Devices and Data Collection Considerations for Connected Health. SENSORS 2021; 21:s21165589. [PMID: 34451032 PMCID: PMC8402237 DOI: 10.3390/s21165589] [Citation(s) in RCA: 64] [Impact Index Per Article: 21.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/04/2021] [Revised: 07/22/2021] [Accepted: 08/02/2021] [Indexed: 12/16/2022]
Abstract
Wearable sensor technology has gradually extended its usability into a wide range of well-known applications. Wearable sensors can typically assess and quantify the wearer’s physiology and are commonly employed for human activity detection and quantified self-assessment. Wearable sensors are increasingly utilised to monitor patient health, rapidly assist with disease diagnosis, and help predict and often improve patient outcomes. Clinicians use various self-report questionnaires and well-known tests to report patient symptoms and assess their functional ability. These assessments are time consuming and costly and depend on subjective patient recall. Moreover, measurements may not accurately demonstrate the patient’s functional ability whilst at home. Wearable sensors can be used to detect and quantify specific movements in different applications. The volume of data collected by wearable sensors during long-term assessment of ambulatory movement can become immense in tuple size. This paper discusses current techniques used to track and record various human body movements, as well as techniques used to measure activity and sleep from long-term data collected by wearable technology devices.
Collapse
|
6
|
Alvarez-Estevez D, Rijsman RM. Inter-database validation of a deep learning approach for automatic sleep scoring. PLoS One 2021; 16:e0256111. [PMID: 34398931 PMCID: PMC8366993 DOI: 10.1371/journal.pone.0256111] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2021] [Accepted: 08/01/2021] [Indexed: 12/17/2022] Open
Abstract
STUDY OBJECTIVES Development of inter-database generalizable sleep staging algorithms represents a challenge due to increased data variability across different datasets. Sharing data between different centers is also a problem due to potential restrictions due to patient privacy protection. In this work, we describe a new deep learning approach for automatic sleep staging, and address its generalization capabilities on a wide range of public sleep staging databases. We also examine the suitability of a novel approach that uses an ensemble of individual local models and evaluate its impact on the resulting inter-database generalization performance. METHODS A general deep learning network architecture for automatic sleep staging is presented. Different preprocessing and architectural variant options are tested. The resulting prediction capabilities are evaluated and compared on a heterogeneous collection of six public sleep staging datasets. Validation is carried out in the context of independent local and external dataset generalization scenarios. RESULTS Best results were achieved using the CNN_LSTM_5 neural network variant. Average prediction capabilities on independent local testing sets achieved 0.80 kappa score. When individual local models predict data from external datasets, average kappa score decreases to 0.54. Using the proposed ensemble-based approach, average kappa performance on the external dataset prediction scenario increases to 0.62. To our knowledge this is the largest study by the number of datasets so far on validating the generalization capabilities of an automatic sleep staging algorithm using external databases. CONCLUSIONS Validation results show good general performance of our method, as compared with the expected levels of human agreement, as well as to state-of-the-art automatic sleep staging methods. The proposed ensemble-based approach enables flexible and scalable design, allowing dynamic integration of local models into the final ensemble, preserving data locality, and increasing generalization capabilities of the resulting system at the same time.
