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Luo X, Zhou B, Shi J, Li G, Zhu Y. Effects of gender and age on sleep EEG functional connectivity differences in subjects with mild difficulty falling asleep. Front Psychiatry 2024; 15:1433316. [PMID: 39045546 PMCID: PMC11264056 DOI: 10.3389/fpsyt.2024.1433316] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/15/2024] [Accepted: 06/17/2024] [Indexed: 07/25/2024] Open
Abstract
Introduction Difficulty falling asleep place an increasing burden on society. EEG-based sleep staging is fundamental to the diagnosis of sleep disorder, and the selection of features for each sleep stage is a key step in the sleep analysis. However, the differences of sleep EEG features in gender and age are not clear enough. Methods This study aimed to investigate the effects of age and gender on sleep EEG functional connectivity through statistical analysis of brain functional connectivity and machine learning validation. The two-overnight sleep EEG data of 78 subjects with mild difficulty falling asleep were categorized into five sleep stages using markers and segments from the "sleep-EDF" public database. First, the 78 subjects were finely grouped, and the mutual information of the six sleep EEG rhythms of δ, θ, α, β, spindle, and sawtooth wave was extracted as a functional connectivity measure. Then, one-way analysis of variance (ANOVA) was used to extract significant differences in functional connectivity of sleep rhythm waves across sleep stages with respect to age and gender. Finally, machine learning algorithms were used to investigate the effects of fine grouping of age and gender on sleep staging. Results and discussion The results showed that: (1) The functional connectivity of each sleep rhythm wave differed significantly across sleep stages, with delta and beta functional connectivity differing significantly across sleep stages. (2) Significant differences in functional connections among young and middle-aged groups, and among young and elderly groups, but no significant difference between middle-aged and elderly groups. (3) Female functional connectivity strength is generally higher than male at the high-frequency band of EEG, but no significant difference in the low-frequency. (4) Finer group divisions based on gender and age can indeed improve the accuracy of sleep staging, with an increase of about 3.58% by using the random forest algorithm. Our results further reveal the electrophysiological neural mechanisms of each sleep stage, and find that sleep functional connectivity differs significantly in both gender and age, providing valuable theoretical guidance for the establishment of automated sleep stage models.
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Affiliation(s)
- Xiaodong Luo
- Psychiatry Department, The Second Hospital of Jinhua, Jinhua, China
| | - Bin Zhou
- College of Mathematical Medicine, Zhejiang Normal University, Jinhua, China
| | - Jilong Shi
- College of Engineering, Zhejiang Normal University, Jinhua, China
| | - Gang Li
- College of Mathematical Medicine, Zhejiang Normal University, Jinhua, China
| | - Yixia Zhu
- Psychiatry Department, The Second Hospital of Jinhua, Jinhua, China
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Moebus M, Holz C. Personalized interpretable prediction of perceived sleep quality: Models with meaningful cardiovascular and behavioral features. PLoS One 2024; 19:e0305258. [PMID: 38976698 PMCID: PMC11230538 DOI: 10.1371/journal.pone.0305258] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2023] [Accepted: 05/28/2024] [Indexed: 07/10/2024] Open
Abstract
Understanding a person's perceived quality of sleep is an important problem, but hard due to its poor definition and high intra- as well as inter-individual variation. In the short term, sleep quality has an established impact on cognitive function during the following day as well as on fatigue. In the long term, good quality sleep is essential for mental and physical health and contributes to quality of life. Despite the need to better understand sleep quality as an early indicator for sleep disorders, perceived sleep quality has been rarely modeled for multiple consecutive days using biosignals. In this paper, we present novel insights on the association of cardiac activity and perceived sleep quality using an interpretable modeling approach utilizing the publicly available intensive-longitudinal study M2Sleep. Our method takes as input signals from commodity wearable devices, including motion and blood volume pulses. Despite processing only simple and clearly interpretable features, we achieve an accuracy of up to 70% with an AUC of 0.76 and reduce the error by up to 36% compared to related work. We further argue that collected biosignals and sleep quality labels should be normalized per-participant to enable a medically insightful analysis. Coupled with explainable models, this allows for the interpretations of effects on perceived sleep quality. Analysis revealed that besides higher skin temperature and sufficient sleep duration, especially higher average heart rate while awake and lower minimal activity of the parasympathetic and sympathetic nervous system while asleep increased the chances of higher sleep quality.
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Affiliation(s)
- Max Moebus
- Department of Computer Science, ETH Zurich, Zürich, Switzerland
| | - Christian Holz
- Department of Computer Science, ETH Zurich, Zürich, Switzerland
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Gamble MC, Williams BR, McKenna JT, Logan RW. SleepInvestigatoR: A flexible R function for analyzing scored sleep in rodents. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.04.12.588853. [PMID: 38659801 PMCID: PMC11042239 DOI: 10.1101/2024.04.12.588853] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/26/2024]
Abstract
Analyzing scored sleep is a fundamental prerequisite to understanding how sleep changes between health and disease. Classically, this is accomplished by manually calculating various measures (e.g., percent of non-rapid eye movement sleep) from a collection of scored sleep files. This process can be tedious and error prone especially when studies include a large number of animals or involve long recording sessions. To address this issue, we present SleepInvestigatoR, a versatile tool that can quickly organize and analyze multiple scored sleep files into a single output. The function is written in the open-source statistical language R and has a total of 25 parameters that can be set to match a wide variety of experimenter needs. SleepInvestigatoR delivers a total of 22 unique measures of sleep, including all measures commonly reported in the rodent literature. A simple plotting function is also provided to quickly graph and visualize the scored data. All code is designed to be implemented with little formal coding knowledge and step-by-step instructions are provided on the corresponding GitHub page. Overall, SleepInvestigatoR provides the sleep researcher a critical tool to increase efficiency, interpretation, and reproducibility in analyzing scored rodent sleep.
