Multi-Class Sleep Stage Analysis and Adaptive Pattern Recognition

EEG Signal Processing

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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 632 · see citing works

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

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Sleep Stage Classification Using EEG Signal Analysis: A Comprehensive Survey and New Investigation

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Analysis and Classification of Sleep Stages Based on Difference Visibility Graphs From a Single-Channel EEG Signal

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A comparative review on sleep stage classification methods in patients and healthy individuals

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Learning machines and sleeping brains: Automatic sleep stage classification using decision-tree multi-class support vector machines

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Automatic sleep stage recurrent neural classifier using energy features of EEG signals

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Multi-class pattern classification using neural networks

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A Comparative Study on Classification of Sleep Stage Based on EEG Signals Using Feature Selection and Classification Algorithms

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Aging-Related Sleep Changes

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A method to detect sleep apnea based on deep neural network and hidden Markov model using single-lead ECG signal

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An Obstructive Sleep Apnea Detection Approach Using a Discriminative Hidden Markov Model From ECG Signals

Song, C., Liu, K., Zhang, X., Chen, L., & Xian, X. (2015) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2015.2498199 · Cited by 253 · see citing works

Sleep Stage Classification Using Unsupervised Feature Learning

Längkvist, M., Karlsson, L., & Loutfi, A. (2012) · Advances in Artificial Neural Systems · doi:10.1155/2012/107046 · Cited by 248 · see citing works

Automatic sleep stages classification based on iterative filtering of electroencephalogram signals

Sharma, R. K., Pachori, R. B., & Upadhyay, A. B. (2017) · Neural Computing and Applications · doi:10.1007/s00521-017-2919-6 · Cited by 202 · see citing works

Adaptive Processing of Brain Signals

Sanei, S. (2013) · Wiley eBooks · doi:10.1002/9781118622162 · Cited by 189 · see citing works

EEG Sleep Stages Classification Based on Time Domain Features and Structural Graph Similarity

Diykh, M., Li, Y., & Wen, P. P. (2016) · IEEE Transactions on Neural Systems and Rehabilitation Engineering · doi:10.1109/tnsre.2016.2552539 · Cited by 180 · see citing works

Sleep stage classification with ECG and respiratory effort

Fonseca, P. M., Long, X., Radha, M., Haakma, R., Aarts, R. M., & Rolink, J. (2015) · Physiological Measurement · doi:10.1088/0967-3334/36/10/2027 · Cited by 168 · see citing works

Sensitivity Versus Accuracy in Multiclass Problems Using Memetic Pareto Evolutionary Neural Networks

Fernández, J. C. F., Martinez, F. J., Hervás‐Martínez, C., & Gutiérrez, P. A. (2010) · IEEE Transactions on Neural Networks · doi:10.1109/tnn.2010.2041468 · Cited by 161 · see citing works

Estimation of Sleep Stages by an Artificial Neural Network Employing EEG, EMG and EOG

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Multi-class ROC analysis from a multi-objective optimisation perspective

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Automatic Sleep Stages Classification Using EEG Entropy Features and Unsupervised Pattern Analysis Techniques

Rodríguez-Sotelo, J. L., Osorio-Forero, A., Jiménez-Rodríguez, A., Cuesta–Frau, D., Cirugeda, E., & Peluffo-Ordóñez, D. H. (2014) · Entropy · doi:10.3390/e16126573 · Cited by 121 · see citing works

Bayesian classification and analysis of gait disorders using image and depth sensors of Microsoft Kinect

Procházka, A., Vyšata, O., Vališ, M., Ťupa, O., Schätz, M., & Mařı́k, V. (2015) · Digital Signal Processing · doi:10.1016/j.dsp.2015.05.011 · Cited by 112 · see citing works

A transition-constrained discrete hidden Markov model for automatic sleep staging

Pan, S.-T., Kuo, C., Zeng, J.-H., & Liang, S. (2012) · BioMedical Engineering OnLine · doi:10.1186/1475-925x-11-52 · Cited by 86 · see citing works