Collapse
Affiliation(s)
- Diego Alvarez-Estevez
- Sleep Center, Haaglanden Medisch Centrum, The Hague, South-Holland, The Netherlands
- Center for Information and Communications Technology Research (CITIC), University of A Coruña, A Coruña, Spain
| | - Roselyne M. Rijsman
- Sleep Center, Haaglanden Medisch Centrum, The Hague, South-Holland, The Netherlands
| |
Collapse
|
7
|
Satapathy S, Loganathan D, Kondaveeti HK, Rath R. Performance analysis of machine learning algorithms on automated sleep staging feature sets. CAAI TRANSACTIONS ON INTELLIGENCE TECHNOLOGY 2021. [DOI: 10.1049/cit2.12042] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022] Open
Affiliation(s)
- Santosh Satapathy
- Puducherry Research Scholar of Computer Science and Engineering Pondicherry Engineering College, Puducherry India
| | - D Loganathan
- Professor of Computer Science and Engineering Pondicherry Engineering College, Puducherry Puducherry India
| | - Hari Kishan Kondaveeti
- Assistant Professor of Computer Science and Engineering VIT University, Amaravati Andhra Pradesh India
| | - RamaKrushna Rath
- Research Scholar of Computer Science and Engineering, Anna University Chennai India
| |
Collapse
|
8
|
Laport F, Dapena A, Castro PM, Vazquez-Araujo FJ, Iglesia D. A Prototype of EEG System for IoT. Int J Neural Syst 2020; 30:2050018. [PMID: 32362151 DOI: 10.1142/s0129065720500185] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/02/2023]
Abstract
In this work, we develop open source hardware and software for eye state classification and integrate it with a protocol for the Internet of Things (IoT). We design and build the hardware using a reduced number of components and with a very low-cost. Moreover, we propose a method for the detection of open eyes (oE) and closed eyes (cE) states based on computing a power ratio between different frequency bands of the acquired signal. We compare several real- and complex-valued transformations combined with two decision strategies: a threshold-based method and a linear discriminant analysis. Simulation results show both classifier accuracies and their corresponding system delays.
Collapse
Affiliation(s)
- Francisco Laport
- Department of Computer Engineering, CITIC Research Center & University of A Coruña, Campus de Elviña, A Coruña 15071, Spain
| | - Adriana Dapena
- Department of Computer Engineering, CITIC Research Center & University of A Coruña, Campus de Elviña, A Coruña 15071, Spain
| | - Paula M Castro
- Department of Computer Engineering, CITIC Research Center & University of A Coruña, Campus de Elviña, A Coruña 15071, Spain
| | - Francisco J Vazquez-Araujo
- Department of Computer Engineering, CITIC Research Center & University of A Coruña, Campus de Elviña, A Coruña 15071, Spain
| | - Daniel Iglesia
- Department of Computer Engineering, CITIC Research Center & University of A Coruña, Campus de Elviña, A Coruña 15071, Spain
| |
Collapse
|
9
|
Alvarez-Estevez D, Fernández-Varela I. Addressing database variability in learning from medical data: An ensemble-based approach using convolutional neural networks and a case of study applied to automatic sleep scoring. Comput Biol Med 2020; 119:103697. [DOI: 10.1016/j.compbiomed.2020.103697] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/09/2020] [Revised: 03/03/2020] [Accepted: 03/04/2020] [Indexed: 10/24/2022]
|
10
|
Faust O, Razaghi H, Barika R, Ciaccio EJ, Acharya UR. A review of automated sleep stage scoring based on physiological signals for the new millennia. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2019; 176:81-91. [PMID: 31200914 DOI: 10.1016/j.cmpb.2019.04.032] [Citation(s) in RCA: 68] [Impact Index Per Article: 13.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/26/2018] [Revised: 04/03/2019] [Accepted: 04/29/2019] [Indexed: 06/09/2023]
Abstract
BACKGROUND AND OBJECTIVE Sleep is an important part of our life. That importance is highlighted by the multitude of health problems which result from sleep disorders. Detecting these sleep disorders requires an accurate interpretation of physiological signals. Prerequisite for this interpretation is an understanding of the way in which sleep stage changes manifest themselves in the signal waveform. With that understanding it is possible to build automated sleep stage scoring systems. Apart from their practical relevance for automating sleep disorder diagnosis, these systems provide a good indication of the amount of sleep stage related information communicated by a specific physiological signal. METHODS This article provides a comprehensive review of automated sleep stage scoring systems, which were created since the year 2000. The systems were developed for Electrocardiogram (ECG), Electroencephalogram (EEG), Electrooculogram (EOG), and a combination of signals. RESULTS Our review shows that all of these signals contain information for sleep stage scoring. CONCLUSIONS The result is important, because it allows us to shift our research focus away from information extraction methods to systemic improvements, such as patient comfort, redundancy, safety and cost.
