Clinical applications of artificial intelligence in sleep medicine: a sleep clinician’s perspective

PhysioBank, PhysioToolkit, and PhysioNet: Components of a New Research Resource for Complex Physiologic Signals

Goldberger, A. L., Amaral, L. A. N., Glass, L., Hausdorff, J. M., Ivanov, P. C., Mark, R. G., Mietus, J. E., Moody, G. B., Peng, C.-K., & Stanley, H. E. (2000) · Circulation · doi:10.1161/01.cir.101.23.e215 · Cited by 14,809

Untitled work

Yang (2021) · doi:10.1038/s41598-020-79139-8 · Cited by 5,035

Sleep Health: Can We Define It? Does It Matter?

Buysse, D. J. (2014) · SLEEP · doi:10.5665/sleep.3298 · Cited by 2,768

DeepSleepNet: a model for automatic sleep stage scoring based on raw single-channel EEG

Supratak, A., Dong, H., Wu, C., & Guo, Y. (2017) · IEEE Transactions on Neural Systems and Rehabilitation Engineering · doi:10.1109/tnsre.2017.2721116 · Cited by 1,376

The Sleep Heart Health Study: Design, Rationale, and Methods

(1997) · Sleep · doi:10.1093/sleep/20.12.1077 · Cited by 1,331

SeqSleepNet: End-to-End Hierarchical Recurrent Neural Network for Sequence-to-Sequence Automatic Sleep Staging

Phan, H., Andreotti, F., Cooray, N., Chén, O. Y., & Vos, M. D. (2019) · IEEE Transactions on Neural Systems and Rehabilitation Engineering · doi:10.1109/tnsre.2019.2896659 · Cited by 660

A Deep Learning Architecture for Temporal Sleep Stage Classification Using Multivariate and Multimodal Time Series

Chambon, S., Galtier, M. N., Arnal, P. J., Wainrib, G., & Gramfort, A. (2018) · IEEE Transactions on Neural Systems and Rehabilitation Engineering · doi:10.1109/tnsre.2018.2813138 · Cited by 657

Computational and Mathematical Methods in Medicine

Jones, P., & Sivaloganathan, S. (2011) · Computational and Mathematical Methods in Medicine · doi:10.1155/2011/303089 · Cited by 529

Advancing Symptom Science Through Symptom Cluster Research: Expert Panel Proceedings and Recommendations

Miaskowski, C. A., Barsevick, A. M., Berger, A. M., Casagrande, R. J., Grady, P. A., Jacobsen, P. B., Kutner, J. S., Patrick, D. L., Zimmerman, L. M., Xiao, C., et al. (2016) · JNCI Journal of the National Cancer Institute · doi:10.1093/jnci/djw253 · Cited by 507

Metrics of sleep apnea severity: beyond the apnea-hypopnea index

Malhotra, A., Ayappa, I., Ayas, N., Collop, N., Kirsch, D., Mcardle, N., Mehra, R., Pack, A. I., Punjabi, N., White, D. P., et al. (2021) · SLEEP · doi:10.1093/sleep/zsab030 · Cited by 489

Neural network analysis of sleep stages enables efficient diagnosis of narcolepsy

Stephansen, J. B., Olesen, A. N., Olsen, M., Ambati, A., Leary, E. B., Moore, H. E., Carrillo, O., Lin, L., Han, F., Yan, H., et al. (2018) · Nature Communications · doi:10.1038/s41467-018-07229-3 · Cited by 455

A convolutional neural network for sleep stage scoring from raw single-channel EEG

Sors, A., Bonnet, S., Mirek, S., Vercueil, L., & Payen, J. (2018) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2017.12.001 · Cited by 435

U-Sleep: resilient high-frequency sleep staging

Perslev, M., Darkner, S., Kempfner, L., Nikolic, M., Jennum, P., & Igel, C. (2021) · npj Digital Medicine · doi:10.1038/s41746-021-00440-5 · Cited by 403

Montreal Archive of Sleep Studies: an open‐access resource for instrument benchmarking and exploratory research