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Shen Y, Huai B, Wang X, Chen M, Shen X, Han M, Su F, Xin T. Automatic sleep-wake classification and Parkinson's disease recognition using multifeature fusion with support vector machine. CNS Neurosci Ther 2024; 30:e14708. [PMID: 38600857 PMCID: PMC11007385 DOI: 10.1111/cns.14708] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/07/2023] [Revised: 01/29/2024] [Accepted: 02/12/2024] [Indexed: 04/12/2024] Open
Abstract
AIMS Sleep disturbance is a prevalent nonmotor symptom of Parkinson's disease (PD), however, assessing sleep conditions is always time-consuming and labor-intensive. In this study, we performed an automatic sleep-wake state classification and early diagnosis of PD by analyzing the electrocorticography (ECoG) and electromyogram (EMG) signals of both normal and PD rats. METHODS The study utilized ECoG power, EMG amplitude, and corticomuscular coherence values extracted from normal and PD rats to construct sleep-wake scoring models based on the support vector machine algorithm. Subsequently, we incorporated feature values that could act as diagnostic markers for PD and then retrained the models, which could encompass the identification of vigilance states and the diagnosis of PD. RESULTS Features extracted from occipital ECoG signals were more suitable for constructing sleep-wake scoring models than those from frontal ECoG (average Cohen's kappa: 0.73 vs. 0.71). Additionally, after retraining, the new models demonstrated increased sensitivity to PD and accurately determined the sleep-wake states of rats (average Cohen's kappa: 0.79). CONCLUSION This study accomplished the precise detection of substantia nigra lesions and the monitoring of sleep-wake states. The integration of circadian rhythm monitoring and disease state assessment has the potential to improve the efficacy of therapeutic strategies considerably.
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Affiliation(s)
- Yin Shen
- Department of NeurosurgeryThe First Affiliated Hospital of Shandong First Medical University & Shandong Provincial Qianfoshan HospitalJinanShandongP. R. China
- Medical Science and Technology Innovation CenterShandong First Medical University and Shandong Academy of Medical SciencesJinanShandongP. R. China
| | - Baogeng Huai
- First Clinical Medical College, Shandong University of Traditional Chinese MedicineJinanP. R. China
| | - Xiaofeng Wang
- Department of NeurosurgeryThe First Affiliated Hospital of Shandong First Medical University & Shandong Provincial Qianfoshan HospitalJinanShandongP. R. China
- Medical Science and Technology Innovation CenterShandong First Medical University and Shandong Academy of Medical SciencesJinanShandongP. R. China
| | - Min Chen
- Department of NeurosurgeryThe First Affiliated Hospital of Shandong First Medical University & Shandong Provincial Qianfoshan HospitalJinanShandongP. R. China
- Department of RadiologyShandong First Medical University & Shandong Academy of Medical SciencesTaianP. R. China
| | - Xiaoyue Shen
- First Clinical Medical College, Shandong University of Traditional Chinese MedicineJinanP. R. China
| | - Min Han
- Department of NeurosurgeryThe First Affiliated Hospital of Shandong First Medical University & Shandong Provincial Qianfoshan HospitalJinanShandongP. R. China
- Medical Science and Technology Innovation CenterShandong First Medical University and Shandong Academy of Medical SciencesJinanShandongP. R. China
| | - Fei Su
- Department of NeurosurgeryThe First Affiliated Hospital of Shandong First Medical University & Shandong Provincial Qianfoshan HospitalJinanShandongP. R. China
- Department of RadiologyShandong First Medical University & Shandong Academy of Medical SciencesTaianP. R. China
| | - Tao Xin
- Department of NeurosurgeryThe First Affiliated Hospital of Shandong First Medical University & Shandong Provincial Qianfoshan HospitalJinanShandongP. R. China
- Medical Science and Technology Innovation CenterShandong First Medical University and Shandong Academy of Medical SciencesJinanShandongP. R. China
- Institute of Brain Science and Brain‐inspired Research, Shandong First Medical University & Shandong Academy of Medical SciencesJinanShandongP. R. China
- Shandong Institute of Brain Science and Brain‐inspired ResearchJinanShandongP. R. China
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Wei Y, Zhu Y, Zhou Y, Yu X, Luo Y. Automatic Sleep Staging Based on Contextual Scalograms and Attention Convolution Neural Network Using Single-Channel EEG. IEEE J Biomed Health Inform 2024; 28:801-811. [PMID: 37955995 DOI: 10.1109/jbhi.2023.3332503] [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: 11/15/2023]
Abstract
Single-channel EEG based sleep staging is of interest to researchers due to its broad application prospect in daily sleep monitoring recently. We proposed using contextual scalograms as input and developed a convolutional neural network with attention modules named Co-ScaleNet for sleep staging. The contextual scalograms were obtained by combining the same color channels of three original RGB scalograms from consecutive epochs, and a simple and efficient data augmentation was designed according to their various forms. The Co-ScaleNet consists of two main parts. Firstly, three parallel convolutional branches with attention modules correspondingly extract and fuse features from contextual scalograms at the top layers. The remaining part is a stack of lightweight blocks. We achieved an overall accuracy of 87.0% for healthy individuals, 84.7% for depressed patients. And we obtained comparable performance on the public Sleep-EDFx (82.8%), ISRUC (84.6%) and SHHS datasets (87.7%), including a high recall of N1. The contextual scalograms of R channel as input achieved the best performance, which conform to the features of interest in visual scoring. The attention modules improved the recall of N1 and N3. Overall, the contextual scalograms provided a novel scheme for both contextual information extraction and data augmentation. Our study successfully expanded its application to depression datasets, as well as patients with sleep apnea, demonstrating its wide applicability.
