{"id":"https://openalex.org/W2983837486","doi":"https://doi.org/10.23919/eusipco.2019.8902562","title":"Detecting Early Parkinson\u2019s Disease from Keystroke Dynamics using the Tensor-Train Decomposition","display_name":"Detecting Early Parkinson\u2019s Disease from Keystroke Dynamics using the Tensor-Train Decomposition","publication_year":2019,"publication_date":"2019-09-01","ids":{"openalex":"https://openalex.org/W2983837486","doi":"https://doi.org/10.23919/eusipco.2019.8902562","mag":"2983837486"},"language":"en","primary_location":{"id":"doi:10.23919/eusipco.2019.8902562","is_oa":false,"landing_page_url":"https://doi.org/10.23919/eusipco.2019.8902562","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 27th European Signal Processing Conference (EUSIPCO)","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5087667469","display_name":"Oroojeni M. J. Hooman","orcid":null},"institutions":[{"id":"https://openalex.org/I55521800","display_name":"Goldsmiths University of London","ror":"https://ror.org/01khx4a30","country_code":"GB","type":"education","lineage":["https://openalex.org/I124357947","https://openalex.org/I55521800"]}],"countries":["GB"],"is_corresponding":true,"raw_author_name":"Oroojeni M. J. Hooman","raw_affiliation_strings":["Department of Computing, Goldsmiths, University of London, London"],"affiliations":[{"raw_affiliation_string":"Department of Computing, Goldsmiths, University of London, London","institution_ids":["https://openalex.org/I55521800"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5046779014","display_name":"James Oldfield","orcid":"https://orcid.org/0000-0002-7000-5179"},"institutions":[{"id":"https://openalex.org/I55521800","display_name":"Goldsmiths University of London","ror":"https://ror.org/01khx4a30","country_code":"GB","type":"education","lineage":["https://openalex.org/I124357947","https://openalex.org/I55521800"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"James Oldfield","raw_affiliation_strings":["Department of Computing, Goldsmiths, University of London, London"],"affiliations":[{"raw_affiliation_string":"Department of Computing, Goldsmiths, University of London, London","institution_ids":["https://openalex.org/I55521800"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5017437981","display_name":"Mihalis A. Nicolaou","orcid":"https://orcid.org/0000-0001-9175-477X"},"institutions":[{"id":"https://openalex.org/I148518341","display_name":"Cyprus Institute","ror":"https://ror.org/01q8k8p90","country_code":"CY","type":"other","lineage":["https://openalex.org/I148518341"]}],"countries":["CY"],"is_corresponding":false,"raw_author_name":"Mihalis A. Nicolaou","raw_affiliation_strings":["Computation-based Science and Technology Research Center, The Cyprus Institute, Cyprus"],"affiliations":[{"raw_affiliation_string":"Computation-based Science and Technology Research Center, The Cyprus Institute, Cyprus","institution_ids":["https://openalex.org/I148518341"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5087667469"],"corresponding_institution_ids":["https://openalex.org/I55521800"],"apc_list":null,"apc_paid":null,"fwci":0.7194,"has_fulltext":false,"cited_by_count":10,"citation_normalized_percentile":{"value":0.65037594,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":97},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"5"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12303","display_name":"Tensor decomposition and applications","score":0.9817000031471252,"subfield":{"id":"https://openalex.org/subfields/2605","display_name":"Computational Mathematics"},"field":{"id":"https://openalex.org/fields/26","display_name":"Mathematics"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T12303","display_name":"Tensor decomposition and applications","score":0.9817000031471252,"subfield":{"id":"https://openalex.org/subfields/2605","display_name":"Computational Mathematics"},"field":{"id":"https://openalex.org/fields/26","display_name":"Mathematics"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10812","display_name":"Human Pose and Action Recognition","score":0.9564999938011169,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/keystroke-dynamics","display_name":"Keystroke dynamics","score":0.8823581337928772},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7166998386383057},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5781646966934204},{"id":"https://openalex.org/keywords/scalability","display_name":"Scalability","score":0.5651969909667969},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.5416986346244812},{"id":"https://openalex.org/keywords/tensor","display_name":"Tensor (intrinsic definition)","score":0.5213790535926819},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.513179361820221},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.44636979699134827},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4434013068675995},{"id":"https://openalex.org/keywords/decomposition","display_name":"Decomposition","score":0.4323580265045166},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.1506413221359253}],"concepts":[{"id":"https://openalex.org/C79540074","wikidata":"https://www.wikidata.org/wiki/Q3269465","display_name":"Keystroke