Classification of Sleep Stages Using Multi-wavelet Time Frequency Entropy and LDA

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Microsoft Kinect Visual and Depth Sensors for Breathing and Heart Rate Analysis

Procházka, A., Schätz, M., Vyšata, O., & Vališ, M. (2016) · Sensors · doi:10.3390/s16070996 · Cited by 85 · see citing works

Approximating the multiclass ROC by pairwise analysis

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Wearable In-Ear Encephalography Sensor for Monitoring Sleep. Preliminary Observations from Nap Studies

Looney, D., Goverdovsky, V., Rosenzweig, I., Morrell, M. J., & Mandic, D. P. (2016) · Annals of the American Thoracic Society · doi:10.1513/annalsats.201605-342bc · Cited by 78 · see citing works

Classification of sleep stages using class-dependent sequential feature selection and artificial neural network

Özşen, S. (2012) · Neural Computing and Applications · doi:10.1007/s00521-012-1065-4 · Cited by 63 · see citing works

An efficient sleep scoring system based on EEG signal using complex-valued machine learning algorithms

Peker, M. (2016) · Neurocomputing · doi:10.1016/j.neucom.2016.04.049 · Cited by 56 · see citing works

Non-invasive analysis of sleep patterns via multimodal sensor input

Metsis, V., Kosmopoulos, D., Athitsos, V., & Makedon, F. S. (2012) · Personal and Ubiquitous Computing · doi:10.1007/s00779-012-0623-1 · Cited by 51 · see citing works

A hierarchical classification system for sleep stage scoring via forehead EEG signals

Huang, C.-S., Lin, C., Ko, L., Liu, S.-Y., Sua, T.-P., & Lin, C. (2013) · IEEE Symposium on Computational Intelligence, Cognitive Algorithms, Mind, and Brain (CCMB) · doi:10.1109/ccmb.2013.6609157 · Cited by 38 · see citing works

Screening for REM sleep behavior disorder in the general population

Bušková, J., Ibarburu, V., Šonka, K., & Růžička, E. (2016) · Sleep Medicine · doi:10.1016/j.sleep.2016.07.003 · Cited by 38 · see citing works

Monitoring of Weekly Sleep Pattern Variations at Home with a Contactless Biomotion Sensor

Hashizaki, M., Nakajima, H., & Kume, K. (2015) · Sensors · doi:10.3390/s150818950 · Cited by 37 · see citing works

Extraction of breathing features using MS Kinect for sleep stage detection

Procházka, A., Schätz, M., Centonze, F., Kuchyňka, J., Vyšata, O., & Vališ, M. (2016) · Signal Image and Video Processing · doi:10.1007/s11760-016-0897-2 · Cited by 32 · see citing works

Discrimination of axonal neuropathy using sensitivity and specificity statistical measures

Procházka, A., Vyšata, O., Ťupa, O., Yadollahi, M., & Vališ, M. (2014) · Neural Computing and Applications · doi:10.1007/s00521-014-1622-0 · Cited by 26 · see citing works

Age and gender dependency of physiological networks in sleep

Krefting, D., Jansen, C., Penzel, T., Han, F., & Kantelhardt, J. W. (2017) · Physiological Measurement · doi:10.1088/1361-6579/aa614e · Cited by 12 · see citing works

An Adaptive Filtering Approach Using Supervised SSA for Identification of Sleep Stages From EEG

Kouchaki, S., Eftaxias, K., & Sanei, S. (2014) · Nottingham Trent University's Institutional Repository (Nottingham Trent Repository) · Cited by 10 · see citing works

Electroencephalography Analysis Using Neural Network and Support Vector Machine during Sleep

Lee, J., & Yoo, S. K. (2013) · Engineering · doi:10.4236/eng.2013.55b018 · Cited by 9 · see citing works

Automatic Sleep Stages Detection Based on EEG Signals Using Combination of Classifiers

