[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-128756-en":3,"doc-seo-128756-105":31,"detail-sidebar-cat-0-en-105":92},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":28,"seo_description":14,"update_tm":29,"read_time":30},128756,1099523885074,"Ivy","https://ap-avatar.wpscdn.com/davatar_9964176cb1d06d4a9deccf72a44ae3dc",8,"Research & Report","MMSleepNet: A Μultimodal Model for Automatic Sleep Staging - Diploma Thesis","Automatic sleep stage classification is crucial for assessing sleep quality and diagnosing sleep disorders. While numerous approaches have been developed, many rely on single-channel electroencephalogram (EEG) signals or use inputs from a single domain. Polysomnography (PSG) enables more comprehensive modeling by capturing multiple signal channels and supporting integration of time- and frequency-domain information. The thesis presents MMSleepNet, a deep learning model that extracts and fuses multi-channel PSG features, and evaluates self-supervised learning methods to mitigate label scarcity and improve performance.","NATIONAL TECHNICAL UNIVERSITY OF ATHENS SCHOOL OF ELECTRICAL AND COMPUTER ENGINEERING DIVISION OF INDUSTRIAL ELECTRIC DEVICES AND DECISION SYSTEMS  \nMMSleepNet: A Μultimodal Model for Automatic  \nSleep Staging  \nDIPLOMA THESIS  \nDimitrios K. Xynogalas  \nSupervisor: Dimitrios Askounis  \nProfessor NTUA  \nAthens, February 2024  \nNATIONAL TECHNICAL UNIVERSITY OF ATHENS SCHOOL OF ELECTRICAL AND COMPUTER ENGINEERING DIVISION OF INDUSTRIAL ELECTRIC DEVICES AND DECISION SYSTEMS  \nMMSleepNet: A Μultimodal Model for Automatic  \nSleep Staging  \nDIPLOMA THESIS  \nDimitrios K. Xynogalas  \nSupervisor: Dimitrios Askounis  \nProfessor NTUA  \nApproved by the examination committee on 29 February 2024  \nIoannis Psarras Professor NTUA  \nDimitrios Askounis Professor NTUA  \nVangelis Marinakis  \nAssistant Professor NTUA  \nAthens, February 2024  \nNATIONAL TECHNICAL UNIVERSITY OF ATHENS SCHOOL OF ELECTRICAL AND COMPUTER ENGINEERING DIVISION OF INDUSTRIAL ELECTRIC DEVICES AND DECISION SYSTEMS  \nDimitrios Xynogalas  \nElectrical and Computer Engineer, NTUA  \nCopyright © – Dimitrios Xynogalas, 2024. All rights reserved. This work is copyright and may not be reproduced, stored nor distributed in whole or in part for commercial purposes. Permission is hereby granted to reproduce, store, and distribute this work for non-profit, educational and research purposes, provided that the source is acknowledged, and the present copyright message is retained. Enquiries regarding use for profit should be directed to the author. The views and conclusions contained in this document are those of the author and should not be interpreted as representing the official policies, either expressed or implied, of the National Technical University of Athens.  \nΠερίληψη  \nΗ αυτόματη ταξινόμηση των σταδίων ύπνου είναι ζωτικής σημασίας για την αξιολόγηση τηςποιότητας του ύπνου και τη διάγνωση των διαταραχών του ύπνου . Αν και έχουν αναπτυχθείπολυάριθμες προσεγγίσεις για τον σκοπό αυτό, πολλές βασίζονται αποκλειστικά σεμονοκάναλα σήματα ηλεκτροεγκεφαλογραφήματος (EEG) ή εξετάζουν εισροές από έναμόνο πεδίο . Η πολυσωμνογραφία (PSG) προσφέρει μια πιο ολοκληρωμένη λύσηπαρέχοντας πολλαπλά κανάλια καταγραφής σήματος, επιτρέποντας στα μοντέλα ναεξάγουν και να ενσωματώνουν πληροφορίες από διάφορα κανάλια για βελτιωμένηαπόδοση ταξινόμησης . Επιπλέον, πολύτιμες πληροφορίες μπορούν να αντληθούν τόσο απότις αναπαραστάσεις του πεδίου του χρόνου όσο και από τις αναπαραστάσεις στο πεδίο τηςσυχνότητας των σημάτων PSG. Η παρούσα μελέτη εισάγει το MMSleepNet, ένα μοντέλοβαθιάς μάθησης που εξάγει και συγχωνεύει επιδέξια πληροφορίες από πολλαπλά κανάλια PSG τόσο στο πεδίο του χρόνου όσο και στο πεδίο της συχνότητας για να επιτύχει τηναυτόματη κατηγοριοποίηση των σταδίων του ύπνου . Αναγνωρίζοντας τη δυνατότητα τωνεργαστηρίων ύπνου να παράγουν τεράστιες ποσότητες μη επισημασμένων δεδομένων γιατη κατηγοριοποίηση του ύπνου και την εγγενή δυσκολία της επισήμανσης αυτών τωνδεδομένων, η μελέτη διερευνά την αποτελεσματικότητα διαφόρων αλγορίθμωναυτοεπιβλεπόμενης μάθησης για την αντιμετώπιση της έλλειψης ετικετών και τη βελτίωσητης απόδοσης του προτεινόμενου μοντέλου .  \nΛέξεις κλειδιά: Μηχανική Μάθηση, Πολυτροπική, Αυτόματη Κατηγοριοποίηση Ύπνου  \nAbstract  \nAutomatic sleep stage classification is crucial for assessing sleep quality and diagnosing sleep disorders. While numerous approaches have been developed for this purpose, many rely solely on single-channel electroencephalogram (EEG) signals or consider inputs from a single domain. Polysomnography (PSG) offers a more comprehensive solution by providing multiple channels of signal recording, enabling models to extract and integrate information from diverse channels for enhanced classification performance. Furthermore, valuable insights can be derived from both the time and frequency domain representations of PSG signals. This study introduces MMSleepNet, a deep learning model that adeptly extracts and fuses information from multiple PSG channels across both t","cbCaiqTwaPksIM5S","https://ap.wps.com/l/cbCaiqTwaPksIM5S","pdf",2395035,2,1,77,"English","en",105,"# Abstract\n# Περίληψη\n# Keywords / Λέξεις κλειδιά","[{\"question\":\"Why is automatic sleep stage classification important?\",\"answer\":\"Automatic sleep staging supports assessing sleep quality and diagnosing sleep disorders by converting sleep signals into stage labels.\"},{\"question\":\"What role does polysomnography (PSG) play in the proposed approach?\",\"answer\":\"PSG provides multiple recording channels, allowing models to extract and integrate information across channels and from both time- and frequency-domain representations.\"},{\"question\":\"How does MMSleepNet handle label scarcity?\",\"answer\":\"MMSleepNet is evaluated with self-supervised learning algorithms to leverage large volumes of unlabeled sleep data and improve classification performance despite limited labels.\"}]","MMSleepNet: A Μultimodal Model for Automatic Sleep Staging - 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