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Lee J, Kim HC, Lee YJ, Lee S. Development of generalizable automatic sleep staging using heart rate and movement based on large databases. Biomed Eng Lett 2023; 13:649-658. [PMID: 37872992 PMCID: PMC10590335 DOI: 10.1007/s13534-023-00288-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2023] [Revised: 05/12/2023] [Accepted: 05/14/2023] [Indexed: 10/25/2023] Open
Abstract
Purpose With the advancement of deep neural networks in biosignals processing, the performance of automatic sleep staging algorithms has improved significantly. However, sleep staging using only non-electroencephalogram features has not been as successful, especially following the current American Association of Sleep Medicine (AASM) standards. This study presents a fine-tuning based approach to widely generalizable automatic sleep staging using heart rate and movement features trained and validated on large databases of polysomnography. Methods A deep neural network is used to predict sleep stages using heart rate and movement features. The model is optimized on a dataset of 8731 nights of polysomnography recordings labeled using the Rechtschaffen & Kales scoring system, and fine-tuned to a smaller dataset of 1641 AASM-labeled recordings. The model prior to and after fine-tuning is validated on two AASM-labeled external datasets totaling 1183 recordings. In order to measure the performance of the model, the output of the optimized model is compared to reference expert-labeled sleep stages using accuracy and Cohen's κ as key metrics. Results The fine-tuned model showed accuracy of 76.6% with Cohen's κ of 0.606 in one of the external validation datasets, outperforming a previously reported result, and showed accuracy of 81.0% with Cohen's κ of 0.673 in another external validation dataset. Conclusion These results indicate that the proposed model is generalizable and effective in predicting sleep stages using features which can be extracted from non-contact sleep monitors. This holds valuable implications for future development of home sleep evaluation systems.
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Affiliation(s)
| | - Hee Chan Kim
- Department of Biomedical Engineering, Seoul National University College of Medicine, Seoul, 03080 South Korea
- Institute of Medical and Biological Engineering, Medical Research Center, Seoul National University, Seoul, 08826 South Korea
| | - Yu Jin Lee
- Department of Neuropsychiatry, Seoul National University Hospital, Seoul, 03080 South Korea
- Center for Sleep and Chronobiology, Seoul National University Hospital, Seoul, 03080 South Korea
| | - Saram Lee
- Transdisciplinary Department of Medicine and Advanced Technology, Seoul National University Hospital, Seoul, 03080 South Korea
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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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Liu G, Wei G, Sun S, Mao D, Zhang J, Zhao D, Tian X, Wang X, Chen N. Micro SleepNet: efficient deep learning model for mobile terminal real-time sleep staging. Front Neurosci 2023; 17:1218072. [PMID: 37575302 PMCID: PMC10416229 DOI: 10.3389/fnins.2023.1218072] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2023] [Accepted: 07/07/2023] [Indexed: 08/15/2023] Open
Abstract
The real-time sleep staging algorithm that can perform inference on mobile devices without burden is a prerequisite for closed-loop sleep modulation. However, current deep learning sleep staging models have poor real-time efficiency and redundant parameters. We propose a lightweight and high-performance sleep staging model named Micro SleepNet, which takes a 30-s electroencephalography (EEG) epoch as input, without relying on contextual signals. The model features a one-dimensional group convolution with a kernel size of 1 × 3 and an Efficient Channel and Spatial Attention (ECSA) module for feature extraction and adaptive recalibration. Moreover, the model efficiently performs feature fusion using dilated convolution module and replaces the conventional fully connected layer with Global Average Pooling (GAP). These design choices significantly reduce the total number of model parameters to 48,226, with only approximately 48.95 Million Floating-point Operations per Second (MFLOPs) computation. The proposed model is conducted subject-independent cross-validation on three publicly available datasets, achieving an overall accuracy of up to 83.3%, and the Cohen Kappa is 0.77. Additionally, we introduce Class Activation Mapping (CAM) to visualize the model's attention to EEG waveforms, which demonstrate the model's ability to accurately capture feature waveforms of EEG at different sleep stages. This provides a strong interpretability foundation for practical applications. Furthermore, the Micro SleepNet model occupies approximately 100 KB of memory on the Android smartphone and takes only 2.8 ms to infer one EEG epoch, meeting the real-time requirements of sleep staging tasks on mobile devices. Consequently, our proposed model has the potential to serve as a foundation for accurate closed-loop sleep modulation.