Collapse
Affiliation(s)
- Oliver Faust
- Department of Engineering and Mathematics, Sheffield Hallam University, United Kingdom.
| | - Hajar Razaghi
- Department of Engineering and Mathematics, Sheffield Hallam University, United Kingdom
| | - Ragab Barika
- Department of Engineering and Mathematics, Sheffield Hallam University, United Kingdom
| | - Edward J Ciaccio
- Department of Medicine - Cardiology, Columbia University, New York, New York, USA
| | - U Rajendra Acharya
- Department of Electronic & Computer Engineering, Ngee Ann Polytechnic, Singapore; Department of Biomedical Engineering, School of Science and Technology, SIM University, Singapore; Department of Biomedical Imaging, Faculty of Medicine, University of Malaya, Kuala Lumpur, Malaysia
| |
Collapse
|
11
|
Pillay K, Dereymaeker A, Jansen K, Naulaers G, Van Huffel S, De Vos M. Automated EEG sleep staging in the term-age baby using a generative modelling approach. J Neural Eng 2018; 15:036004. [DOI: 10.1088/1741-2552/aaab73] [Citation(s) in RCA: 30] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
|
12
|
Stephen JM, Hill DE, Peters A, Flynn L, Zhang T, Okada Y. Development of Auditory Evoked Responses in Normally Developing Preschool Children and Children with Autism Spectrum Disorder. Dev Neurosci 2017; 39:430-441. [PMID: 28772264 PMCID: PMC6724532 DOI: 10.1159/000477614] [Citation(s) in RCA: 26] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2015] [Accepted: 05/18/2017] [Indexed: 11/19/2022] Open
Abstract
The cortical responses to auditory stimuli undergo rapid and dramatic changes during the first 3 years of life in normally developing (ND) children, with decreases in latency and changes in amplitude in the primary peaks. However, most previous studies have focused on children >3 years of age. The analysis of data from the early stages of development is challenging because the temporal pattern of the evoked responses changes with age (e.g., additional peaks emerge with increasing age) and peak latency decreases with age. This study used the topography of the auditory evoked magnetic field (AEF) to identify the auditory components in ND children between 6 and 68 months (n = 48). The latencies of the peaks in the AEF produced by a tone burst (ISI 2 ± 0.2 s) during sleep decreased with age, consistent with previous reports in awake children. The peak latencies of the AEFs in ND children and children with autism spectrum disorder (ASD) were compared. Previous studies indicate that the latencies of the initial components of the auditory evoked potential (AEP) and the AEF are delayed in children with ASD when compared to age-matched ND children >4 years of age. We speculated whether the AEF latencies decrease with age in children diagnosed with ASD as in ND children, but with uniformly longer latencies before the age of about 4 years. Contrary to this hypothesis, the peak latencies did not decrease with age in the ASD group (24-62 months, n = 16) during sleep (unlike in the age-matched controls), although the mean latencies were longer in the ASD group as in previous studies. These results are consistent with previous studies indicating delays in auditory latencies, and they indicate a different maturational pattern in ASD children and ND children. Longitudinal studies are needed to confirm whether the AEF latencies diverge with age, starting at around 3 years, in these 2 groups of children.
Collapse
Affiliation(s)
- Julia M. Stephen
- The Mind Research Network, 1101 Yale Blvd NE, Albuquerque, NM 87106 USA
| | - Dina E. Hill
- Department of Psychiatry, University of New Mexico Health Sciences Center, Albuquerque, NM USA 87131-001
| | - Amanda Peters
- The Mind Research Network, 1101 Yale Blvd NE, Albuquerque, NM 87106 USA
| | - Lucinda Flynn
- The Mind Research Network, 1101 Yale Blvd NE, Albuquerque, NM 87106 USA
| | - Tongsheng Zhang
- Department of Neurology, University of New Mexico Health Sciences Center, Albuquerque, NM USA 87131-001
| | - Yoshio Okada
- Division of Newborn Medicine, Department of Medicine, Children’s Hospital Boston/Harvard Medical School, Boston, MA 02115
| |
Collapse
|
13
|
|
14
|
Khuwaja GA, Haghighi SJ, Hatzinakos D. 40-Hz ASSR fusion classification system for observing sleep patterns. EURASIP JOURNAL ON BIOINFORMATICS & SYSTEMS BIOLOGY 2015; 2015:2. [PMID: 28194171 PMCID: PMC5270494 DOI: 10.1186/s13637-014-0021-2] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/28/2014] [Accepted: 12/29/2014] [Indexed: 11/15/2022]
Abstract
This paper presents a fusion-based neural network (NN) classification algorithm for 40-Hz auditory steady state response (ASSR) ensemble averaged signals which were recorded from eight human subjects for observing sleep patterns (wakefulness W0 and deep sleep N3 or slow wave sleep SWS). In SWS, sensitivity to pain is the lowest relative to other sleep stages and arousal needs stronger stimuli. 40-Hz ASSR signals were extracted by averaging over 900 sweeps on a 30-s window. Signals generated during N3 deep sleep state show similarities to those produced when general anesthesia is given to patients during clinical surgery. Our experimental results show that the automatic classification system used identifies sleep states with an accuracy rate of 100% when the training and test signals come from the same subjects while its accuracy is reduced to 97.6%, on average, when signals are used from different training and test subjects. Our results may lead to future classification of consciousness and wakefulness of patients with 40-Hz ASSR for observing the depth and effects of general anesthesia (DGA).