O’Reilly, C., Gosselin, N., Carrier, J., & Nielsen, T. (2014) · Journal of Sleep Research · doi:10.1111/jsr.12169 · Cited by 358

Expert-level sleep scoring with deep neural networks

Biswal, S., Sun, H., Goparaju, B., Westover, M. B., Sun, J., & Bianchi, M. T. (2018) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocy131 · Cited by 290

Automated sleep scoring: A review of the latest approaches

Fiorillo, L., Puiatti, A., Papandrea, M., Ratti, P.-L., Favaro, P., Roth, C., Bargiotas, P., Bassetti, C. L., & Faraci, F. D. (2019) · Sleep Medicine Reviews · doi:10.1016/j.smrv.2019.07.007 · Cited by 284

A Deep Learning Model for Automated Sleep Stages Classification Using PSG Signals

Yıldırım, Ö., Baloğlu, U. B., & Acharya, U. R. (2019) · International Journal of Environmental Research and Public Health · doi:10.3390/ijerph16040599 · Cited by 273

CYCLOPS reveals human transcriptional rhythms in health and disease

Anafi, R. C., Francey, L. J., Hogenesch, J. B., & Kim, J. (2017) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1619320114 · Cited by 254

Statistical machine learning of sleep and physical activity phenotypes from sensor data in 96,220 UK Biobank participants

Willetts, M., Hollowell, S., Aslett, L., Holmes, C., & Doherty, A. (2018) · Scientific Reports · doi:10.1038/s41598-018-26174-1 · Cited by 250 · cited 1×

High-accuracy detection of early Parkinson’s disease through multimodal features and machine learning

Prashanth, R., Dutta Roy, S., Mandal, P. K., & Ghosh, S. (2016) · International Journal of Medical Informatics · doi:10.1016/j.ijmedinf.2016.03.001 · Cited by 244

Cardiorespiratory-Based Sleep Staging in Subjects With Obstructive Sleep Apnea

Redmond, S. J., & Heneghan, C. (2006) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2005.869773 · Cited by 231

Gender differences in nighttime sleep patterns and variability across the adult lifespan: a global-scale wearables study

Jonasdottir, S. S., Minor, K., & Lehmann, S. (2020) · SLEEP · doi:10.1093/sleep/zsaa169 · Cited by 192

Deep learning approaches for automatic detection of sleep apnea events from an electrocardiogram

Urtnasan, E., Kim, Y.-J., Park, J.-U., Joo, E. Y., & Lee, K.-J. (2019) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2019.105001 · Cited by 183

Sleep stage classification using single-channel EOG

Rahman, M. M., Bhuiyan, M. I. H., & Hassan, A. R. (2018) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2018.08.022 · Cited by 182

Universal method for robust detection of circadian state from gene expression

Braun, R., Kath, W. L., Iwanaszko, M., Kula-Eversole, E., Abbott, S. M., Reid, K. J., Zee, P. C., & Allada, R. (2018) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1800314115 · Cited by 175

More Than the Sum of the Respiratory Events: Personalized Medicine Approaches for Obstructive Sleep Apnea

Edwards, B. A., Redline, S. S., Sands, S. A., & Owens, R. L. (2019) · American Journal of Respiratory and Critical Care Medicine · doi:10.1164/rccm.201901-0014tr · Cited by 174

Deep convolutional neural networks for interpretable analysis of EEG sleep stage scoring

Vilamala, A., Madsen, K. H., & Hansen, L. K. (2017) · Machine learning for signal processing ... · doi:10.1109/mlsp.2017.8168133 · Cited by 171

Attention, Development, and Psychopathology

Merrill, E. C., & Atwell, J. A. (1998) · American Journal on Mental Retardation · doi:10.1352/0895-8017(1998)103 · Cited by 170

An end-to-end framework for real-time automatic sleep stage classification

Patanaik, A., Ong, J. L., Gooley, J. J., Ancoli‐Israel, S., & Chee, M. W. L. (2018) · SLEEP · doi:10.1093/sleep/zsy041 · Cited by 170

Sleep Stage Scoring Using the Neural Network Model: Comparison Between Visual and Automatic Analysis in Normal Subjects and Patients