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Affiliation(s)
- Guisong Liu
- Department of Biomedical Engineering, Bioengineering College, Chongqing University, Chongqing, China
| | - Guoliang Wei
- Department of Biomedical Engineering, Bioengineering College, Chongqing University, Chongqing, China
| | - Shuqing Sun
- Department of Biomedical Engineering, Bioengineering College, Chongqing University, Chongqing, China
| | - Dandan Mao
- Department of Sleep and Psychology, Institute of Surgery Research, Daping Hospital, Third Military Medical University (Army Medical University), Chongqing, China
| | - Jiansong Zhang
- School of Medicine, Huaqiao University, Quanzhou, Fujian, China
| | - Dechun Zhao
- College of Bioinformatics, Chongqing University of Posts and Telecommunications, Chongqing, China
| | - Xuelong Tian
- Department of Biomedical Engineering, Bioengineering College, Chongqing University, Chongqing, China
| | - Xing Wang
- Department of Biomedical Engineering, Bioengineering College, Chongqing University, Chongqing, China
| | - Nanxi Chen
- Department of Biomedical Engineering, Bioengineering College, Chongqing University, Chongqing, China
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Metzner C, Schilling A, Traxdorf M, Schulze H, Tziridis K, Krauss P. Extracting continuous sleep depth from EEG data without machine learning. Neurobiol Sleep Circadian Rhythms 2023; 14:100097. [PMID: 37275555 PMCID: PMC10238579 DOI: 10.1016/j.nbscr.2023.100097] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2023] [Revised: 04/28/2023] [Accepted: 05/02/2023] [Indexed: 06/07/2023] Open
Abstract
The human sleep-cycle has been divided into discrete sleep stages that can be recognized in electroencephalographic (EEG) and other bio-signals by trained specialists or machine learning systems. It is however unclear whether these human-defined stages can be re-discovered with unsupervised methods of data analysis, using only a minimal amount of generic pre-processing. Based on EEG data, recorded overnight from sleeping human subjects, we investigate the degree of clustering of the sleep stages using the General Discrimination Value as a quantitative measure of class separability. Virtually no clustering is found in the raw data, even after transforming the EEG signals of each 30-s epoch from the time domain into the more informative frequency domain. However, a Principal Component Analysis (PCA) of these epoch-wise frequency spectra reveals that the sleep stages separate significantly better in the low-dimensional sub-space of certain PCA components. In particular the component C1(t) can serve as a robust, continuous 'master variable' that encodes the depth of sleep and therefore correlates strongly with the 'hypnogram', a common plot of the discrete sleep stages over time. Moreover, C1(t) shows persistent trends during extended time periods where the sleep stage is constant, suggesting that sleep may be better understood as a continuum. These intriguing properties of C1(t) are not only relevant for understanding brain dynamics during sleep, but might also be exploited in low-cost single-channel sleep tracking devices for private and clinical use.
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Affiliation(s)
- Claus Metzner
- Neuroscience Lab, Experimental Otolaryngology, University Hospital, Erlangen, Germany
| | - Achim Schilling
- Neuroscience Lab, Experimental Otolaryngology, University Hospital, Erlangen, Germany
- Cognitive Computational Neuroscience Group, Friedrich-Alexander-Universität Erlangen, Nürnberg, Germany
| | - Maximilian Traxdorf
- Department of Otorhinolaryngology, Head and Neck Surgery, Paracelsus Medical University, Nürnberg, Germany
| | - Holger Schulze
- Neuroscience Lab, Experimental Otolaryngology, University Hospital, Erlangen, Germany
| | - Konstantin Tziridis
- Neuroscience Lab, Experimental Otolaryngology, University Hospital, Erlangen, Germany
| | - Patrick Krauss
- Neuroscience Lab, Experimental Otolaryngology, University Hospital, Erlangen, Germany
- Cognitive Computational Neuroscience Group, Friedrich-Alexander-Universität Erlangen, Nürnberg, Germany
- Pattern Recognition Lab, Friedrich-Alexander-Universität Erlangen, Nürnberg, Germany
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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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Zhang S, Liu A, Zhou Z, Huang Z, Cheng J, Chen D, Zhong Q, Yu Q, Peng Z, Hong M. Clinical features and power spectral entropy of electroencephalography in Wilson's disease with dystonia. Brain Behav 2022; 12:e2791. [PMID: 36282481 PMCID: PMC9759124 DOI: 10.1002/brb3.2791] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/10/2022] [Revised: 08/28/2022] [Accepted: 10/02/2022] [Indexed: 12/24/2022] Open
Abstract
OBJECTIVE To study the clinical features and power spectral entropy (PSE) of electroencephalography signals in Wilson's disease (WD) patients with dystonia. METHODS Several scale evaluations were performed to assess the clinical features of WD patients. Demographic information and electroencephalography signals were obtained in all subjects. RESULTS 34 WD patients with dystonia were recruited in the case group and 24 patients without dystonia were recruited in the control group. 20 healthy individuals were included in the healthy control group. The mean body mass index (BMI) in the case group was significantly lower than that in the controls (p < .05). The case group had significantly higher SAS, SDS, and Bucco-Facial-Apraxia Assessment scores (p < .05). Total BADS scores in the case group were lower than those in the control group (p < .01). Note that 94.11% of the case group presented with dysarthria and 70.59% of them suffered from dysphagia. Dysphagia was mainly related to the oral preparatory stage and oral stage. Mean power spectral entropy (PSE) values in the case group were significantly different (p < .05) from those in the control group and the healthy group across the different tasks. CONCLUSIONS The patients with dystonia were usually accompanied with low BMI, anxiety, depression, apraxia, executive dysfunction, dysarthria and dysphagia. The cortical activities of the WD patients with dystonia seemed to be more chaotic during the eyes-closed and reading tasks but lower during the swallowing stages than those in the control group.