Kianzad, R., & Kordy, H. M. (2013) · DOAJ (DOAJ: Directory of Open Access Journals) · doi:10.22061/jecei.2013.30 · Cited by 8 · see citing works

Age-dependent complex noise fluctuations in the brain

Mareš, J., Vyšata, O., Procházka, A., & Vališ, M. (2013) · Physiological Measurement · doi:10.1088/0967-3334/34/10/1269 · Cited by 7 · see citing works

Characterizing Sleep Stages by the Fractal Dimensions of Electroencephalograms

Chen, J.-Y. (2017) · Biostatistics and Biometrics Open Access Journal · doi:10.19080/bboaj.2017.02.555584 · Cited by 5 · see citing works

A single-trial toolbox for advanced sleep polysomnographic preprocessing

Chaparro-Vargas, R., & Cvetković, D. (2013) · doi:10.1109/embc.2013.6610877 · Cited by 4 · see citing works

Adaptive segmentation of multimodal polysomnography data for sleep stages detection

Procházka, A., Kuchyňka, J., Yadollahi, M., Araujo, C. P. S., & Vyšata, O. (2017) · IEEE International Conference on Digital Signal Processing (DSP) · doi:10.1109/icdsp.2017.8096108 · Cited by 3 · see citing works

Characterizing Sleep Stages by the Fractal Dimensions of Electroencephalograms

Liaw, S.-S., & Chen, J.-Y. (n.d.) · RePEc: Research Papers in Economics · Cited by 3 · see citing works

Sleep Stages Classification Using Neural Network with Single Channel EEG

Venkatesh, K., Poonguzhali, S., Mohanavelu, K., & Adalarasu, K. (2014) · Cited by 1 · see citing works

Review of Wearable Devices and Data Collection Considerations for Connected Health

Vijayan, V., Connolly, J. P., Condell, J., McKelvey, N., & Gardiner, P. (2021) · Sensors · doi:10.3390/s21165589 · Cited by 435 · see citing works

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

Bandyopadhyay, A., & Goldstein, C. (2023) · Sleep And Breathing · doi:10.1007/s11325-022-02592-4 · Cited by 108 · see citing works

Computational Sleep Behavior Analysis: A Survey

Fallmann, S., & Chen, L. (2019) · IEEE Access · doi:10.1109/access.2019.2944801 · Cited by 58 · see citing works

Comparative analysis of different characteristics of automatic sleep stages

Zhao, D., Wang, Y., Wang, Q., & Wang, X. (2019) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2019.04.004 · Cited by 58 · see citing works

Hybrid CNN-LSTM Network for Real-Time Apnea-Hypopnea Event Detection Based on IR-UWB Radar

Kwon, H. B., Son, D., Lee, D., Yoon, H., Lee, M. H., Lee, Y. J., Choi, S. H., & Park, K. S. (2021) · IEEE Access · doi:10.1109/access.2021.3081747 · Cited by 55 · see citing works

Integrating the Role of Computational Intelligence and Digital Signal Processing in Education: Emerging Technologies and Mathematical Tools

Procházka, A., Vyšata, O., & Mařı́k, V. (2021) · IEEE Signal Processing Magazine · doi:10.1109/msp.2021.3058634 · Cited by 50 · see citing works

Microneedle Array Electrode-Based Wearable EMG System for Detection of Driver Drowsiness through Steering Wheel Grip

Satti, A. T., Kim, J., Yi, E.-S., Cho, H.-Y., & Cho, S. (2021) · Sensors · doi:10.3390/s21155091 · Cited by 46 · see citing works

Recognition of Patient Groups with Sleep Related Disorders using Bio-signal Processing and Deep Learning

Jarchi, D., Andreu-Pérez, J., Kiani, M., Vyšata, O., Kuchyňka, J., Procházka, A., & Sanei, S. (2020) · Sensors · doi:10.3390/s20092594 · Cited by 44 · see citing works