Schaltenbrand, N., Lengelle, R., Toussaint, M., Luthringer, R., Carelli, G., Jacqmin, A., Lainey, E., Muzet, A. G., & Macher, J. P. (1996) · SLEEP · doi:10.1093/sleep/19.1.26 · Cited by 157

Automatic Human Sleep Stage Scoring Using Deep Neural Networks

Malafeev, A., Laptev, D., Bauer, S., Omlin, X., Wierzbicka, A., Wichniak, A., Jernajczyk, W., Riener, R., Buhmann, J., & Achermann, P. (2018) · Frontiers in Neuroscience · doi:10.3389/fnins.2018.00781 · Cited by 151

Artificial intelligence in sleep medicine: background and implications for clinicians

Goldstein, C. A., Berry, R. B., Kent, D. T., Kristo, D. A., Seixas, A. A., Redline, S. S., & Westover, M. B. (2020) · Journal of Clinical Sleep Medicine · doi:10.5664/jcsm.8388 · Cited by 147

Convolution- and Attention-Based Neural Network for Automated Sleep Stage Classification

Zhu, T., Luo, W., & Yu, F. N. (2020) · International Journal of Environmental Research and Public Health · doi:10.3390/ijerph17114152 · Cited by 142

Sleep staging from electrocardiography and respiration with deep learning

Sun, H., Ganglberger, W., Panneerselvam, E., Leone, M. J., Quadri, S. A., Goparaju, B., Tesh, R. A., Akeju, O., THOMAS, R. J., & Westover, M. B. (2019) · SLEEP · doi:10.1093/sleep/zsz306 · Cited by 139

Deep learning enables sleep staging from photoplethysmogram for patients with suspected sleep apnea

Korkalainen, H., Aakko, J., Duce, B., Kainulainen, S., Leino, A., Nikkonen, S., Afara, I. O., Myllymaa, S., Töyräs, J., & Leppänen, T. (2020) · SLEEP · doi:10.1093/sleep/zsaa098 · Cited by 138

An Evaluation of Cardiorespiratory and Movement Features With Respect to Sleep-Stage Classification

Willemen, T., Deun, D. V., Verhaert, V., Vandekerckhove, M. M. P., Exadaktylos, V., Verbraecken, J. A., Huffel, S. V., Haex, B., & Sloten, J. V. (2013) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2013.2276083 · Cited by 131

Which Sleep Health Characteristics Predict All-Cause Mortality in Older Men? An Application of Flexible Multivariable Approaches

Wallace, M. L., Stone, K. L., Smagula, S. F., Hall, M. H., Şimşek, B., Kado, D. M., Redline, S. S., Vo, T., & Buysse, D. J. (2017) · SLEEP · doi:10.1093/sleep/zsx189 · Cited by 121

Automatic and Unsupervised Snore Sound Extraction From Respiratory Sound Signals

Azarbarzin, A., & Moussavi, Z. M. K. (2010) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2010.2061846 · Cited by 115

A Neural Network Method for Detection of Obstructive Sleep Apnea and Narcolepsy Based on Pupil Size and EEG

Liu, D., Pang, Z., & Lloyd, S. R. (2008) · IEEE Transactions on Neural Networks · doi:10.1109/tnn.2007.908634 · Cited by 112

A New Method for Automatic Sleep Stage Classification

Zhang, J., & Wu, Y. X. (2017) · IEEE Transactions on Biomedical Circuits and Systems · doi:10.1109/tbcas.2017.2719631 · Cited by 112

Obstructive sleep apnea detection using SVM-based classification of ECG signal features

Almazaydeh, L. H., Elleithy, K. M., & Faezipour, M. (2012) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc.2012.6347100 · Cited by 110

Deep Recurrent Neural Networks for Automatic Detection of Sleep Apnea from Single Channel Respiration Signals

ElMoaqet, H., Eid, M., Glos, M., Ryalat, M., & Penzel, T. (2020) · Sensors · doi:10.3390/s20185037 · Cited by 108

