[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-127919-en":3,"doc-seo-127919-105":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},127919,137451207643,"Noah","https://ap-avatar.wpscdn.com/davatar_3d24733baf745e90a7e4bdd5f77d97b2",8,"Research & Report","An IoT and Machine Learning-based Neonatal Sleep Stage Classification - Research paper overview","Neonatal sleep quality serves as an indicator of brain and physical development, making accurate sleep-stage assessment essential for early evaluation of developmental deficiencies. Polysomnography (PSG) is the established gold standard but is costly, time-intensive, and dependent on expert neurologists. To reduce this burden, an IoT-oriented approach combined with machine learning is proposed to classify neonatal sleep-wake states. The pipeline preprocesses EEG using finite impulse response filtering, segments signals into 30-second windows, extracts time/frequency/spatial features, and applies a support vector machine. Four-fold cross validation and metrics including sensitivity, specificity, and Kappa yield 83.7% mean accuracy, supporting real-time applicability without prior post-processing.","| NUML International Journal of Engineering and Computing\u003Cbr>Volume: 2 Issue: 2\u003Cbr> | [https://numl.edu.pk/journals/nijec](https://numl.edu.pk/journals/nijec)\u003Cbr>Print ISSN: 2788-9629\u003Cbr>E-ISSN: 2791-3465\u003Cbr>DOI:[https://doi.org/10.52015/nijec.v2i2.21](https://doi.org/10.52015/nijec.v2i2.21) |\n| --- | --- |\n\nAn IoT and Machine Learning-based Neonatal Sleep Stage Classification  \nAwais Abbasa, 􀀍 , Hafiz Sheraz Sheikha , Haris Ahmadb, Saadullah Farooq Abbasic  \n5 a Department of Electrical and Computer Engineering ([awaisabbas212@gmail.com](awaisabbas212@gmail.com))  \na Department of Electrical and Computer Engineering ([hafizsherazsheikh714@gmail.com](hafizsherazsheikh714@gmail.com))  \nb Department of Electrical and Computer Engineering ([harisahmad248@outlook.com](harisahmad248@outlook.com))  \n􀀍 Corresponding Author: Saadullah Farooq Abbasi ([Saadullahfarooq93@gmail.com](Saadullahfarooq93@gmail.com))  \n\n| Submitted | Revised | Published |\n| --- | --- | --- |\n| 04-Jan-2023 | 01-Dec-2023 | 21-Feb-2024 |\n\n10 Abstract  \nSleep, in neonates, is used to access the quality of brain and physical development. It is an important hallmark to access the deficiencies in the development of brain. Typically, neonatal sleep has been divided into three stages: active sleep (AS), quiet sleep (QS) and intermediate sleep (IS) . Traditionally, neurologists are hired to classify neonatal and adult sleep. Polysomnography (PSG) is considered as a gold standard to classify sleep.  \n15 However, PSG consumes tons of time and money. To address this issue, over the past two decades, researchers proposed multiple algorithms for automatic sleep stage classification. These algorithms work achieved outstanding results for some cases i.e. quiet sleep detection still, lacks in many aspects. One major drawback of the existing research is the amalgamation of awake and active sleep into low voltage irregular (LVI) state. This amalgamation corrupts 40% of the overall EEG signal. For this reason, we proposed an algorithm for neonatal  \n20 sleep-wake classification using machine learning. The proposed research is divided into three steps. Firstly, EEG signal was pre-processed using finite impulse response filter to remove the noise and artifacts. Clean EEG signal is then divided into 4560 30-sec segments. Then, twenty prominent EEG features were extracted from time, frequency and spatial domain. After feature extraction, support vector machine was used for sleep stage classification. The propounded study outperformed all the existing algorithms for sleep-wake classification with  \n25 a mean accuracy of 83.7% . Four-fold cross validation was used to validate the overall dataset. Multiple other performance metrices i.e. sensitivity, specificity, Kappa were calculated to prove the efficacy of the proposed study. Statistical results show that the proposed study can be used as a real-time neonatal sleep and Awake classification algorithms, as this did not use prior post-processing technique.  \nKeywords—Neonatal Sleep, Classification, Electroencephalography, Polysomnography, Machine Learning  \n30 1. Introduction  \nSleep is categorized as an arrangement of modifications occurring in our body inside our brain, and muscles, working its way through our eyes (occipital lobe), and respiratory along with cardiac activity. Both mental and physical health are somehow dependent on the dynamic and defined course of sleep activity. The major function of sleep is to protect the metabolized energy which is a cause of nurturing neural acquaintances and associating  \n35 learning with memory. In neonates, it is responsible for mental and physical development. To monitor neonatal sleep for the purpose of attaining a complete understanding of normal neurological productivity, intensive monitoring of the neonates is accompanied by bedside neuro-monitoring. For this reason, Polysomnography (PSG) is used as a gold standard. In PSG, multiple bio-physiological signals are extracted using electrode","cbCainhiQkmnqGmp","https://ap.wps.com/l/cbCainhiQkmnqGmp","pdf",1015585,4,1,11,"English","en",105,"# Abstract\n# 1. Introduction","[{\"question\":\"Why is neonatal sleep stage classification important?