Collapse
Affiliation(s)
- Gulzar A Khuwaja
- Department of Electrical and Computer Engineering, University of Toronto, 40 St. George Street, Toronto, ON M5S 2E4 Canada
| | - Sahar Javaher Haghighi
- Department of Electrical and Computer Engineering, University of Toronto, 40 St. George Street, Toronto, ON M5S 2E4 Canada
| | - Dimitrios Hatzinakos
- Department of Electrical and Computer Engineering, University of Toronto, 40 St. George Street, Toronto, ON M5S 2E4 Canada
| |
Collapse
|
15
|
Kabir MM, Tafreshi R, Boivin DB, Haddad N. Enhanced automated sleep spindle detection algorithm based on synchrosqueezing. Med Biol Eng Comput 2015; 53:635-44. [DOI: 10.1007/s11517-015-1265-z] [Citation(s) in RCA: 16] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/13/2014] [Accepted: 02/27/2015] [Indexed: 11/30/2022]
|
16
|
Long X, Foussier J, Fonseca P, Haakma R, Aarts RM. Analyzing respiratory effort amplitude for automated sleep stage classification. Biomed Signal Process Control 2014. [DOI: 10.1016/j.bspc.2014.08.001] [Citation(s) in RCA: 26] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/24/2022]
|
17
|
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]
|
18
|
A method for the automatic analysis of the sleep macrostructure in continuum. EXPERT SYSTEMS WITH APPLICATIONS 2013. [DOI: 10.1016/j.eswa.2012.09.022] [Citation(s) in RCA: 19] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
|
19
|
Nonclercq A, Urbain C, Verheulpen D, Decaestecker C, Van Bogaert P, Peigneux P. Sleep spindle detection through amplitude–frequency normal modelling. J Neurosci Methods 2013; 214:192-203. [DOI: 10.1016/j.jneumeth.2013.01.015] [Citation(s) in RCA: 23] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/27/2012] [Revised: 01/17/2013] [Accepted: 01/18/2013] [Indexed: 10/27/2022]
|
20
|
Brignol A, Al-Ani T, Drouot X. Phase space and power spectral approaches for EEG-based automatic sleep-wake classification in humans: a comparative study using short and standard epoch lengths. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2013; 109:227-238. [PMID: 23164523 DOI: 10.1016/j.cmpb.2012.10.002] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/19/2012] [Revised: 09/24/2012] [Accepted: 10/01/2012] [Indexed: 06/01/2023]
Abstract
Sleep disorders in humans have become a public health issue in recent years. Sleep can be analysed by studying the electroencephalogram (EEG) recorded during a night's sleep. Alternating between sleep-wake stages gives information related to the sleep quality and quantity since this alternating pattern is highly affected during sleep disorders. Spectral composition of EEG signals varies according to sleep stages, alternating phases of high energy associated to low frequency (deep sleep) with periods of low energy associated to high frequency (wake and light sleep). The analysis of sleep in humans is usually made on periods (epochs) of 30-s length according to the original Rechtschaffen and Kales sleep scoring manual. In this work, we propose a new phase space-based (mainly based on Poincaré plot) algorithm for automatic classification of sleep-wake states in humans using EEG data gathered over relatively short-time periods. The effectiveness of our approach is demonstrated through a series of experiments involving EEG data from seven healthy adult female subjects and was tested on epoch lengths ranging from 3-s to 30-s. The performance of our phase space approach was compared to a 2-dimensional state space approach using the power spectral (PS) in two selected human-specific frequency bands. These powers were calculated by dividing integrated spectral amplitudes at selected human-specific frequency bands. The comparison demonstrated that the phase space approach gives better performance in the case of short as well as standard 30-s epoch lengths.