Detection of REM sleep behaviour disorder by automated polysomnography analysis

Cooray, N., Andreotti, F., Lo, C., Symmonds, M., Hu, M. T. M., & Vos, M. D. (2019) · Clinical Neurophysiology · doi:10.1016/j.clinph.2019.01.011 · Cited by 107

Automated sleep stage scoring of the Sleep Heart Health Study using deep neural networks

Zhang, L., Fabbri, D., Upender, R. P., & Kent, D. T. (2019) · SLEEP · doi:10.1093/sleep/zsz159 · Cited by 101

Automatic sleep stage classification with deep residual networks in a mixed-cohort setting

Olesen, A. N., Jennum, P., Mignot, E. J.-M., & Sørensen, H. B. D. (2020) · SLEEP · doi:10.1093/sleep/zsaa161 · Cited by 97

Deep Learning and Insomnia: Assisting Clinicians With Their Diagnosis

Shahin, M., Ahmed, B., Hamida, S. T.-B., Mulaffer, F. L., Glos, M., & Penzel, T. (2017) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2017.2650199 · Cited by 96

A novel, fast and efficient single-sensor automatic sleep-stage classification based on complementary cross-frequency coupling estimates

Dimitriadis, S. I., Salis, C. I., & Linden, D. E. J. (2018) · Clinical Neurophysiology · doi:10.1016/j.clinph.2017.12.039 · Cited by 93

A new method for sleep apnea classification using wavelets and feedforward neural networks

Fontenla-Romero, Ó., Guijarro‐Berdiñas, B., Alonso‐Betanzos, A., & Moret‐Bonillo, V. (2004) · Artificial Intelligence in Medicine · doi:10.1016/j.artmed.2004.07.014 · Cited by 86

Automatic Sleep-Stage Scoring in Healthy and Sleep Disorder Patients Using Optimal Wavelet Filter Bank Technique with EEG Signals

Sharma, M. D., Tiwari, J., & Acharya, U. R. (2021) · International Journal of Environmental Research and Public Health · doi:10.3390/ijerph18063087 · Cited by 85

Predicting circadian phase across populations: a comparison of mathematical models and wearable devices

Huang, Y., Mayer, C., Cheng, P., Siddula, A., Burgess, H. J., Drake, C. L., Goldstein, C. A., Walch, O., & Forger, D. B. (2021) · SLEEP · doi:10.1093/sleep/zsab126 · Cited by 83

A Novel Model to Estimate Key Obstructive Sleep Apnea Endotypes from Standard Polysomnography and Clinical Data and Their Contribution to Obstructive Sleep Apnea Severity

Dutta, R., Delaney, G. W., Toson, B., Jordan, A. S., White, D. P., Wellman, A., & Eckert, D. J. (2020) · Annals of the American Thoracic Society · doi:10.1513/annalsats.202001-064oc · Cited by 82

Artificial intelligence and sleep: Advancing sleep medicine

Watson, N. F., & Fernandez, C. (2021) · Sleep Medicine Reviews · doi:10.1016/j.smrv.2021.101512 · Cited by 81

Automatic Sleep Stage Classification Based on Convolutional Neural Network and Fine‐Grained Segments

Cui, Z., Zheng, X., Shao, X., & Cui, L. (2018) · Complexity · doi:10.1155/2018/9248410 · Cited by 75

Sleep Versus Wake Classification From Heart Rate Variability Using Computational Intelligence: Consideration of Rejection in Classification Models

Lewicke, A., Sazonov, E. E., Corwin, M. J., Neuman, M. R., & Schuckers, S. (2008) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2007.900558 · Cited by 74

Support vector machine prediction of obstructive sleep apnea in a large-scale Chinese clinical sample

Huang, W.-C., Lee, P., Liu, Y., Chiang, A. A.-P., & Lai, F. (2020) · SLEEP · doi:10.1093/sleep/zsz295 · Cited by 74

A review on current trends in automatic sleep staging through bio-signal recordings and future challenges

Chriskos, P., Frantzidιs, C. A., Nday, C. M., Gkivogkli, P. T., Bamidis, P. D., & Kourtidou‐Papadeli, C. (2020) · Sleep Medicine Reviews · doi:10.1016/j.smrv.2020.101377 · Cited by 74