\",\"answer\":\"Neonatal sleep reflects brain and physical development, helping identify deficiencies in neurological progression. Accurate classification supports understanding normal developmental trajectories.\"},{\"question\":\"What makes polysomnography (PSG) challenging in practice?\",\"answer\":\"PSG is expensive and time-consuming, and it requires professional neurologists for manual sleep staging. These factors motivate automated alternatives.\"},{\"question\":\"How does the proposed method classify neonatal sleep stages?\",\"answer\":\"The method filters EEG with a finite impulse response approach, segments cleaned EEG into 30-second windows, extracts features from time, frequency, and spatial domains, and uses a support vector machine for classification. Four-fold cross validation evaluates performance using sensitivity, specificity, and Kappa.\"}]","An IoT and Machine Learning-based Neonatal Sleep Stage Classification - Research paper overview | PDF",1785942941,28,{"code":4,"msg":32,"data":33},"ok",{"site_id":25,"language":24,"slug":34,"title":13,"keywords":35,"description":14,"schema_data":36,"social_meta":87,"head_meta":89,"extra_data":91,"updated_unix":29},"an-iot-and-machine-learning-based-neonatal-sleep-stage-classification-research-paper-overview","",{"@graph":37,"@context":86},[38,54,69],{"@type":39,"itemListElement":40},"BreadcrumbList",[41,45,49,52],{"item":42,"name":43,"@type":44,"position":21},"https://docshare.wps.com","Home","ListItem",{"item":46,"name":47,"@type":44,"position":48},"https://docshare.wps.com/document/","Document",2,{"item":50,"name":12,"@type":44,"position":51},"https://docshare.wps.com/document/research-report/",3,{"item":53,"name":13,"@type":44,"position":20},"https://docshare.wps.com/document/an-iot-and-machine-learning-based-neonatal-sleep-stage-classification-research-paper-overview/127919/",{"url":53,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":24,"description":14,"dateModified":62,"datePublished":63,"encodingFormat":61,"isAccessibleForFree":64,"interactionStatistic":65},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":42,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-28","2026-08-05",true,{"@type":66,"interactionType":67,"userInteractionCount":20},"InteractionCounter",{"@type":68},"ViewAction",{"@type":70,"mainEntity":71},"FAQPage",[72,78,82],{"name":73,"@type":74,"acceptedAnswer":75},"Why is neonatal sleep stage classification important?","Question",{"text":76,"@type":77},"Neonatal sleep reflects brain and physical development, helping identify deficiencies in neurological progression. Accurate classification supports understanding normal developmental trajectories.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"What makes polysomnography (PSG) challenging in practice?",{"text":81,"@type":77},"PSG is expensive and time-consuming, and it requires professional neurologists for manual sleep staging. These factors motivate automated alternatives.",{"name":83,"@type":74,"acceptedAnswer":84},"How does the proposed method classify neonatal sleep stages?",{"text":85,"@type":77},"The method filters EEG with a finite impulse response approach, segments cleaned EEG into 30-second windows, extracts features from time, frequency, and spatial domains, and uses a support vector machine for classification. Four-fold cross validation evaluates performance using sensitivity, specificity, and Kappa.","https://schema.org",{"og:url":53,"og:type":88,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":90,"canonical":53},"index,follow",{"doc_id":7,"site_id":25},{"code":4,"msg":5,"data":93},[94,98,102,106,111,116,121,124,129,132,136],{"id":21,"doc_module":4,"doc_module_name":47,"category_name":95,"show_sort_weight":96,"slug":97},"Story & Novel",90,"story-novel",{"id":48,"doc_module":4,"doc_module_name":47,"category_name":99,"show_sort_weight":100,"slug":101},"Literature",80,"literature",{"id":20,"doc_module":4,"doc_module_name":47,"category_name":103,"show_sort_weight":104,"slug":105},"Exam",70,"exam",{"id":107,"doc_module":4,"doc_module_name":47,"category_name":108,"show_sort_weight":109,"slug":110},5,"Comic",60,"comic",{"id":112,"doc_module":4,"doc_module_name":47,"category_name":113,"show_sort_weight":114,"slug":115},6,"Technology",50,"technology",{"id":117,"doc_module":4,"doc_module_name":47,"category_name":118,"show_sort_weight":119,"slug":120},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":47,"category_name":12,"show_sort_weight":122,"slug":123},30,"research-report",{"id":125,"doc_module":4,"doc_module_name":47,"category_name":126,"show_sort_weight":127,"slug":128},9,"Religion & Spirituality",20,"religion-spirituality",{"id":127,"doc_module":4,"doc_module_name":47,"category_name":130,"show_sort_weight":127,"slug":131},"World Cup","world-cup",{"id":133,"doc_module":4,"doc_module_name":47,"category_name":134,"show_sort_weight":133,"slug":135},10,"Lifestyle","lifestyle",{"id":137,"doc_module":4,"doc_module_name":47,"category_name":138,"show_sort_weight":107,"slug":139},19,"General","general"]