Collapse
Affiliation(s)
- Arnaud Brignol
- Département Informatique, ESIEE-Paris, Cité Descartes-BP 99, 93162 Noisy-Le-Grand, France
| | | | | |
Collapse
|
21
|
Terrill PI, Wilson SJ, Suresh S, Cooper DM, Dakin C. Application of recurrence quantification analysis to automatically estimate infant sleep states using a single channel of respiratory data. Med Biol Eng Comput 2012; 50:851-65. [PMID: 22614135 DOI: 10.1007/s11517-012-0918-4] [Citation(s) in RCA: 20] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2011] [Accepted: 04/23/2012] [Indexed: 11/28/2022]
Abstract
Previous work has identified that non-linear variables calculated from respiratory data vary between sleep states, and that variables derived from the non-linear analytical tool recurrence quantification analysis (RQA) are accurate infant sleep state discriminators. This study aims to apply these discriminators to automatically classify 30 s epochs of infant sleep as REM, non-REM and wake. Polysomnograms were obtained from 25 healthy infants at 2 weeks, 3, 6 and 12 months of age, and manually sleep staged as wake, REM and non-REM. Inter-breath interval data were extracted from the respiratory inductive plethysmograph, and RQA applied to calculate radius, determinism and laminarity. Time-series statistic and spectral analysis variables were also calculated. A nested cross-validation method was used to identify the optimal feature subset, and to train and evaluate a linear discriminant analysis-based classifier. The RQA features radius and laminarity and were reliably selected. Mean agreement was 79.7, 84.9, 84.0 and 79.2 % at 2 weeks, 3, 6 and 12 months, and the classifier performed better than a comparison classifier not including RQA variables. The performance of this sleep-staging tool compares favourably with inter-human agreement rates, and improves upon previous systems using only respiratory data. Applications include diagnostic screening and population-based sleep research.
Collapse
Affiliation(s)
- Philip I Terrill
- School of Information Technology and Electrical Engineering, The University of Queensland, St. Lucia, QLD, Australia.
| | | | | | | | | |
Collapse
|
22
|
Held CM, Causa J, Causa L, Estévez PA, Perez CA, Garrido M, Chamorro R, Algarin C, Peirano P. Automated detection of rapid eye movements in children. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2012; 2012:2267-2270. [PMID: 23366375 DOI: 10.1109/embc.2012.6346414] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/01/2023]
Abstract
We present an automated multiple-step tool to identify Rapid Eye Movements (REMs) in the polysomnogram, based on modeling expert criteria. It begins by identifying the polysomnogram segments compatible with REMs presence. On these segments, high-energy REMs are identified. Then, vicinity zones around those REMs are defined, and lesser-energy REMs are sought in these vicinities. This strategy has the advantage that it can detect lesser-energy REMs without increasing much the false positive detections. Signal processing, feature extraction, and fuzzy logic tools are used to achieve the goal. The tool was trained and validated on a database consisting of 20 all-night polysomnogram recordings (160 hr) of healthy ten-year-old children. Preliminary results on the validation set show 85.5% sensitivity and a false positive rate of 16.2%. Our tool works on complete polysomnogram recordings, without the need of preprocessing, prior knowledge of the hypnogram, or noise-free segments selection.