Extracting health-related causality from twitter messages using natural language processing

Doan, S., Yang, E. W., Tilak, S. S., Li, P. W., Zisook, D. S., & Torii, M. (2019) · BMC Medical Informatics and Decision Making · doi:10.1186/s12911-019-0785-0 · Cited by 73

Expert-level automated sleep staging of long-term scalp electroencephalography recordings using deep learning

Jaoude, M. A., Sun, H., Pellerin, K. R., Pavlova, M. K., Sarkis, R. A., Cash, S. S., Westover, M. B., & Lam, A. D. (2020) · SLEEP · doi:10.1093/sleep/zsaa112 · Cited by 73

A scalable method of determining physiological endotypes of sleep apnea from a polysomnographic sleep study

Finnsson, E., Ólafsdóttir, G. H., Loftsdóttir, D. L., Jónsson, S. Æ., Helgadóttir, H., Ágústsson, J. S., Sands, S. A., & Wellman, A. (2020) · SLEEP · doi:10.1093/sleep/zsaa168 · Cited by 70

Sleep problems, internalizing and externalizing symptoms, and domains of health-related quality of life: bidirectional associations from early childhood to early adolescence

Williamson, A. A., Zendarski, N. J., Lange, K., Quach, J., Molloy, C. S., Clifford, S. A., & Mulraney, M. (2020) · SLEEP · doi:10.1093/sleep/zsaa139 · Cited by 68

Single Channel ECG for Obstructive Sleep Apnea Severity Detection Using a Deep Learning Approach

Banluesombatkul, N., Rakthanmanon, T., & Wilaiprasitporn, T. (2018) · IEEE Region 10 Conference (TENCON) · doi:10.1109/tencon.2018.8650429 · Cited by 67

Protocol of the SOMNIA project: an observational study to create a neurophysiological database for advanced clinical sleep monitoring

van Gilst, M. M., van Dijk, J. P., Krijn, R., Hoondert, B., Fonseca, P. M., van Sloun, R. J. G., Arsenali, B., Vandenbussche, N., Pillen, S., Maass, H., et al. (2019) · BMJ Open · doi:10.1136/bmjopen-2019-030996 · Cited by 66

Exploring the clinical features of narcolepsy type 1 versus narcolepsy type 2 from European Narcolepsy Network database with machine learning

Zhang, Z., Mayer, G., Dauvilliers, Y. A., Plazzi, G., Pizza, F., Fronczek, R., Santamaría, J., Partinen, M., Overeem, S., Peraita‐Adrados, R., et al. (2018) · Scientific Reports · doi:10.1038/s41598-018-28840-w · Cited by 64

Complex-valued unsupervised convolutional neural networks for sleep stage classification

Zhang, J., & Wu, Y. X. (2018) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2018.07.015 · Cited by 61

Smartphone-based virtual agents to help individuals with sleep concerns during COVID-19 confinement: feasibility study

Philip, P., Dupuy, L., Morin, C. M., de Sevin, E., Bioulac, S., Taillard, J., Serre, F., Auriacombe, M., & Micoulaud-Franchi, J.-A. (2020) · Journal of Medical Internet Research · doi:10.2196/24268 · Cited by 61

Automated multi-model deep neural network for sleep stage scoring with unfiltered clinical data

Zhang, X., Xu, M., Li, Y., Su, M.-M., Xu, Z., Wang, C., Kang, D., Li, H., Mu, X., Xiu, D., et al. (2020) · Sleep And Breathing · doi:10.1007/s11325-019-02008-w · Cited by 57

Sleep Stage Classification Using Time-Frequency Spectra From Consecutive Multi-Time Points

Xu, Z., Yang, X., Sun, J., Liu, P., & Qin, W. (2020) · Frontiers in Neuroscience · doi:10.3389/fnins.2020.00014 · Cited by 56

Convolutional Neural Networks on Multiple Respiratory Channels to Detect Hypopnea and Obstructive Apnea Events

Haidar, R., McCloskey, S., Koprinska, I., & Jeffries, B. (2018) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn.2018.8489248 · Cited by 55

