[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-126674-en":3,"doc-seo-126674-105":30,"detail-sidebar-cat-0-en-105":91},{"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":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},126674,962084925502,"Lucas Martin","https://ap-avatar.wpscdn.com/davatar_6f874abed73319feea01a86fa6f0fab8",8,"Research & Report","A machine learning approach to microwave sensing for non-invasive Alzheimer’s disease early detection - Conference paper","Early Alzheimer’s disease detection benefits patients, caregivers, and clinicians, yet common diagnostic routes remain invasive, costly, and difficult to deploy in portable settings. Microwave sensing offers a non-invasive alternative by exploiting permittivity variations in cerebrospinal fluid associated with Alzheimer’s pathology. The work applies machine learning to microwave sensing data using a multilayer perceptron classifier. Multiple network architectures are evaluated on experimental measurements from controlled multi-tissue head phantom tests representing different disease stages, with initial results supporting early-stage diagnostic potential.","POLITECNICO DI TORINO  \nRepository ISTITUZIONALE  \nA machine learning approach to microwave sensing for non-invasive alzheimer’s disease early detection  \nOriginal  \nA machine learning approach to microwave sensing for non-invasive alzheimer’s disease early detection / Cardinali, Leonardo; Spano, Mattia; Gugliermino, Martina; Rodriguez-Duarte, David Orlando; Ricci, Marco; Tobon Vasquez, Jorge Alberto; Palmeri, Roberta; Scapaticci, Rosa; Crocco, Lorenzo; Vipiana, Francesca. -ELETTRONICO. - (2023) .(Intervento presentato al convegno Metrology for eXtended Reality, Artificial Intelligence and Neural Engineering tenutosi a Milano, Italy nel 25-27 ottobre 2023) [10 . 1109/MetroXRAINE58569 .2023. 10405555] .  \nAvailability:  \nThis version is available at: 11583/2982792 since: 2023-10-06T11:02:19Z  \nPublisher: IEEE  \nPublished  \nDOI:10.1109/MetroXRAINE58569.2023.10405555  \nTerms of use:  \nThis article is made available under terms and conditions as specified in the corresponding bibliographic description in the repository  \nPublisher copyright  \nIEEE postprint/Author's Accepted Manuscript  \n©2023 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collecting works, for resale or lists, or reuse of any copyrighted component of this work in other works.  \n(Article begins on next page)  \n18 September 2024  \nA Machine Learning Approach to Microwave Sensing for Non-invasive Alzheimer’s Disease Early Detection  \nLeonardo Cardinali Dept. of electronics and telecommunications Politecnico di Torino Torino, Italy  \nleonardo.cardinali@polito.it  \nMarco Ricci Dept. of electronics and telecommunications Politecnico di Torino Torino, Italy  \nmarco.ricci@polito.it  \nLorenzo Crocco National Research Council of Italy  \nInstitute for the Electromagnetic Sensing of the Environment Napoli, Italy  \n[crocco.l@irea.cnr.it](crocco.l@irea.cnr.it)  \nMattia Spano Dept. of electronics and telecommunications Politecnico di Torino Torino, Italy  \ns292254@studenti.polito.it  \nJorge A. Tobon Vasquez Dept. of electronics and telecommunications Politecnico di Torino Torino, Italy  \njorge.tobonvasquez@polito.it  \nFrancesca Vipiana Dept. of electronics and telecommunications Politecnico di Torino Torino, Italy  \nfrancesca.vipiana@polito.it  \nMartina Gugliermino Dept. of electronics and telecommunications Politecnico di Torino Torino, Italy  \nmartina.gugliermino@polito.it  \nRoberta Palmeri National Research Council of Italy  \nInstitute for the Electromagnetic Sensing of the Environment Napoli, Italy  \n[palmeri.r@irea.cnr.it](palmeri.r@irea.cnr.it)  \nDavid O. Rodriguez-Duarte Dept. of electronics and telecommunications Politecnico di Torino Torino, Italy  \ndavid.rodriguez@polito.it  \nRosa Scapaticci National Research Council of Italy  \nInstitute for the Electromagnetic Sensing of the Environment Napoli, Italy  \n[scapaticci.r@irea.cnr.it](scapaticci.r@irea.cnr.it)  \nAbstract— Early Alzheimer’s disease detection can greatly benefit patients, caregivers, and clinicians. Unfortunately, current diagnostic procedures are invasive, expensive, and not easily portable. To overcome these limitations, microwave sensing is emerging as an alternative non-invasive approach to distinguish between healthy and pathological conditions, based on the variation of permittivity in cerebrospinal fluid in Alzheimer’s patients. In this framework, our paper explores the use of machine learning applied to microwave sensing data, by means of a multilayer perceptron classifier. Different architectures have been considered and appraised by relying on experimental data collected with controlled experiments involving a multi-tissue head phantom that can be filled with tissue-mimicking liquids simulating different stages of the pathology. The initial results confirm the potential of the proposed non-invasive approach","cbCaimIjXVnzn3Fc","https://ap.wps.com/l/cbCaimIjXVnzn3Fc","pdf",1453673,1,7,"English","en",105,"# Abstract\n# Introduction\n## Alzheimer’s Disease","[{\"question\":\"Why is non-invasive early detection of Alzheimer’s disease important?\",\"answer\":\"Early detection is essential for slowing disease progression and enabling better decision-making for care and planning. It also improves support for patients and caregivers.\"},{\"question\":\"What makes microwave sensing suitable for distinguishing healthy and pathological conditions?\",\"answer\":\"Microwave sensing leverages changes in permittivity in cerebrospinal fluid linked to Alzheimer’s patients, enabling separation between healthy and pathological conditions.\"},{\"question\":\"How is machine learning used in the proposed approach?\",\"answer\":\"The method applies machine learning to microwave sensing data using a multilayer perceptron classifier. Different network architectures are tested and compared using experimental data.\"}]","A machine learning approach to microwave sensing for non-invasive Alzheimer’s disease early detection - Conference paper | PDF",1785934171,18,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"a-machine-learning-approach-to-microwave-sensing-for-non-invasive-alzheimers-disease-early-detection-conference-paper","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/a-machine-learning-approach-to-microwave-sensing-for-non-invasive-alzheimers-disease-early-detection-conference-paper/126674/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-05",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"Why is non-invasive early detection of Alzheimer’s disease important?","Question",{"text":75,"@type":76},"Early detection is essential for slowing disease progression and enabling better decision-making for care and planning. 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