Collapse
Affiliation(s)
- Claudio M Held
- Department of Electrical Engineering, Universidad de Chile, Santiago, Chile.
| | | | | | | | | | | | | | | | | |
Collapse
|
23
|
Krakovská A, Mezeiová K. Automatic sleep scoring: A search for an optimal combination of measures. Artif Intell Med 2011; 53:25-33. [DOI: 10.1016/j.artmed.2011.06.004] [Citation(s) in RCA: 87] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2009] [Revised: 05/13/2011] [Accepted: 06/16/2011] [Indexed: 11/30/2022]
|
24
|
Causa L, Held CM, Causa J, Estévez PA, Perez CA, Chamorro R, Garrido M, Algarín C, Peirano P. Automated Sleep-Spindle Detection in Healthy Children Polysomnograms. IEEE Trans Biomed Eng 2010; 57:2135-46. [DOI: 10.1109/tbme.2010.2052924] [Citation(s) in RCA: 42] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
|
25
|
Šušmáková K, Krakovská A. Discrimination ability of individual measures used in sleep stages classification. Artif Intell Med 2008; 44:261-77. [DOI: 10.1016/j.artmed.2008.07.005] [Citation(s) in RCA: 61] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2008] [Revised: 06/30/2008] [Accepted: 07/08/2008] [Indexed: 10/21/2022]
|
26
|
Lewicke A, Sazonov E, Corwin MJ, Neuman M, Schuckers S. Sleep versus wake classification from heart rate variability using computational intelligence: consideration of rejection in classification models. IEEE Trans Biomed Eng 2008; 55:108-18. [PMID: 18232352 DOI: 10.1109/tbme.2007.900558] [Citation(s) in RCA: 46] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Abstract
Reliability of classification performance is important for many biomedical applications. A classification model which considers reliability in the development of the model such that unreliable segments are rejected would be useful, particularly, in large biomedical data sets. This approach is demonstrated in the development of a technique to reliably determine sleep and wake using only the electrocardiogram (ECG) of infants. Typically, sleep state scoring is a time consuming task in which sleep states are manually derived from many physiological signals. The method was tested with simultaneous 8-h ECG and polysomnogram (PSG) determined sleep scores from 190 infants enrolled in the collaborative home infant monitoring evaluation (CHIME) study. Learning vector quantization (LVQ) neural network, multilayer perceptron (MLP) neural network, and support vector machines (SVMs) are tested as the classifiers. After systematic rejection of difficult to classify segments, the models can achieve 85%-87% correct classification while rejecting only 30% of the data. This corresponds to a Kappa statistic of 0.65-0.68. With rejection, accuracy improves by about 8% over a model without rejection. Additionally, the impact of the PSG scored indeterminate state epochs is analyzed. The advantages of a reliable sleep/wake classifier based only on ECG include high accuracy, simplicity of use, and low intrusiveness. Reliability of the classification can be built directly in the model, such that unreliable segments are rejected.
Collapse
Affiliation(s)
- Aaron Lewicke
- Department of Electrical and Computer Engineering, Clarkson University, Potsdam, NY 13699, USA.
| | | | | | | | | | | |
Collapse
|
27
|
Held CM, Causa L, Estévez P, Pérez C, Garrido M, Algarín C, Peirano P. Dual approach for automated sleep spindles detection within EEG background activity in infant polysomnograms. CONFERENCE PROCEEDINGS : ... ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL CONFERENCE 2007; 2006:566-9. [PMID: 17271739 DOI: 10.1109/iembs.2004.1403220] [Citation(s) in RCA: 10] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Abstract
An automated system for sleep spindles detection within EEG background activity, combining two different approaches, is presented. The first approach applies detection criteria on the sigma-band filtered EEG signal, including fuzzy thresholds. The second approach mimics an expert's procedure. A sleep spindle detection is validated if both approaches agree. The method was applied on a testing set, consisting of continuous sleep recordings of two patients, totaling 1132 epochs (pages). A total of 803 sleep spindles events were marked by the experts. Results showed an 87.7% agreement between the detection system and the medical experts.
