Review of Deep Learning Methods for Automated Sleep Staging

In order to diagnose sleep problems, it is critical to correctly identify sleep stages which is a labor-intensive task. Due to rising data volumes, advanced algorithms, and improvements in computational power and storage, artificial intelligence has been more popular in recent years. Automated sleep staging through cardiac rhythm is one of the active research areas that has gained attention over the last decade. In this study, we review four recent state-of-the-art deep learning methods for automated sleep staging, datasets developed in recent years, and discuss their performance evaluations.

[1]  James H. Jones,et al.  Offensive Language Detection on Social Media Based on Text Classification , 2022, 2022 IEEE 12th Annual Computing and Communication Workshop and Conference (CCWC).

[2]  Mina Esmail Zadeh Nojoo Kambar,et al.  A Survey on Mobile Malware Detection Methods using Machine Learning , 2022, 2022 IEEE 12th Annual Computing and Communication Workshop and Conference (CCWC).

[3]  Maryam Heidari,et al.  Efficient Large Scale NLP Feature Engineering with Apache Spark , 2022, 2022 IEEE 12th Annual Computing and Communication Workshop and Conference (CCWC).

[4]  Maryam Heidari,et al.  MapReduce Preprocess of Big Graphs for Rapid Connected Components Detection , 2022, 2022 IEEE 12th Annual Computing and Communication Workshop and Conference (CCWC).

[5]  Vadim Sokolov,et al.  Bayesian Learning: A Selective Overview , 2021, 2112.12722.

[6]  James H. Jones,et al.  Review of Graph Neural Network in Text Classification , 2021, 2021 IEEE 12th Annual Ubiquitous Computing, Electronics & Mobile Communication Conference (UEMCON).

[7]  James H. Jones,et al.  BERT Model for Fake News Detection Based on Social Bot Activities in the COVID-19 Pandemic , 2021, 2021 IEEE 12th Annual Ubiquitous Computing, Electronics & Mobile Communication Conference (UEMCON).

[8]  M. Bitaraf,et al.  Output-only entropy-based damage detection using transmissibility function , 2021, Journal of Civil Structural Health Monitoring.

[9]  Saba Heidari Gheshlaghi,et al.  Breast Cancer Histopathological Image Classification with Adversarial Image Synthesis , 2021, 2021 43rd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC).

[10]  Maryam Heidari,et al.  A Survey of Deep Learning Methods on Semantic Similarity and Sentence Modeling , 2021, 2021 IEEE 12th Annual Information Technology, Electronics and Mobile Communication Conference (IEMCON).

[11]  James H. Jones,et al.  An Empirical Study of the GraphSAGE and Word2vec Algorithms for Graph Multiclass Classification , 2021, 2021 IEEE 12th Annual Information Technology, Electronics and Mobile Communication Conference (IEMCON).

[12]  J. Dutta,et al.  PET Image Denoising Using Unsupervised Domain Translation , 2021, 2021 IEEE Nuclear Science Symposium and Medical Imaging Conference (NSS/MIC).

[13]  Mohammad Amin Hariri-Ardebili,et al.  An Empirical Evaluation of the t-SNE Algorithm for Data Visualization in Structural Engineering , 2021, 2021 20th IEEE International Conference on Machine Learning and Applications (ICMLA).

[14]  Ronald M. Aarts,et al.  A deep transfer learning approach for wearable sleep stage classification with photoplethysmography , 2021, npj Digital Medicine.

[15]  Parth Nagarkar,et al.  A Survey on Machine Learning Techniques for Auto Labeling of Video, Audio, and Text Data , 2021, ArXiv.

[16]  Kazem Taghva,et al.  Text Classification Using Neural Network Language Model (NNLM) and BERT: An Empirical Comparison , 2021, IntelliSys.

[17]  Nasser M. Nasrabadi,et al.  Deep GAN-Based Cross-Spectral Cross-Resolution Iris Recognition , 2021, IEEE Transactions on Biometrics, Behavior, and Identity Science.