An Optimal Time for Treatment—Predicting Circadian Time by Machine Learning and Mathematical Modelling

Hesse, J., Malhan, D., Yalҫin, M., Aboumanify, O., Basti, A., & Relógio, A. (2020) · Cancers · doi:10.3390/cancers12113103 · Cited by 55

Data-driven modeling of sleep EEG and EOG reveals characteristics indicative of pre-Parkinson's and Parkinson's disease

Christensen, J. A. E., Zoetmulder, M., Koch, H., Frandsen, R. A. V., Arvastson, L., Christensen, S. R., Jennum, P., & Sørensen, H. B. D. (2014) · Journal of Neuroscience Methods · doi:10.1016/j.jneumeth.2014.07.014 · Cited by 51

Validity of Neural Network in Sleep Apnea

El-Solh, A., Mador, M. J., TenBrock, E., Shucard, D. W., Abul-Khoudoud, M. R., & Grant, B. J. B. (1999) · SLEEP · doi:10.1093/sleep/22.1.105 · Cited by 50

Lower socioeconomic status and co-morbid conditions are associated with reduced continuous positive airway pressure adherence among older adult medicare beneficiaries with obstructive sleep apnea

Wickwire, E. M., Jobe, S. L., Oldstone, L., Scharf, S. M., Johnson, A. M., & Albrecht, J. S. (2020) · SLEEP · doi:10.1093/sleep/zsaa122 · Cited by 50

Combined unsupervised-supervised machine learning for phenotyping complex diseases with its application to obstructive sleep apnea

Ma, E.-Y., Kim, J., Lee, Y., Cho, S.-W., Kim, J. K., & Kim, J. K. (2021) · Scientific Reports · doi:10.1038/s41598-021-84003-4 · Cited by 50

AI-based approach to automatic sleep classification

Kubát, M., Pfurtscheller, G., & Flotzinger, D. (1994) · Biological Cybernetics · doi:10.1007/bf00203237 · Cited by 46

Automatic analysis of single-channel sleep EEG in a large spectrum of sleep disorders

Peter‐Derex, L., Berthomier, C., Taillard, J., Berthomier, P., Bouet, R., Mattout, J., Brandewinder, M., & Bastuji, H. (2020) · Journal of Clinical Sleep Medicine · doi:10.5664/jcsm.8864 · Cited by 46

Singular Spectrum Analysis of Sleep EEG in Insomnia

Aydın, S., Saraoğlu, H. M., & Kara, S. (2009) · Journal of Medical Systems · doi:10.1007/s10916-009-9381-7 · Cited by 41

Machine Learning in Control Systems: An Overview of the State of the Art

Moe, S., Rustad, A. M., & Hanssen, K. G. (2018) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-04191-5_23 · Cited by 37

Multivariate Analysis of Full-Term Neonatal Polysomnographic Data

Gerla, V., Paul, K., Lhotská, L., & Krajča, V. (2008) · IEEE Transactions on Information Technology in Biomedicine · doi:10.1109/titb.2008.2007193 · Cited by 35

Characterization of cyclic alternating pattern during sleep in older men and women using large population studies

Hartmann, S., Bruni, O., Ferri, R., Redline, S. S., & Baumert, M. (2020) · SLEEP · doi:10.1093/sleep/zsaa016 · Cited by 35

Multi-Class Sleep Stage Analysis and Adaptive Pattern Recognition

Procházka, A., Kuchyňka, J., Vyšata, O., Cejnar, P., Vališ, M., & Mařík, V. (2018) · Applied Sciences · doi:10.3390/app8050697 · Cited by 32

Clustering Insomnia Patterns by Data From Wearable Devices: Algorithm Development and Validation Study

Park, S. (., Lee, S. W., Han, S., & Cha, M. (2019) · JMIR mHealth and uHealth · doi:10.2196/14473 · Cited by 30

Sleep and Big Data: harnessing data, technology, and analytics for monitoring sleep and improving diagnostics, prediction, and interventions—an era forSleep-Omics?

