{"id":"https://openalex.org/W2919854899","doi":"https://doi.org/10.1109/taffc.2020.2981610","title":"A Bayesian Deep Learning Framework for End-To-End Prediction of Emotion From Heartbeat","display_name":"A Bayesian Deep Learning Framework for End-To-End Prediction of Emotion From Heartbeat","publication_year":2020,"publication_date":"2020-03-20","ids":{"openalex":"https://openalex.org/W2919854899","doi":"https://doi.org/10.1109/taffc.2020.2981610","mag":"2919854899"},"language":"en","primary_location":{"id":"doi:10.1109/taffc.2020.2981610","is_oa":false,"landing_page_url":"https://doi.org/10.1109/taffc.2020.2981610","pdf_url":null,"source":{"id":"https://openalex.org/S104780363","display_name":"IEEE Transactions on Affective Computing","issn_l":"1949-3045","issn":["1949-3045","2371-9850"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Affective Computing","raw_type":"journal-article"},"type":"article","indexed_in":["arxiv","crossref"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/1902.03043","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5028958868","display_name":"Ross Harper","orcid":"https://orcid.org/0000-0002-2403-2088"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Ross Harper","raw_affiliation_strings":["Limbic Ltd, London, UK"],"raw_orcid":"https://orcid.org/0000-0002-2403-2088","affiliations":[{"raw_affiliation_string":"Limbic Ltd, London, UK","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5045362680","display_name":"Joshua Southern","orcid":"https://orcid.org/0000-0001-9042-6504"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Joshua Southern","raw_affiliation_strings":["Limbic Ltd, London, UK"],"raw_orcid":"https://orcid.org/0000-0001-9042-6504","affiliations":[{"raw_affiliation_string":"Limbic Ltd, London, UK","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":{"value":2045,"currency":"USD","value_usd":2045},"apc_paid":null,"fwci":8.9381,"has_fulltext":false,"cited_by_count":86,"citation_normalized_percentile":{"value":0.98304915,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":89,"max":100},"biblio":{"volume":"13","issue":"2","first_page":"985","last_page":"991"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10667","display_name":"Emotion and Mood Recognition","score":0.9993000030517578,"subfield":{"id":"https://openalex.org/subfields/3205","display_name":"Experimental and Cognitive Psychology"},"field":{"id":"https://openalex.org/fields/32","display_name":"Psychology"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},"topics":[{"id":"https://openalex.org/T10667","display_name":"Emotion and Mood Recognition","score":0.9993000030517578,"subfield":{"id":"https://openalex.org/subfields/3205","display_name":"Experimental and Cognitive Psychology"},"field":{"id":"https://openalex.org/fields/32","display_name":"Psychology"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},{"id":"https://openalex.org/T10429","display_name":"EEG and Brain-Computer Interfaces","score":0.9975000023841858,"subfield":{"id":"https://openalex.org/subfields/2805","display_name":"Cognitive Neuroscience"},"field":{"id":"https://openalex.org/fields/28","display_name":"Neuroscience"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}},{"id":"https://openalex.org/T10745","display_name":"Heart Rate Variability and Autonomic Control","score":0.9937000274658203,"subfield":{"id":"https://openalex.org/subfields/2705","display_name":"Cardiology and Cardiovascular Medicine"},"field":{"id":"https://openalex.org/fields/27","display_name":"Medicine"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/heartbeat","display_name":"Heartbeat","score":0.7702962160110474},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6891849040985107},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6854608654975891},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.6526745557785034},{"id":"https://openalex.org/keywords/probabilistic-logic","display_name":"Probabilistic logic","score":0.5369067192077637},{"id":"https://openalex.org/keywords/modalities","display_name":"Modalities","score":0.5226747393608093},{"id":"https://openalex.org/keywords/bayesian-probability","display_name":"Bayesian probability","score":0.5097724795341492},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.47971218824386597},{"id":"https://openalex.org/keywords/bayesian-network","display_name":"Bayesian network","score":0.46210774779319763},{"id":"https://openalex.org/keywords/valence","display_name":"Valence (chemistry)","score":0.4560319483280182},{"id":"https://openalex.org/keywords/emotional-valence","display_name":"Emotional