Collapse
|
28
|
Held CM, Heiss JE, Estévez PA, Perez CA, Garrido M, Algarín C, Peirano P. Extracting Fuzzy Rules From Polysomnographic Recordings for Infant Sleep Classification. IEEE Trans Biomed Eng 2006; 53:1954-62. [PMID: 17019859 DOI: 10.1109/tbme.2006.881798] [Citation(s) in RCA: 45] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Abstract
A neuro-fuzzy classifier (NFC) of sleep-wake states and stages has been developed for healthy infants of ages 6 mo and onward. The NFC takes five input patterns previously identified on 20-s epochs from polysomnographic recordings and assigns them to one out of five possible classes: Wakefulness, REM-Sleep, Non-REM Sleep Stage 1, Stage 2, and Stage 3-4. The definite criterion for a sleep state or stage to be established is duration of at least 1 min. The data set consisted of a total of 14 continuous recordings of naturally occurring naps (average duration: 143 +/- 39 min), corresponding to a total of 6021 epochs. They were divided in a training, a validation and a test set with 7, 2, and 5 recordings, respectively. During supervised training, the system determined the fuzzy concepts associated to the inputs and the rules required for performing the classification, extracting knowledge from the training set, and pruning nonrelevant rules. Results on an independent test set achieved 83.9 +/- 0.4% of expert agreement. The fuzzy rules obtained from the training examples without a priori information showed a high level of coincidence with the crisp rules stated by the experts, which are based on internationally accepted criteria. These results show that the NFC can be a valuable tool for implementing an automated sleep-wake classification system.
Collapse
Affiliation(s)
- Claudio M Held
- Department of Electrical Engineering, Universidad de Chile, Casilla 412-3, Santiago, Chile.
| | | | | | | | | | | | | |
Collapse
|
29
|
Abstract
A high-quality distance preserving output representation is provided to the neural gas (NG) network. The nonlinear mapping is determined concurrently along with the codebook vectors. The adaptation rule for codebook positions in the projection space minimizes a cost function that favors the trustworthy preservation of the local topology. The proposed visualization method, called OVI-NG, is an enhancement over curvilinear component analysis (CCA). The results show that the mapping quality obtained with OVI-NG outperforms the original CCA, in terms of the trustworthiness, continuity, topographic function and topology preservation measures.
Collapse
Affiliation(s)
- Pablo A Estévez
- Department of Electrical Engineering, University of Chile, Casilla 412-3, Santiago, Chile
| | | |
Collapse
|
30
|
Estévez PA, Figueroa CJ, Saito K. Cross-entropy embedding of high-dimensional data using the neural gas model. Neural Netw 2005; 18:727-37. [PMID: 16087314 DOI: 10.1016/j.neunet.2005.06.010] [Citation(s) in RCA: 11] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
Abstract
A cross-entropy approach to mapping high-dimensional data into a low-dimensional space embedding is presented. The method allows to project simultaneously the input data and the codebook vectors, obtained with the Neural Gas (NG) quantizer algorithm, into a low-dimensional output space. The aim of this approach is to preserve the relationship defined by the NG neighborhood function for each pair of input and codebook vectors. A cost function based on the cross-entropy between input and output probabilities is minimized by using a Newton-Raphson method. The new approach is compared with Sammon's non-linear mapping (NLM) and the hierarchical approach of combining a vector quantizer such as the self-organizing feature map (SOM) or NG with the NLM recall algorithm. In comparison with these techniques, our method delivers a clear visualization of both data points and codebooks, and it achieves a better mapping quality in terms of the topology preservation measure q(m).
Collapse
Affiliation(s)
- Pablo A Estévez
- Department of Electrical Engineering, University of Chile, Casilla 412-3, Santiago, Chile.
| | | | | |
Collapse
|
31
|
Heiss JE, Held CM, Estévez PA, Perez CA, Holzmann CA, Pérez JP. Classification of sleep stages in infants: a neuro fuzzy approach. IEEE ENGINEERING IN MEDICINE AND BIOLOGY MAGAZINE : THE QUARTERLY MAGAZINE OF THE ENGINEERING IN MEDICINE & BIOLOGY SOCIETY 2002; 21:147-51. [PMID: 12405069 DOI: 10.1109/memb.2002.1044185] [Citation(s) in RCA: 30] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Affiliation(s)
- J E Heiss
- Department of Electrical Engineering, Universidad de Chile.
| | | | | | | | | | | |
Collapse
|