[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-117577-en":3,"doc-seo-117577-105":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},117577,1374391974468,"Eden","https://ap-avatar.wpscdn.com/davatar_29158cc5080c5b710cf443261637dec0",7,"Healthcare","Music-induced physiological markers for detecting Alzheimer’s disease using machine learning","Alzheimer’s disease is marked by progressive cognitive and emotional decline, creating a demand for novel, non-invasive biomarkers to support early detection, monitoring, and stage-specific interventions. This study examines music-evoked physiological responses recorded via electrodermal activity and facial electromyography while machine learning models classify disease presence and severity. Results show reduced physiological reactivity with progression and emotion-specific patterns, enabling ML to distinguish Alzheimer’s patients from controls and estimate severity.","TYPE Original Research PUBLISHED 24 November 2025 DOI 10. 3389/fnagi.2025.1701970  \nOPEN ACCESS  \nEDITED BY  \nEnzo Emanuele, 2E Science, Italy  \nREVIEWED BY  \nSantosh Kumar Prajapati,  \nUniversity of South Florida, United States Piercarlo Minoretti,  \nStudio Minoretti, Italy  \n*CORRESPONDENCE  \nGonçalo Barradas  \n [g.t.barradas@salford.ac.uk](g.t.barradas@salford.ac.uk)  \nRECEIVED 09 September 2025  \nACCEPTED 27 October 2025  \nPUBLISHED 24 November 2025  \nCITATION  \nLima R, Barradas G and Bermúdez i Badia S (2025) Music-induced physiological markers for detecting Alzheimer’s disease using machine learning.  \nFront. Aging Neurosci. 17:1701970 .  \ndoi: 10.3389/fnagi.2025.1701970  \nCOPYRIGHT  \n© 2025 Lima, Barradas and Bermúdez i Badia. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.  \nMusic-induced physiological markers for detecting Alzheimer’s disease using machine learning  \nRodrigo Lima1,2,3 , Gonçalo Barradas4* and Sergi Bermúdez i Badia1,2,3  \n1 Faculdade de Ciências Exatas e da Engenharia, Universidade da Madeira, Funchal, Portugal, 2Agência Regional para o Desenvolvimento da Investigação, Tecnologia e Inovação, Funchal, Portugal, 3 NOVA Laboratory for Computer Science and Informatics, Universidade Nova de Lisboa, Lisboa, Portugal,  \n4 School of Health and Society, University of Salford, Salford, United Kingdom  \nIntroduction: Alzheimer’s disease (AD) is characterized by progressive cognitive and emotional decline, highlighting the need for novel, non-invasive biomarkers to aid in early detection, monitoring, and stage-specific interventions. This study investigates music-evoked physiological responses as potential biomarkers of AD and evaluates their translational value using machine learning (ML) .  \nMaterials and methods: A total of 36 AD patients, spanning different severity levels, listened to emotionally evocative musical excerpts while electrodermal activity and facial electromyography (corrugator and zygomaticus muscles) were recorded. Machine learning models were then trained on these signals to classify the presence and severity of AD and to detect residual emotion-specific physiological responses elicited by music.  \nResults: Physiological reactivity to music declined with disease progression, with positive emotions eliciting more distinct responses than negative ones. The Random Forest classifier distinguished AD patients from healthy controls with 70.5% accuracy, while the Naïve Bayes model predicted severity with 65.6% accuracy, demonstrating that ML models can detect subtle music-evoked physiological differences even in individuals with AD.  \nDiscussion: Music-evoked physiological signals reflect the hierarchical disruption of emotion-related neural circuits in AD and hold promise as complementary biomarkers for disease presence and stage. When combined with machine learning (ML), these measures provide a non-invasive, ecologically valid approach to support early detection, monitoring, and the development of stage-specific interventions.  \nKEYWORDS  \nAlzheimer’s disease, dementia, electrodermal activity, electromyography, emotional responses, machine learning, music  \n1 Introduction  \nDementia is a progressive neurodegenerative disorder characterized by a gradual decline in cognitive functions, including memory, learning, orientation, language, and judgment. Alzheimer’s disease (AD) accounts for 60%–80% of all dementia cases and often begins long before symptoms become apparent, with progression varying among individuals (Matziorinis and Koelsch, 2022; Ferri et al., 2009) . Although pharmacological treatments can alle","cbCaikidDGrtViUk","https://ap.wps.com/l/cbCaikidDGrtViUk","pdf",2496772,1,16,"English","en",105,"# Introduction\n## Dementia and Alzheimer’s disease\n## Music as a therapeutic and diagnostic tool\n# Materials and methods\n## Participants and recorded signals\n## Machine learning classification\n# Results\n## Emotion-linked physiological reactivity\n## Model performance\n# Discussion\n## Clinical translational value and stage assessment","[{\"question\":\"What physiological signals are used in the study of Alzheimer’s detection?\",\"answer\":\"Participants listened to emotionally evocative music while electrodermal activity and facial electromyography from specific muscles were recorded.\"},{\"question\":\"How does the study evaluate Alzheimer’s disease using machine learning?\",\"answer\":\"Machine learning models are trained on the recorded physiological signals to classify Alzheimer’s presence and estimate disease severity.\"},{\"question\":\"What were the main findings on emotion and disease progression?\",\"answer\":\"Physiological reactivity to music declined as disease progressed, and positive emotions produced more distinct responses than negative ones. Random Forest and Naïve Bayes achieved reported accuracies for distinguishing patients and predicting severity.\"}]","Music-induced physiological markers for detecting Alzheimer’s disease using machine learning | PDF",1785677075,40,{"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},"music-induced-physiological-markers-for-detecting-alzheimers-disease-using-machine-learning","",{"@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/healthcare/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/music-induced-physiological-markers-for-detecting-alzheimers-disease-using-machine-learning/117577/",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","2026-08-02",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},"What physiological signals are used in the study of Alzheimer’s detection?","Question",{"text":75,"@type":76},"Participants listened to emotionally evocative music while electrodermal activity and facial electromyography from specific muscles were recorded.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How does the study evaluate Alzheimer’s disease using machine learning?",{"text":80,"@type":76},"Machine learning models are trained on the recorded physiological signals to classify Alzheimer’s presence and estimate disease severity.",{"name":82,"@type":73,"acceptedAnswer":83},"What were the main findings on emotion and disease progression?",{"text":84,"@type":76},"Physiological reactivity to music declined as disease progressed, and positive emotions produced more distinct responses than negative ones. 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