Deep Learning for Accelerometric Data Assessment and Ataxic Gait Monitoring

Procházka, A., Dostál, O., Cejnar, P., Mohamed, H. I., Pavelek, Z., Vališ, M., & Vyšata, O. (2021) · IEEE Transactions on Neural Systems and Rehabilitation Engineering · doi:10.1109/tnsre.2021.3051093 · Cited by 36 · see citing works

Deep learning approach for ECG-based automatic sleep state classification in preterm infants

Werth, J., Radha, M., Andriessen, P., Aarts, R. M., & Long, X. (2019) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2019.101663 · Cited by 35 · see citing works

Automatic sleep scoring: A deep learning architecture for multi-modality time series

Yan, R., Li, F., Zhou, D., Ristaniemi, T., & Cong, F. (2020) · Journal of Neuroscience Methods · doi:10.1016/j.jneumeth.2020.108971 · Cited by 34 · see citing works

A systematic review on sleep stage classification and sleep disorder detection using artificial intelligence

Wara, T. U., Fahad, A. H., Das, A. S., & Shawon, M. M. H. (2025) · Heliyon · doi:10.1016/j.heliyon.2025.e43576 · Cited by 27 · see citing works

A Compressive Sensing-Based Automatic Sleep-Stage Classification System With Radial Basis Function Neural Network

Lee, H., Choi, J., Kim, S., Jun, S. C., & Lee, B. (2019) · IEEE Access · doi:10.1109/access.2019.2961326 · Cited by 26 · see citing works

Photoplethysmographic-based automated sleep–wake classification using a support vector machine

Motin, M. A., Karmakar, C., Palaniswami, M. S., & Penzel, T. (2020) · Physiological Measurement · doi:10.1088/1361-6579/ab9482 · Cited by 26 · see citing works

A novel approach to diagnose sleep apnea using enhanced frequency extraction network

Wu, Y., Pang, X., Zhao, G., Yue, H., Lei, W., & Wang, Y. (2021) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2021.106119 · Cited by 23 · see citing works

Image classification method on class imbalance datasets using multi-scale CNN and two-stage transfer learning

Liu, J., Guo, F., Gao, H., Huang, Z., Zhang, Y., & Zhou, H. (2021) · Neural Computing and Applications · doi:10.1007/s00521-021-06066-8 · Cited by 21 · see citing works

Sleep Arousal Detection From Polysomnography Using the Scattering Transform and Recurrent Neural Networks

Warrick, P. A., & Homsi, M. N. (2018) · Computing in cardiology · doi:10.22489/cinc.2018.368 · Cited by 19 · see citing works

Classification of brain activities during language and music perception

Besedová, P., Vyšata, O., Mazurová, R., Kopál, J., Ondráková, J., Vališ, M., & Procházka, A. (2019) · Signal Image and Video Processing · doi:10.1007/s11760-019-01505-5 · Cited by 13 · see citing works

A novel solution of enhanced loss function using deep learning in sleep stage classification: predict and diagnose patients with sleep disorders

Rajbhandari, E., Alsadoon, A., Prasad, P. W. C., Seher, I., Nguyen, T. Q. V., & Pham, D. T. H. (2021) · Multimedia Tools and Applications · doi:10.1007/s11042-020-10199-8 · Cited by 13 · see citing works

Detection of Sleep Apnea/hypopnea Events Using Synchrosqueezed Wavelet Transform

Jarchi, D., Sanei, S., & Procházka, A. (2019) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.2019.8682893 · Cited by 12 · see citing works

Discrimination of cycling patterns using accelerometric data and deep learning techniques

Procházka, A., Charvátová, H., Vyšata, O., Jarchi, D., & Sanei, S. (2020) · Neural Computing and Applications · doi:10.1007/s00521-020-05504-3 · Cited by 12 · see citing works

Incremental deep learning for reflectivity data recognition in stomatology

Procházka, A., Charvát, J., Vyšata, O., & Mandic, D. P. (2022) · Neural Computing and Applications · doi:10.1007/s00521-021-06842-6 · Cited by 6 · see citing works

