[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-125762-en":3,"doc-seo-125762-105":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},125762,3848291630094,"Emma Wilson","https://eur-avatar.wpscdn.com/davatar_085a072bc5b1113ac321206ff7593b45",7,"Healthcare","Denoising and decoding spontaneous vagus nerve recordings with machine learning","Neural interfaces that electrically stimulate peripheral nerves can improve symptom management for conditions such as epilepsy and depression, but effective closed-loop neuromodulation depends on extracting meaningful information from nerve recordings. Such recordings often show low signal-to-noise ratio and non-stationary noise, making it difficult to denoise and interpret clinically relevant signals. This work adapts machine learning methods for unsupervised denoising of in-vivo spontaneous vagus nerve data, evaluating respiratory afferent extraction using correlation, MSE, and accuracy.","Citation for published version:  \nRibeiro, M, Koh, R, Donnelly, T, Lutteroth, C, Proulx, M, Rocha, P & Metcalfe, B 2023, Denoising and decoding spontaneous vagus nerve recordings with machine learning. in 2023 45th Annual International Conference of the IEEE Engineering in Medicine and Biology Conference, EMBC 2023-Proceedings. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS, IEEE, 45th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC'23), 24/07/23 . [https://doi.org/10.1109/EMBC40787.2023.10340443](https://doi.org/10.1109/EMBC40787.2023.10340443)  \nDOI:  \n10.1109/EMBC40787.2023.10340443  \nPublication date:  \n2023  \nDocument Version  \nPeer reviewed version  \nLink to publication  \n© 2023 IEEE. Personal use of this material is permitted. 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Jan. 2024  \nDenoising and decoding spontaneous vagus nerve recordings with  \nmachine learning  \nMafalda Ribeiro 1 ,2 , Ryan G. L. Koh3 , Tom Donnelly 1 ,2 , Christof Lutteroth 1 ,2 , Michael J. Proulx 1 ,2 ,4 ,  \nPaulo R. F. Rocha5 , Benjamin Metcalfe 1 ,2  \nAbstract—Neural interfaces that electrically stimulate the peripheral nervous system have been shown to successfully improve symptom management for several conditions, such as epilepsy and depression. A crucial part for closing the loop and improving the efficacy of implantable neuromodulation devices is the efficient extraction of meaningful information from nerve recordings, which can have a low Signal-to-Noise ratio (SNR) and non-stationary noise. In recent years, machine learning (ML) models have shown outstanding performance in regression and classification problems, but it is often unclear how to translate and assess these for novel tasks in biomedical engineering. This paper aims to adapt existing ML algorithms to carry out unsupervised denoising of neural recordings instead. This is achieved by applying bandpass filtering and two novel ML algorithms to in-vivo spontaneous, low-SNR vagus nerve recordings. The performance of each approach is compared using the task of extracting respiratory afferent activity and validated using cross-correlation, MSE, and accuracy in terms of extracting the true respiratory rate. A variational autoencoder (VAE) model in particular produces results that show better correlation with respiratory activity compared to bandpass filtering, highlighting that these models have the potential to preserve relevant features in complex neural recordings.  \nI. INTRODUCTION  \nNeural implants that electrically stimulate the central or peripheral nervous systems have been an active topic in neural engineering research and in industry, given the possibility to treat neurological conditions in a more localised way [1] . There are commercially-available systems capable of stimulating the nervous system for the treatment of epilepsy and depression (i.e. brain, vagus nerve), for bladder control (i.e. sacral roots), and other applications [2], [3] . However, given the v","cbCaimeaPsKEnptz","https://ap.wps.com/l/cbCaimeaPsKEnptz","pdf",1186457,1,5,"English","en",105,"# Abstract\n# I. Introduction","[{\"question\":\"What problem does the paper address in vagus nerve recordings?\",\"answer\":\"It targets efficient extraction of meaningful information from nerve recordings with low signal-to-noise ratio and non-stationary noise, which makes denoising and processing difficult.\"},{\"question\":\"How is the proposed denoising approach evaluated?\",\"answer\":\"The work compares bandpass filtering and two novel machine learning algorithms using the task of extracting respiratory afferent activity, validated with cross-correlation, MSE, and accuracy for respiratory rate.\"},{\"question\":\"Why does the variational autoencoder (VAE) stand out?\",\"answer\":\"The VAE produces stronger correlation with respiratory activity than bandpass filtering, indicating better preservation of relevant features in complex neural recordings.\"}]","Denoising and decoding spontaneous vagus nerve recordings with machine learning | PDF",1785901074,13,{"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},"denoising-and-decoding-spontaneous-vagus-nerve-recordings-with-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/denoising-and-decoding-spontaneous-vagus-nerve-recordings-with-machine-learning/125762/",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},"What problem does the paper address in vagus nerve recordings?","Question",{"text":75,"@type":76},"It targets efficient extraction of meaningful information from nerve recordings with low signal-to-noise ratio and non-stationary noise, which makes denoising and processing difficult.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How is the proposed denoising approach evaluated?",{"text":80,"@type":76},"The work compares bandpass filtering and two novel machine learning algorithms using the task of extracting respiratory afferent activity, validated with cross-correlation, MSE, and accuracy for respiratory rate.",{"name":82,"@type":73,"acceptedAnswer":83},"Why does the variational autoencoder (VAE) stand out?",{"text":84,"@type":76},"The VAE produces stronger correlation with respiratory activity than bandpass filtering, indicating better preservation of relevant features in complex neural recordings.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,109,114,117,122,127,130,134],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":21,"doc_module":4,"doc_module_name":46,"category_name":106,"show_sort_weight":107,"slug":108},"Comic",60,"comic",{"id":110,"doc_module":4,"doc_module_name":46,"category_name":111,"show_sort_weight":112,"slug":113},6,"Technology",50,"technology",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":115,"slug":116},40,"healthcare",{"id":118,"doc_module":4,"doc_module_name":46,"category_name":119,"show_sort_weight":120,"slug":121},8,"Research & Report",30,"research-report",{"id":123,"doc_module":4,"doc_module_name":46,"category_name":124,"show_sort_weight":125,"slug":126},9,"Religion & Spirituality",20,"religion-spirituality",{"id":125,"doc_module":4,"doc_module_name":46,"category_name":128,"show_sort_weight":125,"slug":129},"World Cup","world-cup",{"id":131,"doc_module":4,"doc_module_name":46,"category_name":132,"show_sort_weight":131,"slug":133},10,"Lifestyle","lifestyle",{"id":135,"doc_module":4,"doc_module_name":46,"category_name":136,"show_sort_weight":21,"slug":137},19,"General","general"]