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Affiliation(s)
- Shaoru Zhang
- Department of Neurology, The First Affiliated Hospital, Clinical Medicine College of Guangdong Pharmaceutical University, Guangzhou, Guangdong, China
| | - Aiqun Liu
- Department of Neurology, The First Affiliated Hospital, Clinical Medicine College of Guangdong Pharmaceutical University, Guangzhou, Guangdong, China
| | - Zhihua Zhou
- Department of Neurology, The First Affiliated Hospital, Clinical Medicine College of Guangdong Pharmaceutical University, Guangzhou, Guangdong, China
| | - Zheng Huang
- Department of Neurology, The First Affiliated Hospital, Clinical Medicine College of Guangdong Pharmaceutical University, Guangzhou, Guangdong, China
| | - Jing Cheng
- Department of Neurology, The First Affiliated Hospital, Clinical Medicine College of Guangdong Pharmaceutical University, Guangzhou, Guangdong, China
| | - Danping Chen
- Department of Neurology, The First Affiliated Hospital, Clinical Medicine College of Guangdong Pharmaceutical University, Guangzhou, Guangdong, China
| | - Qizhi Zhong
- Department of Neurology, The First Affiliated Hospital, Clinical Medicine College of Guangdong Pharmaceutical University, Guangzhou, Guangdong, China
| | - Qingyun Yu
- Department of Neurology, The First Affiliated Hospital, Clinical Medicine College of Guangdong Pharmaceutical University, Guangzhou, Guangdong, China
| | - Zhongxing Peng
- Department of Neurology, The First Affiliated Hospital, Clinical Medicine College of Guangdong Pharmaceutical University, Guangzhou, Guangdong, China
| | - Mingfan Hong
- Department of Neurology, The First Affiliated Hospital, Clinical Medicine College of Guangdong Pharmaceutical University, Guangzhou, Guangdong, China
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Sohaib M, Ghaffar A, Shin J, Hasan MJ, Suleman MT. Automated Analysis of Sleep Study Parameters Using Signal Processing and Artificial Intelligence. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2022; 19:13256. [PMID: 36293844 PMCID: PMC9603486 DOI: 10.3390/ijerph192013256] [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: 08/20/2022] [Revised: 09/27/2022] [Accepted: 10/12/2022] [Indexed: 06/16/2023]
Abstract
An automated sleep stage categorization can readily face noise-contaminated EEG recordings, just as other signal processing applications. Therefore, the denoising of the contaminated signals is inevitable to ensure a reliable analysis of the EEG signals. In this research work, an empirical mode decomposition is used in combination with stacked autoencoders to conduct automatic sleep stage classification with reliable analytical performance. Due to the decomposition of the composite signal into several intrinsic mode functions, empirical mode decomposition offers an effective solution for denoising non-stationary signals such as EEG. Preliminary results showed that through these intrinsic modes, a signal with a high signal-to-noise ratio can be obtained, which can be used for further analysis with confidence. Therefore, later, when statistical features were extracted from the denoised signals and were classified using stacked autoencoders, improved results were obtained for Stage 1, Stage 2, Stage 3, Stage 4, and REM stage EEG signals using this combination.
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Affiliation(s)
- Muhammad Sohaib
- Department of Software Engineering, Lahore Garrison University, Lahore 54000, Pakistan
| | - Ayesha Ghaffar
- Department of Software Engineering, Lahore Garrison University, Lahore 54000, Pakistan
| | - Jungpil Shin
- School of Computer Science and Engineering, The University of Aizu, Aizuwakamatsu 965-8580, Fukushima, Japan
| | - Md Junayed Hasan
- National Subsea Centre, Robert Gordon University, Scotland AB10 7AQ, UK
| | - Muhammad Taseer Suleman
- Digital Forensics Research and Service Centre, Lahore Garrison University, Lahore 54000, Pakistan or
- Department of Computer Science, School of Systems and Technology, University of Management and Technology Lahore, Lahore 54770, Pakistan
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13
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A comprehensive evaluation of contemporary methods used for automatic sleep staging. Biomed Signal Process Control 2022. [DOI: 10.1016/j.bspc.2022.103819] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
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14
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Automatic sleep scoring with LSTM networks: impact of time granularity and input signals. BIOMED ENG-BIOMED TE 2022; 67:267-281. [DOI: 10.1515/bmt-2021-0408] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2021] [Accepted: 05/17/2022] [Indexed: 11/15/2022]
Abstract
Abstract
Supervised automatic sleep scoring algorithms are usually trained using sleep stage labels manually annotated on 30 s epochs of PSG data. In this study, we investigate the impact of using shorter epochs with various PSG input signals for training and testing a Long Short Term Memory (LSTM) neural network. An LSTM model is evaluated on the provided 30 s epoch sleep stage labels from a publicly available dataset, as well as on 10 s subdivisions. Additionally, three independent scorers re-labeled a subset of the dataset on shorter time windows. The automatic sleep scoring experiments were repeated on the re-annotated subset.The highest performance is achieved on features extracted from 30 s epochs of a single channel frontal EEG. The resulting accuracy, precision and recall were of 92.22%, 67.58% and 66.00% respectively. When using a shorter epoch as input, the performance decreased by approximately 20%. Re-annotating a subset of the dataset on shorter time epochs did not improve the results and further altered the sleep stage detection performance. Our results show that our feature-based LSTM classification algorithm performs better on 30 s PSG epochs when compared to 10 s epochs used as input. Future work could be oriented to determining whether varying the epoch size improves classification outcomes for different types of classification algorithms.
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15
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Detection of Sleep Apnea from Electrocardiogram and Pulse Oximetry Signals Using Random Forest. APPLIED SCIENCES-BASEL 2022. [DOI: 10.3390/app12094218] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/08/2023]
Abstract
Sleep apnea (SA) is a common sleep disorder which could impair the human physiological system. Therefore, early diagnosis of SA is of great interest. The traditional method of diagnosing SA is an overnight polysomnography (PSG) evaluation. When PSG has limited availability, automatic SA screening with a fewer number of signals should be considered. The primary purpose of this study is to develop and evaluate a SA detection model based on electrocardiogram (ECG) and blood oxygen saturation (SpO2). We adopted a multimodal approach to fuse ECG and SpO2 signals at the feature level. Then, feature selection was conducted using the recursive feature elimination with cross-validation (RFECV) algorithm and random forest (RF) classifier used to discriminate between apnea and normal events. Experiments were conducted on the Apnea-ECG database. The introduced algorithm obtained an accuracy of 97.5%, a sensitivity of 95.9%, a specificity of 98.4% and an AUC of 0.992 in per-segment classification, and outperformed previous works. The results showed that ECG and SpO2 are complementary in detecting SA, and that the combination of ECG and SpO2 enhances the ability to diagnose SA. Therefore, the proposed method has the potential to be an alternative to conventional detection methods.