[18]  Joyita Dutta,et al.  Artificial Intelligence-Based Image Enhancement in PET Imaging: Noise Reduction and Resolution Enhancement. , 2021, PET clinics.

[19]  A. Tzallas,et al.  Continuous Non-Invasive Glucose Monitoring via Contact Lenses: Current Approaches and Future Perspectives , 2021, Biosensors.

[20]  Maarten De Vos,et al.  SleepTransformer: Automatic Sleep Staging With Interpretability and Uncertainty Quantification , 2021, IEEE Transactions on Biomedical Engineering.

[21]  James H. Jones,et al.  Emotion Detection of Textual Data: An Interdisciplinary Survey , 2021, 2021 IEEE World AI IoT Congress (AIIoT).

[22]  James H. Jones,et al.  A Survey on Concept-Level Sentiment Analysis Techniques of Textual Data , 2021, 2021 IEEE World AI IoT Congress (AIIoT).

[23]  Maryam Heidari,et al.  Ontology Creation Model based on Attention Mechanism for a Specific Business Domain , 2021, 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS).

[24]  James H. Jones,et al.  An Empirical Study of Machine learning Algorithms for Social Media Bot Detection , 2021, 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS).

[25]  Maryam Heidari,et al.  Ensemble of Supervised and Unsupervised Learning Models to Predict a Profitable Business Decision , 2021, 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS).

[26]  A. Wu,et al.  Development and Preliminary Validation of the Patient-Reported Outcome Measure for Vascular Malformation Questionnaire: A Prospective Cohort Study. , 2021, Journal of vascular and interventional radiology : JVIR.

[27]  Pantea Kiaei,et al.  SN4KE: Practical Mutation Testing at Binary Level , 2021, ArXiv.

[28]  Stephanie Forrest,et al.  MIMOSA: Reducing Malware Analysis Overhead with Coverings , 2021, ArXiv.

[29]  Nasser M. Nasrabadi,et al.  OCT Image Segmentation Using Neural Architecture Search and SRGAN , 2021, 2020 25th International Conference on Pattern Recognition (ICPR).

[30]  H. Amindavar,et al.  Deep Bayesian Active Learning, A Brief Survey on Recent Advances , 2020, ArXiv.

[31]  Maryam Heidari,et al.  Bidirectional Transformer based on online Text-based information to Implement Convolutional Neural Network Model For Secure Business Investment , 2020, 2020 IEEE International Symposium on Technology and Society (ISTAS).

[32]  James H. Jones,et al.  Deep Contextualized Word Embedding for Text-based Online User Profiling to Detect Social Bots on Twitter , 2020, 2020 International Conference on Data Mining Workshops (ICDMW).

[33]  Setareh Rafatirad,et al.  Using Transfer Learning Approach to Implement Convolutional Neural Network model to Recommend Airline Tickets by Using Online Reviews , 2020, 2020 15th International Workshop on Semantic and Social Media Adaptation and Personalization (SMA.

[34]  James H. Jones,et al.  Using BERT to Extract Topic-Independent Sentiment Features for Social Media Bot Detection , 2020, 2020 11th IEEE Annual Ubiquitous Computing, Electronics & Mobile Communication Conference (UEMCON).

[35]  Ali H. Shoeb,et al.  Deep learning for automated sleep staging using instantaneous heart rate , 2020, npj Digital Medicine.

[36]  Omid Dehzangi,et al.  Efficient Oct Image Segmentation Using Neural Architecture Search , 2020, 2020 IEEE International Conference on Image Processing (ICIP).

[37]  Akara Supratak,et al.  TinySleepNet: An Efficient Deep Learning Model for Sleep Stage Scoring based on Raw Single-Channel EEG , 2020, 2020 42nd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC).

[38]  Jonathan Montaño,et al.  Improving Locality Sensitive Hashing by Efficiently Finding Projected Nearest Neighbors , 2020, SISAP.

[39]  Y. Lin,et al.  Learning embeddings for multiplex networks using triplet loss , 2019, Applied Network Science.

[40]  Donald A. Adjeroh,et al.  A New Framework for Spatial Modeling and Synthesis of Genomic Sequences , 2019, 2020 IEEE International Conference on Bioinformatics and Biomedicine (BIBM).