valence","score":0.4520662724971771},{"id":"https://openalex.org/keywords/end-to-end-principle","display_name":"End-to-end principle","score":0.44888439774513245},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.3554261326789856}],"concepts":[{"id":"https://openalex.org/C13852961","wikidata":"https://www.wikidata.org/wiki/Q17021880","display_name":"Heartbeat","level":2,"score":0.7702962160110474},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6891849040985107},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6854608654975891},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.6526745557785034},{"id":"https://openalex.org/C49937458","wikidata":"https://www.wikidata.org/wiki/Q2599292","display_name":"Probabilistic logic","level":2,"score":0.5369067192077637},{"id":"https://openalex.org/C2779903281","wikidata":"https://www.wikidata.org/wiki/Q6888026","display_name":"Modalities","level":2,"score":0.5226747393608093},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.5097724795341492},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.47971218824386597},{"id":"https://openalex.org/C33724603","wikidata":"https://www.wikidata.org/wiki/Q812540","display_name":"Bayesian network","level":2,"score":0.46210774779319763},{"id":"https://openalex.org/C168900304","wikidata":"https://www.wikidata.org/wiki/Q171407","display_name":"Valence (chemistry)","level":2,"score":0.4560319483280182},{"id":"https://openalex.org/C3020774634","wikidata":"https://www.wikidata.org/wiki/Q3113318","display_name":"Emotional valence","level":3,"score":0.4520662724971771},{"id":"https://openalex.org/C74296488","wikidata":"https://www.wikidata.org/wiki/Q2527392","display_name":"End-to-end principle","level":2,"score":0.44888439774513245},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3554261326789856},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0},{"id":"https://openalex.org/C144024400","wikidata":"https://www.wikidata.org/wiki/Q21201","display_name":"Sociology","level":0,"score":0.0},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0},{"id":"https://openalex.org/C169900460","wikidata":"https://www.wikidata.org/wiki/Q2200417","display_name":"Cognition","level":2,"score":0.0},{"id":"https://openalex.org/C36289849","wikidata":"https://www.wikidata.org/wiki/Q34749","display_name":"Social science","level":1,"score":0.0},{"id":"https://openalex.org/C169760540","wikidata":"https://www.wikidata.org/wiki/Q207011","display_name":"Neuroscience","level":1,"score":0.0},{"id":"https://openalex.org/C38652104","wikidata":"https://www.wikidata.org/wiki/Q3510521","display_name":"Computer security","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/taffc.2020.2981610","is_oa":false,"landing_page_url":"https://doi.org/10.1109/taffc.2020.2981610","pdf_url":null,"source":{"id":"https://openalex.org/S104780363","display_name":"IEEE Transactions on Affective Computing","issn_l":"1949-3045","issn":["1949-3045","2371-9850"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Affective Computing","raw_type":"journal-article"},{"id":"pmh:oai:arXiv.org:1902.03043","is_oa":true,"landing_page_url":"http://arxiv.org/abs/1902.03043","pdf_url":"https://arxiv.org/pdf/1902.03043","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:1902.03043","is_oa":true,"landing_page_url":"http://arxiv.org/abs/1902.03043","pdf_url":"https://arxiv.org/pdf/1902.03043","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"sustainable_development_goals":[{"display_name":"Peace, Justice and strong institutions","score":0.8100000023841858,"id":"https://metadata.un.org/sdg/16"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":79,"referenced_works":["https://openalex.org/W582134693","https://openalex.org/W1494192115","https://openalex.org/W1522301498","https://openalex.org/W1567512734","https://openalex.org/W1677182931","https://openalex.org/W1963525383","https://openalex.org/W1965035892","https://openalex.org/W1969756516","https://openalex.org/W1975911018","https://openalex.org/W1987048275","https://openalex.org/W1989756802","https://openalex.org/W2001233357","https://openalex.org/W2006633893","https://openalex.org/W2012711836","https://openalex.org/W2019312772","https://openalex.org/W2019608648","https://openalex.org/W2029334490","https://openalex.org/W2036309320","https://openalex.org/W2048278868","https://openalex.org/W2058815792","https://openalex.org/W2068276368","https://openalex.org/W2072036788","https://openalex.org/W2077681639","https://openalex.org/W2095705004","https://openalex.org/W2098816250","https://openalex.org/W2104637120","https://openalex.org/W2105198535","https://openalex.org/W2108677974","https://openalex.org/W2110659025","https://openalex.org/W2112945682","https://openalex.org/W2114025269","https://openalex.org/W2117670920","https://openalex.org/W2122098299","https://openalex.org/W2122348661","https://openalex.org/W2125387256","https://openalex.org/W2125462608","https://openalex.org/W2145069607","https://openalex.org/W2146010402","https://openalex.org/W2149055748","https://openalex.org/W2149875516","https://openalex.org/W2150150736","https://openalex.org/W2156322511","https://openalex.org/W2156503193","https://openalex.org/W2159017231","https://openalex.org/W2159394210","https://openalex.org/W2164368909","https://openalex.org/W2167433878","https://openalex.org/W2171848217","https://openalex.org/W2172947082","https://openalex.org/W2277786047","https://openalex.org/W2291663305","https://openalex.org/W2312303239","https://openalex.org/W2343412983","https://openalex.org/W2395644623","https://openalex.org/W2533298663","https://openalex.org/W2545857823","https://openalex.org/W2547146855","https://openalex.org/W2562037482","https://openalex.org/W2563836857","https://openalex.org/W2599124244","https://openalex.org/W2602582143","https://openalex.org/W2625812463","https://openalex.org/W2731964405","https://openalex.org/W2750852530","https://openalex.org/W2765856398","https://openalex.org/W2782273434","https://openalex.org/W2790444357","https://openalex.org/W2807343060","https://openalex.org/W2953384591","https://openalex.org/W2963275203","https://openalex.org/W2964059111","https://openalex.org/W2964121744","https://openalex.org/W4236533540","https://openalex.org/W4244739940","https://openalex.org/W4253024038","https://openalex.org/W6631190155","https://openalex.org/W6684809622","https://openalex.org/W6687969547","https://openalex.org/W6713134421"],"related_works":["https://openalex.org/W4385543909","https://openalex.org/W3039320222","https://openalex.org/W3199640442","https://openalex.org/W1898280036","https://openalex.org/W2315807364","https://openalex.org/W2382278803","https://openalex.org/W2376695684","https://openalex.org/W1982967776","https://openalex.org/W2803040299","https://openalex.org/W2034075638"],"abstract_inverted_index":{"Automatic":[0],"prediction":[1,54],"of":[2,13,27,40,52,59,131,141,159],"emotion":[3,53],"promises":[4],"to":[5,36,66,107,114],"revolutionise":[6],"human-computer":[7],"interaction.":[8],"Recent":[9],"trends":[10],"involve":[11],"fusion":[12],"multiple":[14],"data":[15,29],"modalities$-$-audio,":[16],"visual,":[17],"and":[18,100,126,161],"physiological$-$-to":[19],"classify":[20],"emotional":[21,80],"state.":[22],"However,":[23],"in":[24,64,144],"practice,":[25],"collection":[26,158],"physiological":[28],"\u2018in":[30],"the":[31,41,115,137],"wild\u2019":[32],"is":[33,154],"currently":[34],"limited":[35],"heartbeat":[37,84],"time":[38,85],"series":[39],"kind":[42],"generated":[43],"by":[44],"affordable":[45],"wearable":[46],"heart":[47],"monitors.":[48],"Furthermore,":[49],"real-world":[50,145],"applications":[51,140],"often":[55],"require":[56],"some":[57],"measure":[58],"uncertainty":[60,95],"over":[61,96],"model":[62,77,111],"output,":[63],"order":[65],"inform":[67],"downstream":[68],"decision-making.":[69],"We":[70,87,118],"present":[71],"here":[72],"an":[73],"end-to-end":[74],"deep":[75],"learning":[76],"for":[78,93,105,139],"classifying":[79],"valence":[81,98],"from":[82],"unimodal":[83],"series.":[86],"further":[88],"propose":[89],"a":[90,102,151],"Bayesian":[91],"framework":[92,121],"modelling":[94],"these":[97],"predictions,":[99],"describe":[101],"probabilistic":[103],"procedure":[104],"choosing":[106],"accept":[108],"or":[109],"reject":[110],"output":[112],"according":[113],"intended":[116],"application.":[117],"benchmarked":[119],"our":[120],"against":[122],"two":[123],"established":[124],"datasets":[125],"achieved":[127],"peak":[128],"classification":[129],"accuracy":[130],"90":[132],"percent.":[133],"These":[134],"results":[135],"lay":[136],"foundation":[138],"affective":[142],"computing":[143],"domains":[146],"such":[147],"as":[148],"healthcare,":[149],"where":[150],"high":[152],"premium":[153],"placed":[155],"on":[156],"non-invasive":[157],"data,":[160],"predictive":[162],"certainty.":[163]},"counts_by_year":[{"year":2026,"cited_by_count":8},{"year":2025,"cited_by_count":14},{"year":2024,"cited_by_count":10},{"year":2023,"cited_by_count":20},{"year":2022,"cited_by_count":17},{"year":2021,"cited_by_count":10},{"year":2020,"cited_by_count":6},{"year":2019,"cited_by_count":1}],"updated_date":"2026-08-28T12:50:07.497085","created_date":"2019-03-11T00:00:00"}