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16
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Phan H, Mikkelsen K. Automatic sleep staging of EEG signals: recent development, challenges, and future directions. Physiol Meas 2022; 43. [PMID: 35320788 DOI: 10.1088/1361-6579/ac6049] [Citation(s) in RCA: 34] [Impact Index Per Article: 17.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2021] [Accepted: 03/23/2022] [Indexed: 11/11/2022]
Abstract
Modern deep learning holds a great potential to transform clinical practice on human sleep. Teaching a machine to carry out routine tasks would be a tremendous reduction in workload for clinicians. Sleep staging, a fundamental step in sleep practice, is a suitable task for this and will be the focus in this article. Recently, automatic sleep staging systems have been trained to mimic manual scoring, leading to similar performance to human sleep experts, at least on scoring of healthy subjects. Despite tremendous progress, we have not seen automatic sleep scoring adopted widely in clinical environments. This review aims to give a shared view of the authors on the most recent state-of-the-art development in automatic sleep staging, the challenges that still need to be addressed, and the future directions for automatic sleep scoring to achieve clinical value.
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Affiliation(s)
- Huy Phan
- School of Electronic Engineering and Computer Science, Queen Mary University of London, Mile End Rd, London, E1 4NS, UNITED KINGDOM OF GREAT BRITAIN AND NORTHERN IRELAND
| | - Kaare Mikkelsen
- Department of Electrical and Computer Engineering, Aarhus Universitet, Finlandsgade 22, Aarhus, 8000, DENMARK
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Garcia-Molina G, Jiang J. Interbeat interval-based sleep staging: work in progress toward real-time implementation. Physiol Meas 2022; 43. [PMID: 35297780 DOI: 10.1088/1361-6579/ac5a78] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/05/2021] [Accepted: 03/03/2022] [Indexed: 01/27/2023]
Abstract
Objective. Cardiac activity changes during sleep enable real-time sleep staging. We developed a deep neural network (DNN) to detect sleep stages using interbeat intervals (IBIs) extracted from electrocardiogram signals.Approach. Data from healthy and apnea subjects were used for training and validation; 2 additional datasets (healthy and sleep disorders subjects) were used for testing. R-peak detection was used to determine IBIs before resampling at 2 Hz; the resulting signal was segmented into 150 s windows (30 s shift). DNN output approximated the probabilities of a window belonging to light, deep, REM, or wake stages. Cohen's Kappa, accuracy, and sensitivity/specificity per stage were determined, and Kappa was optimized using thresholds on probability ratios for each stage versus light sleep.Main results. Mean (SD) Kappa and accuracy for 4 sleep stages were 0.44 (0.09) and 0.65 (0.07), respectively, in healthy subjects. For 3 sleep stages (light+deep, REM, and wake), Kappa and accuracy were 0.52 (0.12) and 0.76 (0.07), respectively. Algorithm performance on data from subjects with REM behavior disorder or periodic limb movement disorder was significantly worse, with Kappa of 0.24 (0.09) and 0.36 (0.12), respectively. Average processing time by an ARM microprocessor for a 300-sample window was 19.2 ms.Significance. IBIs can be obtained from a variety of cardiac signals, including electrocardiogram, photoplethysmography, and ballistocardiography. The DNN algorithm presented is 3 orders of magnitude smaller compared with state-of-the-art algorithms and was developed to perform real-time, IBI-based sleep staging. With high specificity and moderate sensitivity for deep and REM sleep, small footprint, and causal processing, this algorithm may be used across different platforms to perform real-time sleep staging and direct intervention strategies.Novelty & Significance(92/100 words) This article describes the development and testing of a deep neural network-based algorithm to detect sleep stages using interbeat intervals, which can be obtained from a variety of cardiac signals including photoplethysmography, electrocardiogram, and ballistocardiography. Based on the interbeat intervals identified in electrocardiogram signals, the algorithm architecture included a group of convolution layers and a group of long short-term memory layers. With its small footprint, fast processing time, high specificity and good sensitivity for deep and REM sleep, this algorithm may provide a good option for real-time sleep staging to direct interventions.
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Affiliation(s)
| | - Jiewei Jiang
- Sleep Number Labs, San Jose, CA, United States of America
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18
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Classification of Sleep Stage with Biosignal Images Using Convolutional Neural Networks. APPLIED SCIENCES-BASEL 2022. [DOI: 10.3390/app12063028] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/27/2023]
Abstract
Clinicians and researchers divide sleep periods into different sleep stages to analyze the quality of sleep. Despite advances in machine learning, sleep-stage classification is still performed manually. The classification process is tedious and time-consuming, but its automation has not yet been achieved. Another problem is low accuracy due to inconsistencies between somnologists. In this paper, we propose a method to classify sleep stages using a convolutional neural network. The network is trained with EEG and EOG images of time and frequency domains. The images of the biosignal are appropriate as inputs to the network, as these are natural inputs provided to somnologists in polysomnography. To validate the network, the sleep-stage classifier was trained and tested using the public Sleep-EDFx dataset. The results show that the proposed method achieves state-of-the-art performance on the Sleep-EDFx (accuracy 94%, F1 94%). The results demonstrate that the classifier is able to learn features described in the sleep scoring manual from the sleep data.