dynamics","level":4,"score":0.8823581337928772},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7166998386383057},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5781646966934204},{"id":"https://openalex.org/C48044578","wikidata":"https://www.wikidata.org/wiki/Q727490","display_name":"Scalability","level":2,"score":0.5651969909667969},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.5416986346244812},{"id":"https://openalex.org/C155281189","wikidata":"https://www.wikidata.org/wiki/Q3518150","display_name":"Tensor (intrinsic definition)","level":2,"score":0.5213790535926819},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.513179361820221},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.44636979699134827},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4434013068675995},{"id":"https://openalex.org/C124681953","wikidata":"https://www.wikidata.org/wiki/Q339062","display_name":"Decomposition","level":2,"score":0.4323580265045166},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.1506413221359253},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0},{"id":"https://openalex.org/C77088390","wikidata":"https://www.wikidata.org/wiki/Q8513","display_name":"Database","level":1,"score":0.0},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0},{"id":"https://openalex.org/C4957475","wikidata":"https://www.wikidata.org/wiki/Q242186","display_name":"S/KEY","level":3,"score":0.0},{"id":"https://openalex.org/C202444582","wikidata":"https://www.wikidata.org/wiki/Q837863","display_name":"Pure mathematics","level":1,"score":0.0},{"id":"https://openalex.org/C109297577","wikidata":"https://www.wikidata.org/wiki/Q161157","display_name":"Password","level":2,"score":0.0},{"id":"https://openalex.org/C18903297","wikidata":"https://www.wikidata.org/wiki/Q7150","display_name":"Ecology","level":1,"score":0.0},{"id":"https://openalex.org/C31258907","wikidata":"https://www.wikidata.org/wiki/Q1301371","display_name":"Computer network","level":1,"score":0.0},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.23919/eusipco.2019.8902562","is_oa":false,"landing_page_url":"https://doi.org/10.23919/eusipco.2019.8902562","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 27th European Signal Processing Conference (EUSIPCO)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":31,"referenced_works":["https://openalex.org/W76115318","https://openalex.org/W1246381107","https://openalex.org/W1755563775","https://openalex.org/W1924770834","https://openalex.org/W1976709621","https://openalex.org/W1993482030","https://openalex.org/W2024165284","https://openalex.org/W2031547184","https://openalex.org/W2057503509","https://openalex.org/W2058580716","https://openalex.org/W2070013413","https://openalex.org/W2076063813","https://openalex.org/W2102892751","https://openalex.org/W2119412403","https://openalex.org/W2167083368","https://openalex.org/W2207405009","https://openalex.org/W2343698281","https://openalex.org/W2528907418","https://openalex.org/W2545001194","https://openalex.org/W2752217370","https://openalex.org/W2783318359","https://openalex.org/W2802314367","https://openalex.org/W2808870406","https://openalex.org/W2890529804","https://openalex.org/W3013294074","https://openalex.org/W3098973674","https://openalex.org/W3103552852","https://openalex.org/W6640212811","https://openalex.org/W6729149246","https://openalex.org/W6743676628","https://openalex.org/W6747544394"],"related_works":["https://openalex.org/W2026138706","https://openalex.org/W2987557069","https://openalex.org/W2888741745","https://openalex.org/W3008338658","https://openalex.org/W2971685648","https://openalex.org/W2949968632","https://openalex.org/W2735165841","https://openalex.org/W2086833104","https://openalex.org/W4323697194","https://openalex.org/W1501550656"],"abstract_inverted_index":{"We":[0],"present":[1],"a":[2,58,88],"method":[3],"for":[4,83],"detecting":[5,104],"early":[6,105],"signs":[7],"of":[8,90,103],"Parkinson's":[9,106],"disease":[10,107],"from":[11,108],"keystroke":[12,109],"hold":[13],"times":[14],"that":[15,74],"is":[16],"based":[17,67],"on":[18,35,68,100],"the":[19,36,43,52,75,84,101],"Tensor-Train":[20],"(TT)":[21],"decomposition.":[22],"While":[23],"simple":[24],"uni-variate":[25],"methods":[26,94],"such":[27,95],"as":[28,96],"logistic":[29],"regression":[30],"have":[31],"shown":[32],"good":[33],"performance":[34,89],"given":[37,85],"problem":[38,102],"by":[39,50],"using":[40],"appropriate":[41],"features,":[42],"TT":[44],"format":[45],"facilitates":[46],"modelling":[47],"high-order":[48],"interactions":[49],"representing":[51],"exponentially":[53],"large":[54],"parameter":[55],"tensor":[56],"in":[57],"compact":[59],"multi-linear":[60],"form.":[61],"By":[62],"performing":[63],"time-series":[64],"feature":[65],"extraction":[66],"scalable":[69],"hypothesis":[70],"testing,":[71],"we":[72],"show":[73],"proposed":[76],"approach":[77],"can":[78],"significantly":[79],"improve":[80],"upon":[81],"state-of-the-art":[82],"problem,":[86],"reaching":[87],"AUC=0.88,":[91],"outperforming":[92],"compared":[93],"deep":[97],"neural":[98],"networks":[99],"dynamics.":[110]},"counts_by_year":[{"year":2025,"cited_by_count":2},{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":2},{"year":2022,"cited_by_count":4},{"year":2021,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
