{"id":"https://openalex.org/W2914543868","doi":"https://doi.org/10.5220/0004721000970103","title":"Permutation Entropy of the Electroencephalogram Background Activity in Alzheimer\u2019s Disease - Investigation into the Incidence of Repeated Values","display_name":"Permutation Entropy of the Electroencephalogram Background Activity in Alzheimer\u2019s Disease - Investigation into the Incidence of Repeated Values","publication_year":2014,"publication_date":"2014-01-01","ids":{"openalex":"https://openalex.org/W2914543868","doi":"https://doi.org/10.5220/0004721000970103","mag":"2914543868"},"language":"en","primary_location":{"id":"doi:10.5220/0004721000970103","is_oa":true,"landing_page_url":"https://doi.org/10.5220/0004721000970103","pdf_url":null,"source":null,"license":"cc-by-nc-nd","license_id":"https://openalex.org/licenses/cc-by-nc-nd","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the International Conference on Bio-inspired Systems and Signal Processing","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://doi.org/10.5220/0004721000970103","any_repository_has_fulltext":null},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5075465524","display_name":"Samantha Simons","orcid":"https://orcid.org/0000-0003-4547-793X"},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Samantha Simons","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5074026899","display_name":"Daniel Ab\u00e1solo","orcid":"https://orcid.org/0000-0002-4268-2885"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Daniel Ab\u00e1solo","raw_affiliation_strings":[],"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5075465524"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.26933134,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":94},"biblio":{"volume":null,"issue":null,"first_page":"97","last_page":"103"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10429","display_name":"EEG and Brain-Computer Interfaces","score":0.9987000226974487,"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"}},"topics":[{"id":"https://openalex.org/T10429","display_name":"EEG and Brain-Computer Interfaces","score":0.9987000226974487,"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/T11601","display_name":"Neuroscience and Neural Engineering","score":0.9606999754905701,"subfield":{"id":"https://openalex.org/subfields/2804","display_name":"Cellular and Molecular 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/T10581","display_name":"Neural dynamics and brain function","score":0.9437999725341797,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/electroencephalography","display_name":"Electroencephalography","score":0.5937420129776001},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5416550636291504},{"id":"https://openalex.org/keywords/bonferroni-correction","display_name":"Bonferroni correction","score":0.5072369575500488},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.5055086612701416},{"id":"https://openalex.org/keywords/embedding","display_name":"Embedding","score":0.496084064245224},{"id":"https://openalex.org/keywords/thresholding","display_name":"Thresholding","score":0.49583420157432556},{"id":"https://openalex.org/keywords/permutation","display_name":"Permutation (music)","score":0.49217620491981506},{"id":"https://openalex.org/keywords/entropy","display_name":"Entropy (arrow of time)","score":0.47164076566696167},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.42049896717071533},{"id":"https://openalex.org/keywords/receiver-operating-characteristic","display_name":"Receiver operating characteristic","score":0.419511616230011},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.39026886224746704},{"id":"https://openalex.org/keywords/audiology","display_name":"Audiology","score":0.3586072623729706},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.3262674808502197},{"id":"https://openalex.org/keywords/medicine","display_name":"Medicine","score":0.30540895462036133},{"id":"https://openalex.org/keywords/psychology","display_name":"Psychology","score":0.19018545746803284},{"id":"https://openalex.org/keywords/neuroscience","display_name":"Neuroscience","score":0.12070593237876892}],"concepts":[{"id":"https://openalex.org/C522805319","wikidata":"https://www.wikidata.org/wiki/Q179965","display_name":"Electroencephalography","level":2,"score":0.5937420129776001},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5416550636291504},{"id":"https://openalex.org/C127808970","wikidata":"https://www.wikidata.org/wiki/Q385989","display_name":"Bonferroni correction","level":2,"score":0.5072369575500488},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.5055086612701416},{"id":"https://openalex.org/C41608201","wikidata":"https://www.wikidata.org/wiki/Q980509","display_name":"Embedding","level":2,"score":0.496084064245224},{"id":"https://openalex.org/C191178318","wikidata":"https://www.wikidata.org/wiki/Q2256906","display_name":"Thresholding","level":3,"score":0.49583420157432556},{"id":"https://openalex.org/C21308566","wikidata":"https://www.wikidata.org/wiki/Q7169365","display_name":"Permutation (music)","level":2,"score":0.49217620491981506},{"id":"https://openalex.org/C106301342","wikidata":"https://www.wikidata.org/wiki/Q4117933","display_name":"Entropy (arrow of time)","level":2,"score":0.47164076566696167},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.42049896717071533},{"id":"https://openalex.org/C58471807","wikidata":"https://www.wikidata.org/wiki/Q327120","display_name":"Receiver operating characteristic","level":2,"score":0.419511616230011},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.39026886224746704},{"id":"https://openalex.org/C548259974","wikidata":"https://www.wikidata.org/wiki/Q569965","display_name":"Audiology","level":1,"score":0.3586072623729706},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.3262674808502197},{"id":"https://openalex.org/C71924100","wikidata":"https://www.wikidata.org/wiki/Q11190","display_name":"Medicine","level":0,"score":0.30540895462036133},{"id":"https://openalex.org/C15744967","wikidata":"https://www.wikidata.org/wiki/Q9418","display_name":"Psychology","level":0,"score":0.19018545746803284},{"id":"https://openalex.org/C169760540","wikidata":"https://www.wikidata.org/wiki/Q207011","display_name":"Neuroscience","level":1,"score":0.12070593237876892},{"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/C24890656","wikidata":"https://www.wikidata.org/wiki/Q82811","display_name":"Acoustics","level":1,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.5220/0004721000970103","is_oa":true,"landing_page_url":"https://doi.org/10.5220/0004721000970103","pdf_url":null,"source":null,"license":"cc-by-nc-nd","license_id":"https://openalex.org/licenses/cc-by-nc-nd","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the International Conference on Bio-inspired Systems and Signal Processing","raw_type":"proceedings-article"}],"best_oa_location":{"id":"doi:10.5220/0004721000970103","is_oa":true,"landing_page_url":"https://doi.org/10.5220/0004721000970103","pdf_url":null,"source":null,"license":"cc-by-nc-nd","license_id":"https://openalex.org/licenses/cc-by-nc-nd","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the International Conference on Bio-inspired Systems and Signal Processing","raw_type":"proceedings-article"},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/10","display_name":"Reduced inequalities","score":0.7099999785423279}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":["https://openalex.org/W2390064908","https://openalex.org/W2917228042","https://openalex.org/W2564440352","https://openalex.org/W4253992846","https://openalex.org/W2239990209","https://openalex.org/W2901814704","https://openalex.org/W1542224353","https://openalex.org/W2461483947","https://openalex.org/W2798513666","https://openalex.org/W3009106047"],"abstract_inverted_index":{"This":[0,178],"pilot":[1],"study":[2],"applied":[3],"Permutation":[4],"Entropy":[5],"(PE),":[6],"a":[7,12],"non-linear":[8],"symbolic":[9],"measure,":[10],"and":[11,30,43,104,128,137,144,146,153,169],"novel":[13],"modification":[14],"(modPE),":[15],"to":[16,83,123],"investigate":[17],"the":[18,47,70],"regularity":[19,63],"of":[20,49],"electroencephalogram":[21],"(EEG)":[22],"signals":[23,187],"from":[24,115],"11":[25,31],"Alzheimer\u00e2\u0080\u0099s":[26],"disease":[27],"(AD)":[28],"patients":[29,127],"age-matched":[32],"controls":[33,60,129],"given":[34,190],"input":[35,170],"parameters":[36],"n":[37],"(embedding":[38],"vector),":[39],"I\u0084":[40,68],"(coarse":[41],"graining)":[42],"slide":[44],"(difference":[45],"between":[46,73,89],"start":[48],"two":[50],"concurrent":[51],"embedding":[52,77],"vectors).":[53],"PE":[54,136,143,152,181],"discriminated":[55],"better":[56],"than":[57],"modPE":[58,194],"with":[59,94,101,113,174],"showing":[61],"reduced":[62],"over":[64,198],"AD":[65,126],"patients.":[66],"Increasing":[67],"identified":[69],"greatest":[71,87,121],"differences":[72],"EEG":[74,186],"signals.":[75],"Longer":[76],"vectors":[78],"were":[79,130],"also":[80],"more":[81],"able":[82],"identify":[84,125,183],"differences.":[85],"The":[86,120,156],"difference":[88],"groups":[90,160],"was":[91,110,163],"at":[92,166],"Fp1":[93],"n,I\u0084,slide":[95,133,140,149],"=":[96,134,141,150],"3,10,1":[97],"(p=0.0112":[98],"Kruskal":[99],"Wallis":[100],"Bonferroni).":[102],"Subject":[103],"epoch":[105],"based":[106,176],"leave-one-out":[107],"cross":[108],"validation":[109],"carried":[111],"out":[112],"thresholding":[114],"Receiver":[116],"Operating":[117],"Characteristic":[118],"Curves.":[119],"ability":[122],"correctly":[124,161],"81.82%":[131,164],"(Fp2":[132],"7,4,4,":[135],"modPE,":[138],"F7":[139],"3,10,1,":[142,151],"modPE)":[145],"90.91%":[147],"(Fp1":[148],"modPE),":[154],"respectively.":[155],"maximum":[157],"accuracy":[158],"(both":[159],"identified)":[162],"seen":[165],"many":[167],"electrode":[168],"combinations.":[171],"All":[172],"are":[173],"subject":[175],"analysis.":[177],"suggests":[179],"that":[180],"can":[182],"changes":[184],"in":[185,188],"AD,":[189],"appropriate":[191],"variables.":[192],"However,":[193],"makes":[195],"little":[196],"improvement":[197],"PE.":[199]},"counts_by_year":[{"year":2022,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