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Ioannides AA, Orphanides GA, Liu L. Rhythmicity in heart rate and its surges usher a special period of sleep, a likely home for PGO waves. Curr Res Physiol 2022; 5:118-141. [PMID: 35243361 PMCID: PMC8867048 DOI: 10.1016/j.crphys.2022.02.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/14/2021] [Revised: 02/01/2022] [Accepted: 02/06/2022] [Indexed: 11/30/2022] Open
Abstract
High amplitude electroencephalogram (EEG) events, like unitary K-complex (KC), are used to partition sleep into stages and hence define the hypnogram, a key instrument of sleep medicine. Throughout sleep the heart rate (HR) changes, often as a steady HR increase leading to a peak, what is known as a heart rate surge (HRS). The hypnogram is often unavailable when most needed, when sleep is disturbed and the graphoelements lose their identity. The hypnogram is also difficult to define during normal sleep, particularly at the start of sleep and the periods that precede and follow rapid eye movement (REM) sleep. Here, we use objective quantitative criteria that group together periods that cannot be assigned to a conventional sleep stage into what we call REM0 periods, with the presence of a HRS one of their defining properties. Extended REM0 periods are characterized by highly regular sequences of HRS that generate an infra-low oscillation around 0.05 Hz. During these regular sequence of HRS, and just before each HRS event, we find avalanches of high amplitude events for each one of the mass electrophysiological signals, i.e. related to eye movement, the motor system and the general neural activity. The most prominent features of long REM0 periods are sequences of three to five KCs which we label multiple K-complexes (KCm). Regarding HRS, a clear dissociation is demonstrated between the presence or absence of high gamma band spectral power (55-95 Hz) of the two types of KCm events: KCm events with strong high frequencies (KCmWSHF) cluster just before the peak of HRS, while KCm between HRS show no increase in high gamma band (KCmNOHF). Tomographic estimates of activity from magnetoencephalography (MEG) in pre-KC periods (single and multiple) showed common increases in the cholinergic Nucleus Basalis of Meynert in the alpha band. The direct contrast of KCmWSHF with KCmNOHF showed increases in all subjects in the high sigma band in the base of the pons and in three subjects in both the delta and high gamma bands in the medial Pontine Reticular Formation (mPRF), the putative Long Lead Initial pulse (LLIP) for Ponto-Geniculo-Occipital (PGO) waves.
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Affiliation(s)
- Andreas A. Ioannides
- Lab. for Human Brain Dynamics, AAI Scientific Cultural Services Ltd., Nicosia, 1065, Cyprus
| | - Gregoris A. Orphanides
- Lab. for Human Brain Dynamics, AAI Scientific Cultural Services Ltd., Nicosia, 1065, Cyprus
- The English School, Nicosia, 1684, Cyprus
| | - Lichan Liu
- Lab. for Human Brain Dynamics, AAI Scientific Cultural Services Ltd., Nicosia, 1065, Cyprus
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Metzner C, Schilling A, Traxdorf M, Schulze H, Krauss P. Sleep as a random walk: a super-statistical analysis of EEG data across sleep stages. Commun Biol 2021; 4:1385. [PMID: 34893700 PMCID: PMC8664947 DOI: 10.1038/s42003-021-02912-6] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/10/2021] [Accepted: 11/23/2021] [Indexed: 11/15/2022] Open
Abstract
In clinical practice, human sleep is classified into stages, each associated with different levels of muscular activity and marked by characteristic patterns in the EEG signals. It is however unclear whether this subdivision into discrete stages with sharply defined boundaries is truly reflecting the dynamics of human sleep. To address this question, we consider one-channel EEG signals as heterogeneous random walks: stochastic processes controlled by hyper-parameters that are themselves time-dependent. We first demonstrate the heterogeneity of the random process by showing that each sleep stage has a characteristic distribution and temporal correlation function of the raw EEG signals. Next, we perform a super-statistical analysis by computing hyper-parameters, such as the standard deviation, kurtosis, and skewness of the raw signal distributions, within subsequent 30-second epochs. It turns out that also the hyper-parameters have characteristic, sleep-stage-dependent distributions, which can be exploited for a simple Bayesian sleep stage detection. Moreover, we find that the hyper-parameters are not piece-wise constant, as the traditional hypnograms would suggest, but show rising or falling trends within and across sleep stages, pointing to an underlying continuous rather than sub-divided process that controls human sleep. Based on the hyper-parameters, we finally perform a pairwise similarity analysis between the different sleep stages, using a quantitative measure for the separability of data clusters in multi-dimensional spaces. To improve our understand of how EEG activity reflects the dynamics of human sleep, Metzner et al. use human EEG data and superstatistical analysis to demonstrate that each sleep stage has a characteristic distribution and temporal correlation function of raw EEG signals. They also show that the hyper-parameters controlling the EEG signals have characteristic, sleep-stage-dependent distributions, which can be exploited for a simple Bayesian sleep stage detection.
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Affiliation(s)
- Claus Metzner
- Neuroscience Lab, Experimental Otolaryngology, University Hospital Erlangen, Erlangen, Germany.
