{"id":"https://openalex.org/W4285216091","doi":"https://doi.org/10.1109/tai.2022.3184656","title":"Adaptive Multidimensional Dual Attentive DCNN for Detecting Cardiac Morbidities Using Fused ECG-PPG Signals","display_name":"Adaptive Multidimensional Dual Attentive DCNN for Detecting Cardiac Morbidities Using Fused ECG-PPG Signals","publication_year":2022,"publication_date":"2022-06-21","ids":{"openalex":"https://openalex.org/W4285216091","doi":"https://doi.org/10.1109/tai.2022.3184656"},"language":"en","primary_location":{"id":"doi:10.1109/tai.2022.3184656","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tai.2022.3184656","pdf_url":null,"source":{"id":"https://openalex.org/S4210169448","display_name":"IEEE Transactions on Artificial Intelligence","issn_l":"2691-4581","issn":["2691-4581"],"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 Artificial Intelligence","raw_type":"journal-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/A5047212453","display_name":"Poulomi Pal","orcid":"https://orcid.org/0000-0002-9090-0621"},"institutions":[{"id":"https://openalex.org/I145894827","display_name":"Indian Institute of Technology Kharagpur","ror":"https://ror.org/03w5sq511","country_code":"IN","type":"education","lineage":["https://openalex.org/I145894827"]}],"countries":["IN"],"is_corresponding":true,"raw_author_name":"Poulomi Pal","raw_affiliation_strings":["School of Medical Science and Technology, Indian Institute of Technology Kharagpur, West Bengal, India"],"raw_orcid":"https://orcid.org/0000-0002-9090-0621","affiliations":[{"raw_affiliation_string":"School of Medical Science and Technology, Indian Institute of Technology Kharagpur, West Bengal, India","institution_ids":["https://openalex.org/I145894827"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5044064329","display_name":"Manjunatha Mahadevappa","orcid":"https://orcid.org/0000-0003-4700-9080"},"institutions":[{"id":"https://openalex.org/I145894827","display_name":"Indian Institute of Technology Kharagpur","ror":"https://ror.org/03w5sq511","country_code":"IN","type":"education","lineage":["https://openalex.org/I145894827"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Manjunatha Mahadevappa","raw_affiliation_strings":["School of Medical Science and Technology, Indian Institute of Technology Kharagpur, West Bengal, India"],"raw_orcid":"https://orcid.org/0000-0003-4700-9080","affiliations":[{"raw_affiliation_string":"School of Medical Science and Technology, Indian Institute of Technology Kharagpur, West Bengal, India","institution_ids":["https://openalex.org/I145894827"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5047212453"],"corresponding_institution_ids":["https://openalex.org/I145894827"],"apc_list":null,"apc_paid":null,"fwci":2.7837,"has_fulltext":false,"cited_by_count":17,"citation_normalized_percentile":{"value":0.91636091,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":97,"max":99},"biblio":{"volume":"4","issue":"5","first_page":"1225","last_page":"1235"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11021","display_name":"ECG Monitoring and Analysis","score":0.9998000264167786,"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"}},"topics":[{"id":"https://openalex.org/T11021","display_name":"ECG Monitoring and Analysis","score":0.9998000264167786,"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"}},{"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/T11196","display_name":"Non-Invasive Vital Sign Monitoring","score":0.9883000254631042,"subfield":{"id":"https://openalex.org/subfields/2204","display_name":"Biomedical Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4683990180492401},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.45936813950538635},{"id":"https://openalex.org/keywords/signal","display_name":"SIGNAL (programming language)","score":0.42234036326408386},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.41769659519195557},{"id":"https://openalex.org/keywords/medicine","display_name":"Medicine","score":0.3893524408340454},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.3693539798259735}],"concepts":[{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4683990180492401},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.45936813950538635},{"id":"https://openalex.org/C2779843651","wikidata":"https://www.wikidata.org/wiki/Q7390335","display_name":"SIGNAL (programming language)","level":2,"score":0.42234036326408386},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.41769659519195557},{"id":"https://openalex.org/C71924100","wikidata":"https://www.wikidata.org/wiki/Q11190","display_name":"Medicine","level":0,"score":0.3893524408340454},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.3693539798259735},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/tai.2022.3184656","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tai.2022.3184656","pdf_url":null,"source":{"id":"https://openalex.org/S4210169448","display_name":"IEEE Transactions on Artificial