[41]  Mohammad Bagher Menhaj,et al.  A Superpixel Segmentation Based Technique for Multiple Sclerosis Lesion Detection , 2019, 1907.03109.

[42]  Guo-Qiang Zhang,et al.  The National Sleep Research Resource: towards a sleep data commons , 2018, BCB.

[43]  Hassan Naderi,et al.  Connected components of big graphs in fixed MapReduce rounds , 2017, Cluster Computing.

[44]  Ronald M. Aarts,et al.  Validation of Photoplethysmography-Based Sleep Staging Compared With Polysomnography in Healthy Middle-Aged Adults , 2017, Sleep.

[45]  Mohsen Ahmadi,et al.  Preserving privacy in location based mobile coupon systems using anonymous authentication scheme , 2016, 2016 13th International Iranian Society of Cryptology Conference on Information Security and Cryptology (ISCISC).

[46]  Hamed Fazlollahtabar,et al.  A Fuzzy Probabilistic Maximum Technique to Optimize an Unconstrained Utility Based Multi Objective Model , 2015 .

[47]  C. O’Reilly,et al.  Montreal Archive of Sleep Studies: an open‐access resource for instrument benchmarking and exploratory research , 2014, Journal of sleep research.

[48]  S. Quan,et al.  Rules for scoring respiratory events in sleep: update of the 2007 AASM Manual for the Scoring of Sleep and Associated Events. Deliberations of the Sleep Apnea Definitions Task Force of the American Academy of Sleep Medicine. , 2012, Journal of clinical sleep medicine : JCSM : official publication of the American Academy of Sleep Medicine.

[49]  M. McHugh.  Interrater reliability: the kappa statistic , 2012, Biochemia medica.

[50]  P. Anderer,et al.  Sleep classification according to AASM and Rechtschaffen & Kales: effects on sleep scoring parameters. , 2009, Sleep.

[51]  R. Kronmal,et al.  Multi-Ethnic Study of Atherosclerosis: objectives and design. , 2002, American journal of epidemiology.

[52]  A. Varri,et al.  The SIESTA project polygraphic and clinical database , 2001, IEEE Engineering in Medicine and Biology Magazine.

[53]  Aeilko H. Zwinderman,et al.  Analysis of a sleep-dependent neuronal feedback loop: the slow-wave microcontinuity of the EEG , 2000, IEEE Transactions on Biomedical Engineering.

[54]  Jeffrey M. Hausdorff,et al.  Physionet: Components of a New Research Resource for Complex Physiologic Signals". Circu-lation Vol , 2000 .

[55]  J. Samet,et al.  The Sleep Heart Health Study: design, rationale, and methods. , 1997, Sleep.

[56]  Jorge Ramón Fonseca Cacho,et al.  Building Wikipedia N-grams with Apache Spark , 2022, SAI.

[57]  Mohammad Amin Hariri-Ardebili,et al.  Kernel Matrix Approximation on Class-Imbalanced Data With an Application to Scientific Simulation , 2021, IEEE Access.

[58]  Kazem Taghva,et al.  Clinical Text Classification of Alzheimer's Drugs' Mechanism of Action , 2021, ICICT.

[59]  Reza Zafarani,et al.  Noise-Enhanced Unsupervised Link Prediction , 2021, PAKDD.

[60]  Omid Jafari,et al.  SatelliteNER: An Effective Named Entity Recognition Model for the Satellite Domain , 2020, KMIS.

[61]  P. Hajibabaee.  Transit Agencies Performance Assessment and Implications , 2020 .

[62]  Armin Esmaeilzadeh,et al.  A Test Driven Approach to Develop Web-Based Machine Learning Applications , 2017 .

[63]  M. Torkjazi.  Applying a utility based fuzzy probabilistic α-cut method to optimize a constrained multi objective model , 2015 .

[64]  Iraj Mahdavi,et al.  Optimizing an unconstrained multi objective model using a utility based fuzzy probabilistic α-cut method , 2014, J. Intell. Fuzzy Syst..