| | - Achim Schilling
- Neuroscience Lab, Experimental Otolaryngology, University Hospital Erlangen, Erlangen, Germany.,Laboratory of Sensory and Cognitive Neuroscience, Aix-Marseille University, Marseille, France.,Cognitive Computational Neuroscience Group, Friedrich-Alexander University Erlangen-Nuremberg, Nuremberg, Germany
| | - Maximilian Traxdorf
- Department of Otorhinolaryngology, Paracelsus Medical University, Nuremberg, Germany
| | - Holger Schulze
- Neuroscience Lab, Experimental Otolaryngology, University Hospital Erlangen, Erlangen, Germany
| | - Patrick Krauss
- Neuroscience Lab, Experimental Otolaryngology, University Hospital Erlangen, Erlangen, Germany.,Cognitive Computational Neuroscience Group, Friedrich-Alexander University Erlangen-Nuremberg, Nuremberg, Germany.,Pattern Recognition Lab, Friedrich-Alexander University Erlangen-Nuremberg, Nuremberg, Germany
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21
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Liang X, Xiong J, Cao Z, Wang X, Li J, Liu C. Decreased sample entropy during sleep-to-wake transition in sleep apnea patients. Physiol Meas 2021; 42. [PMID: 33761471 DOI: 10.1088/1361-6579/abf1b2] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/12/2020] [Accepted: 03/24/2021] [Indexed: 11/12/2022]
Abstract
Objective. This study aimed to prove that there is a sudden change in the human physiology system when switching from one sleep stage to another and physical threshold-based sample entropy (SampEn) is able to capture this transition in an RR interval time series from patients with disorders such as sleep apnea.Approach. Physical threshold-based SampEn was used to analyze different sleep-stage RR segments from sleep apnea subjects in the St. Vincents University Hospital/University College Dublin Sleep Apnea Database, and SampEn differences were compared between two consecutive sleep stages. Additionally, other standard heart rate variability (HRV) measures were also analyzed to make comparisons.Main results. The findings suggested that the sleep-to-wake transitions presented a SampEn decrease significantly larger than intra-sleep ones (P < 0.01), which outperformed other standard HRV measures. Moreover, significant entropy differences between sleep and subsequent wakefulness appeared when the previous sleep stage was either S1 (P < 0.05), S2 (P < 0.01) or S4 (P < 0.05).Significance. The results demonstrated that physical threshold-based SampEn has the capability of depicting physiological changes in the cardiovascular system during the sleep-to-wake transition in sleep apnea patients and it is more reliable than the other analyzed HRV measures. This noninvasive HRV measure is a potential tool for further evaluation of sleep physiological time series.
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Affiliation(s)
- Xueyu Liang
- The State Key Laboratory of Bioelectronics, School of Instrument Science and Engineering, Southeast University, Nanjing, 210096, People's Republic of China
| | - Jinle Xiong
- The State Key Laboratory of Bioelectronics, School of Instrument Science and Engineering, Southeast University, Nanjing, 210096, People's Republic of China
| | - Zhengtao Cao
- Air Force Medical Center, PLA. Beijing, 100142, People's Republic of China
| | - Xingyao Wang
- The State Key Laboratory of Bioelectronics, School of Instrument Science and Engineering, Southeast University, Nanjing, 210096, People's Republic of China
| | - Jianqing Li
- The State Key Laboratory of Bioelectronics, School of Instrument Science and Engineering, Southeast University, Nanjing, 210096, People's Republic of China
| | - Chengyu Liu
- The State Key Laboratory of Bioelectronics, School of Instrument Science and Engineering, Southeast University, Nanjing, 210096, People's Republic of China
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22
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Photoplethysmography in Normal and Pathological Sleep. SENSORS 2021; 21:s21092928. [PMID: 33922042 PMCID: PMC8122413 DOI: 10.3390/s21092928] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/29/2021] [Revised: 04/19/2021] [Accepted: 04/20/2021] [Indexed: 01/20/2023]
Abstract
This article presents an overview of the advancements that have been made in the use of photoplethysmography (PPG) for unobtrusive sleep studies. PPG is included in the quickly evolving and very popular landscape of wearables but has specific interesting properties, particularly the ability to capture the modulation of the autonomic nervous system during sleep. Recent advances have been made in PPG signal acquisition and processing, including coupling it with accelerometry in order to construct hypnograms in normal and pathologic sleep and also to detect sleep-disordered breathing (SDB). The limitations of PPG (e.g., oxymetry signal failure, motion artefacts, signal processing) are reviewed as well as technical solutions to overcome these issues. The potential medical applications of PPG are numerous, including home-based detection of SDB (for triage purposes), and long-term monitoring of insomnia, circadian rhythm sleep disorders (to assess treatment effects), and treated SDB (to ensure disease control). New contact sensor combinations to improve future wearables seem promising, particularly tools that allow for the assessment of brain activity. In this way, in-ear EEG combined with PPG and actigraphy could be an interesting focus for future research.
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23
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Imtiaz SA. A Systematic Review of Sensing Technologies for Wearable Sleep Staging. SENSORS (BASEL, SWITZERLAND) 2021; 21:1562. [PMID: 33668118 PMCID: PMC7956647 DOI: 10.3390/s21051562] [Citation(s) in RCA: 50] [Impact Index Per Article: 16.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/06/2021] [Revised: 02/13/2021] [Accepted: 02/20/2021] [Indexed: 12/15/2022]
Abstract
Designing wearable systems for sleep detection and staging is extremely challenging due to the numerous constraints associated with sensing, usability, accuracy, and regulatory requirements. Several researchers have explored the use of signals from a subset of sensors that are used in polysomnography (PSG), whereas others have demonstrated the feasibility of using alternative sensing modalities. In this paper, a systematic review of the different sensing modalities that have been used for wearable sleep staging is presented. Based on a review of 90 papers, 13 different sensing modalities are identified. Each sensing modality is explored to identify signals that can be obtained from it, the sleep stages that can be reliably identified, the classification accuracy of systems and methods using the sensing modality, as well as the usability constraints of the sensor in a wearable system. It concludes that the two most common sensing modalities in use are those based on electroencephalography (EEG) and photoplethysmography (PPG). EEG-based systems are the most accurate, with EEG being the only sensing modality capable of identifying all the stages of sleep. PPG-based systems are much simpler to use and better suited for wearable monitoring but are unable to identify all the sleep stages.
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Affiliation(s)
- Syed Anas Imtiaz
- Wearable Technologies Lab, Imperial College London, London SW7 2AZ, UK
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