Redline, S. S., & Purcell, S. (2021) · SLEEP · doi:10.1093/sleep/zsab107 · Cited by 25

Beyond K-complex binary scoring during sleep: probabilistic classification using deep learning

Lechat, B., Hansen, K. L., CATCHESIDE, P. G., & Zajamšek, B. (2020) · SLEEP · doi:10.1093/sleep/zsaa077 · Cited by 23

Development of a Minimally Invasive Screening Tool to Identify Obese Pediatric Population at Risk of Obstructive Sleep Apnea/Hypopnea Syndrome

Calderón, J. M., Álvarez‐Pitti, J., Ortolá, I. C., Ponce, F., & Redón, P. (2020) · Bioengineering · doi:10.3390/bioengineering7040131 · Cited by 21

Contactless recording of sleep apnea and periodic leg movements by nocturnal 3-D-video and subsequent visual perceptive computing

Veauthier, C., Ryczewski, J., Mansow‐Model, S., Otte, K., Kayser, B., Glos, M., Schöbel, C., Paul, F., Brandt, A. U., & Penzel, T. (2019) · Scientific Reports · doi:10.1038/s41598-019-53050-3 · Cited by 20

Diagnostic value of sleep stage dissociation as visualized on a 2-dimensional sleep state space in human narcolepsy

Olsen, A. V., Stephansen, J. B., Leary, E. B., Peppard, P. E., Sheungshul, H., Jennum, P., Sørensen, H. B. D., & Mignot, E. J.-M. (2017) · Journal of Neuroscience Methods · doi:10.1016/j.jneumeth.2017.02.004 · Cited by 19

Sleep–Wake Transition in Narcolepsy and Healthy Controls Using a Support Vector Machine

Jensen, J. B., Sørensen, H. B. D., Kempfner, J., Sørensen, G. L., Knudsen, S., & Jennum, P. (2014) · Journal of Clinical Neurophysiology · doi:10.1097/wnp.0000000000000074 · Cited by 18

Twice is nice? Test-retest reliability of the Multiple Sleep Latency Test in the central disorders of hypersomnolence

Trotti, L. M. (2020) · Journal of Clinical Sleep Medicine · doi:10.5664/jcsm.8884 · Cited by 18

Fuzzy structural algorithms to identify and characterize apnea and hypopnea episodes

Otero, A., Félix, P., Álvarez, M. R., & Zamarrón, C. (2008) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/iembs.2008.4650396 · Cited by 17

Automated Frequency Analysis of Synchronous and Diffuse Sleep Spindles

Huupponen, E., Saastamoinen, A., Niemi, J., Virkkala, J., Hasan, J., Värri, A. O., & Himanen, S. (2005) · Neuropsychobiology · doi:10.1159/000085821 · Cited by 13

The utility of neural network in the diagnosis of Cheyne-Stokes respiration

El-Solh, A., Magalang, U. J., Mador, M. J., Dmochowski, J., Veeramachaneni, S. A., Saberi, A., Draw, A., Lieber, B. B., & Grant, B. J. B. (2003) · Journal of Medical Engineering & Technology · doi:10.1080/0309190021000043693 · Cited by 12

A fuzzy logic based apnoea monitor for SIDS risk infants

Burke, M. J., & Downes, R. (2006) · Journal of Medical Engineering & Technology · doi:10.1080/03091900600590140 · Cited by 12

Insomnia Characterization: From Hypnogram to Graph Spectral Theory

Chaparro-Vargas, R., Ahmed, B., Wessel, N., Penzel, T., & Cvetković, D. (2016) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2016.2515261 · Cited by 12

Detection of Periodic Leg Movements by Machine Learning Methods Using Polysomnographic Parameters Other Than Leg Electromyography

Umut, İ., & Çentik, G. (2016) · Computational and Mathematical Methods in Medicine · doi:10.1155/2016/2041467 · Cited by 12

Precision Medicine in Rapid Eye Movement Sleep Behavior Disorder

Högl, B., Santamaría, J., Iranzo, Á., & Stefani, A. (2019) · Sleep Medicine Clinics · doi:10.1016/j.jsmc.2019.04.003 · Cited by 11