Automated sleep staging using sequential XGBoost and multi-scale temporal fusion

Kuchyňka, J., & Vyšata, O. (2025) · Discover Artificial Intelligence · doi:10.1007/s44163-025-00356-z · Cited by 3 · see citing works

The Effect of Face Masks on Physiological Data and the Classification of Rehabilitation Walking

Procházka, A., Charvátová, H., & Vyšata, O. (2022) · IEEE Transactions on Neural Systems and Rehabilitation Engineering · doi:10.1109/tnsre.2022.3201487 · Cited by 3 · see citing works

Infrared Thermography and Computational Intelligence in Analysis of Facial Video-Records

Procházka, A., Charvátová, H., & Vyšata, O. (2021) · Communications in computer and information science · doi:10.1007/978-3-030-88113-9_51 · Cited by 2 · see citing works

ROM-based Inference Method Built on Deep Learning for Sleep Stage Classification

Al-Meer, M. H., Hassen, H., & Nawaz, N. (2019) · Journal of Engineering and Applied Sciences · doi:10.36478/jeasci.2019.5906.5916 · Cited by 2 · see citing works

Potential Use of Personal Tracking Device for Sleep Quality Assessment of Flight Attendant

Imsombut, A., Jirapanthong, W., Poolsawas, B., Laovoravit, V., & Pongpirul, K. (2020) · International Conference on Frontiers of Educational Technologies (ICFET) · doi:10.1145/3404709.3404772 · Cited by 1 · see citing works

A New Spectrum Driven Index for the Assessment of ECG Signal Quality

Jarchi, D., Procházka, A., & Sanei, S. (2020) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc44109.2020.9175573 · Cited by 1 · see citing works

Digital Fiat Currency (DFC): A Taxonomy for Automatic Sleep Stage Classification

Kaur, A., Alsadoon, O. H., & Aloussi, S. A. (2020) · International Conference on Innovative Technologies in Intelligent Systems and Industrial Applications (CITISIA) · doi:10.1109/citisia50690.2020.9371800 · Cited by 1 · see citing works

ROM-Based Deep Learning Inference for Sleep Stage Classification

Al-Meer, M. H., Hassen, H., & Nawaz, N. (2019) · Advances in intelligent systems and computing · doi:10.1007/978-3-030-29516-5_66

Comparative Study of Deep Learning Based Sleep Scoring Methods

Procházka, A., Vyšata, O., Kuchynka, J., Jarchi, D., & Sanei, S. (2020) · International Conference on Emerging Trends in Electrical, Electronic and Communications Engineering (ELECOM) · doi:10.1109/elecom49001.2020.9296996

Diffuse Reflectance Spectroscopy for Optical Identification of Dental Materials Toward Minimally Invasive Treatment

Charvát, J., & Procházka, A. (2026) · IEEE Sensors Journal · doi:10.1109/jsen.2026.3727480

Adaptive segmentation of multimodal polysomnography data for sleep stages detection

Procházka, A., Kuchyňka, J., Yadollahi, M., Araujo, C. P. S., & Vyšata, O. (2017) · IEEE International Conference on Digital Signal Processing (DSP) · doi:10.1109/icdsp.2017.8096108 · Cited by 3 · see citing works

Sleep scoring using polysomnography data features

Procházka, A., Kuchyňka, J., Vyšata, O., Schätz, M., Yadollahi, M., Sanei, S., & Vališ, M. (2018) · Signal Image and Video Processing · doi:10.1007/s11760-018-1252-6 · Cited by 14 · see citing works

A multi-modal assessment of sleep stages using adaptive Fourier decomposition and machine learning

Fatimah, B., Singhal, A., & Singh, P. (2022) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2022.105877 · Cited by 28 · see citing works

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 632 · see citing works

Multimodal Polysomnography-Based Automatic Sleep Stage Classification via Multiview Fusion Network

Lin, Y., Wang, M., Hu, F., Cheng, X., & Xu, J. (2023) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2023.3343781 · Cited by 20 · see citing works