Intelligence","issn_l":"2691-4581","issn":["2691-4581"],"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 Artificial Intelligence","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.7799999713897705,"id":"https://metadata.un.org/sdg/10","display_name":"Reduced inequalities"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":36,"referenced_works":["https://openalex.org/W1990576314","https://openalex.org/W2050755789","https://openalex.org/W2097117768","https://openalex.org/W2121014581","https://openalex.org/W2145339207","https://openalex.org/W2145812612","https://openalex.org/W2147018083","https://openalex.org/W2183341477","https://openalex.org/W2194775991","https://openalex.org/W2327284516","https://openalex.org/W2341500107","https://openalex.org/W2557878698","https://openalex.org/W2612025826","https://openalex.org/W2618530766","https://openalex.org/W2756134016","https://openalex.org/W2775795276","https://openalex.org/W2789027062","https://openalex.org/W2800585605","https://openalex.org/W2898898181","https://openalex.org/W2950663303","https://openalex.org/W2954801211","https://openalex.org/W2982983009","https://openalex.org/W2987602118","https://openalex.org/W3039860058","https://openalex.org/W3042663993","https://openalex.org/W3048249223","https://openalex.org/W3084352865","https://openalex.org/W3097020549","https://openalex.org/W3134736546","https://openalex.org/W3199849862","https://openalex.org/W4299802238","https://openalex.org/W4385245566","https://openalex.org/W6739901393","https://openalex.org/W6769955919","https://openalex.org/W6781888022","https://openalex.org/W6801360721"],"related_works":["https://openalex.org/W2748952813","https://openalex.org/W3031052312","https://openalex.org/W4389568370","https://openalex.org/W3032375762","https://openalex.org/W1995515455","https://openalex.org/W2080531066","https://openalex.org/W2586801360","https://openalex.org/W3108674512","https://openalex.org/W2033914206","https://openalex.org/W2042327336"],"abstract_inverted_index":{"Clinicians":[0],"refer":[1],"to":[2,9,53,59,125,167,183,199,225],"standard":[3],"physiological":[4],"signals":[5,74],"(ECG":[6],"and":[7,13,38,55,62,72,87,101,112,118,159,177,207,213,217,243],"PPG)":[8],"diagnose":[10],"conduction":[11],"disorders":[12],"coronary":[14,63],"arterial":[15],"malfunctions.":[16],"The":[17,70,104,133,191],"exact":[18],"disease":[19],"is":[20,51,93,108,123,141,181,194],"identified":[21],"from":[22,65,78,98],"the":[23,99,131,138,174,185,188,200,205,227],"results":[24],"of":[25,146,149,152,157,161,187,204,215,229,235],"confirmatory":[26],"medical":[27],"tests.":[28],"This":[29,220],"procedure":[30],"creates":[31],"a":[32,48,84,232],"time":[33],"lag":[34],"in":[35,41,130,136,197],"treating":[36],"patients":[37],"causes":[39],"difficulty":[40],"attending":[42],"emergency":[43],"cases.":[44],"In":[45],"this":[46],"study,":[47],"novel":[49],"method":[50],"proposed":[52,189],"detect":[54],"classify":[56],"diseases":[57,140],"related":[58],"electrical":[60],"impulses":[61],"arteries":[64],"other":[66,178],"cardiac":[67],"abnormalities":[68],"efficiently.":[69],"ECG":[71,102],"PPG":[73,100],"are":[75,165],"collected":[76],"simultaneously":[77],"300":[79],"cardiovascular":[80],"diseased":[81],"patients,":[82],"following":[83],"predecided":[85],"inclusion":[86],"exclusion":[88],"criteria.":[89],"A":[90],"fused":[91,192],"signal":[92,193],"generated":[94],"using":[95],"an":[96,127,144,169,211],"algorithm":[97],"signals.":[103,202],"deep":[105],"neural":[106],"network":[107],"constructed":[109],"involving":[110,239],"self":[111],"cross":[113],"attention":[114],"properties,":[115],"multidimensional":[116],"convolution,":[117],"skip":[119],"connections.":[120],"Reinforcement":[121],"learning":[122],"utilized":[124],"induce":[126],"adaptive":[128],"property":[129],"model.":[132],"model\u2019s":[134],"ability":[135],"distinguishing":[137],"targeted":[139],"achieved":[142],"with":[143,173],"accuracy":[145],"0.80,":[147],"recall":[148],"0.75,":[150],"precision":[151],"0.73,":[153],"micro":[154],"avg":[155],"F-score":[156],"0.78,":[158],"specificity":[160],"0.85.":[162],"Ablation":[163],"studies":[164],"performed":[166],"get":[168],"efficient":[170,196],"network.":[171,190],"Comparison":[172],"state-of-art":[175],"methods":[176],"established":[179],"networks":[180],"made":[182],"denote":[184],"superiority":[186],"more":[195],"comparison":[198],"individual":[201],"Analysis":[203],"ROC":[206],"PR":[208],"curves":[209],"produced":[210],"AUC":[212],"AP":[214],"0.79":[216],"0.76,":[218],"respectively.":[219],"technique":[221],"could":[222],"be":[223],"used":[224],"identify":[226],"type":[228],"CVD":[230],"at":[231],"preliminary":[233],"stage":[234],"clinical":[236],"diagnosis":[237],"effectively":[238],"less":[240],"time,":[241],"effort,":[242],"resources.":[244]},"counts_by_year":[{"year":2025,"cited_by_count":5},{"year":2024,"cited_by_count":8},{"year":2023,"cited_by_count":4}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}