Basics of Machine Learning

Ayyadevara, V. K. (2018) · Apress eBooks · doi:10.1007/978-1-4842-3564-5_1 · Cited by 10

ECG Biomarkers for Simultaneous Detection of Obstructive Sleep Apnea and Cheyne-Stokes Breathing

Suhas, S. R., Behbehani, K., Vijendra, S., Burk, J. R., & Lucas, E. A. (2007) · Conference proceedings · doi:10.1109/iembs.2007.4352474 · Cited by 6

Expert system application for prioritizing preventive actions for shift work: shift expert

Esen, H., HATİPOĞLU, T., Cihan, A., & Fığlalı, N. (2017) · International Journal of Occupational Safety and Ergonomics · doi:10.1080/10803548.2017.1350392 · Cited by 6

[A Domestic Diagnosis System for Early Restless Legs Syndrome Based on Deep Learning].

Zhou, P., Huang, L., Zhao, Q., Xiao, W., & Li, S. (2019) · PubMed · doi:10.3969/j.issn.1671-7104.2019.02.001 · Cited by 6

Author Correction: Deep learning for automated sleep staging using instantaneous heart rate

Sridhar, N., Shoeb, A. H., Stephens, P., Kharbouch, A. A., Shimol, D. B., Burkart, J., Ghoreyshi, A., & Myers, L. J. (2020) · npj Digital Medicine · doi:10.1038/s41746-020-00337-9 · Cited by 6

The Role of AI in Hospitals and Clinics: Transforming Healthcare in the 21st Century

Varnosfaderani, S. M., & Forouzanfar, M. (2024) · Bioengineering · doi:10.3390/bioengineering11040337 · Cited by 1,141

The emergence of AI-based wearable sensors for digital health technology: a review

Shajari, S., Kuruvinashetti, K., Komeili, A., & Sundararaj, U. (2023) · Sensors · doi:10.3390/s23239498 · Cited by 535

An introduction to machine learning for classification and prediction

Black, J. E., Kueper, J. K., & Williamson, T. S. (2022) · Family Practice · doi:10.1093/fampra/cmac104 · Cited by 108

Advancements in Wearable and Implantable BioMEMS Devices: Transforming Healthcare Through Technology

Abhinav, V., Basu, P., Verma, S. S., Verma, J., Das, A., Kumari, S., Yadav, P. R., & Kumar, V. (2025) · Micromachines · doi:10.3390/mi16050522 · Cited by 106

Artificial intelligence for medicine: Progress, challenges, and perspectives

Huang, T., Xu, H., Wang, H., Huang, H., Xu, Y., Li, B., Hong, S., Feng, G., Kui, S., Liu, G., et al. (2023) · The Innovation Medicine · doi:10.59717/j.xinn-med.2023.100030 · Cited by 77

Application of artificial intelligence in sleep medicine

chen, Q., & Hu, M. (2023) · International Conference on Artificial Intelligence and Computer Engineering (ICAICE) · doi:10.1117/12.2671764 · Cited by 1

Artificial intelligence in sleep medicine

Bandyopadhyay, A., & Goldstein, C. A. (2023) · Elsevier eBooks · doi:10.1016/b978-0-443-15688-5.00001-2 · Cited by 3

Artificial Intelligence in Sleep Medicine: A New Epoch Dawns

Ahmed, Q. A. A., & Capasso, R. (2023) · Springer eBooks · doi:10.1007/978-3-031-35225-6_34

Transforming sleep medicine: the evolving role of artificial intelligence

Banjade, P., Sharma, M., & Surani, S. (2025) · Current Opinion in Pulmonary Medicine · doi:10.1097/mcp.0000000000001210 · Cited by 2

Strengths, weaknesses, opportunities, and threats of using AI-enabled technology in sleep medicine: a commentary

Bandyopadhyay, A., Oks, M., Sun, H., Prasad, B., Rusk, S., Jefferson, F., Malkani, R. G., Haghayegh, S., Sachdeva, R. C., Hwang, D., et al. (2024) · Journal of Clinical Sleep Medicine · doi:10.5664/jcsm.11132 · Cited by 32