{"id":"https://openalex.org/W2180361250","doi":"https://doi.org/10.1109/cicc.2015.7338456","title":"A seizure-detection IC employing machine learning to overcome data-conversion and analog-processing non-idealities","display_name":"A seizure-detection IC employing machine learning to overcome data-conversion and analog-processing non-idealities","publication_year":2015,"publication_date":"2015-09-01","ids":{"openalex":"https://openalex.org/W2180361250","doi":"https://doi.org/10.1109/cicc.2015.7338456","mag":"2180361250"},"language":"en","primary_location":{"id":"doi:10.1109/cicc.2015.7338456","is_oa":false,"landing_page_url":"https://doi.org/10.1109/cicc.2015.7338456","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2015 IEEE Custom Integrated Circuits Conference (CICC)","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/A5052607394","display_name":"Jintao Zhang","orcid":"https://orcid.org/0000-0002-2909-4552"},"institutions":[{"id":"https://openalex.org/I20089843","display_name":"Princeton University","ror":"https://ror.org/00hx57361","country_code":"US","type":"education","lineage":["https://openalex.org/I20089843"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Jintao Zhang","raw_affiliation_strings":["Princeton University, Princeton, NJ, USA"],"affiliations":[{"raw_affiliation_string":"Princeton University, Princeton, NJ, USA","institution_ids":["https://openalex.org/I20089843"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5102183431","display_name":"Liechao Huang","orcid":null},"institutions":[{"id":"https://openalex.org/I20089843","display_name":"Princeton University","ror":"https://ror.org/00hx57361","country_code":"US","type":"education","lineage":["https://openalex.org/I20089843"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Liechao Huang","raw_affiliation_strings":["Princeton University, Princeton, NJ, USA"],"affiliations":[{"raw_affiliation_string":"Princeton University, Princeton, NJ, USA","institution_ids":["https://openalex.org/I20089843"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100446577","display_name":"Zhuo Wang","orcid":"https://orcid.org/0000-0002-3296-8599"},"institutions":[{"id":"https://openalex.org/I20089843","display_name":"Princeton University","ror":"https://ror.org/00hx57361","country_code":"US","type":"education","lineage":["https://openalex.org/I20089843"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Zhuo Wang","raw_affiliation_strings":["Princeton University, Princeton, NJ, USA"],"affiliations":[{"raw_affiliation_string":"Princeton University, Princeton, NJ, USA","institution_ids":["https://openalex.org/I20089843"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5101645607","display_name":"Naveen Verma","orcid":"https://orcid.org/0000-0002-8208-5030"},"institutions":[{"id":"https://openalex.org/I20089843","display_name":"Princeton University","ror":"https://ror.org/00hx57361","country_code":"US","type":"education","lineage":["https://openalex.org/I20089843"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Naveen Verma","raw_affiliation_strings":["Princeton University, Princeton, NJ, USA"],"affiliations":[{"raw_affiliation_string":"Princeton University, Princeton, NJ, USA","institution_ids":["https://openalex.org/I20089843"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5052607394"],"corresponding_institution_ids":["https://openalex.org/I20089843"],"apc_list":null,"apc_paid":null,"fwci":0.6452,"has_fulltext":false,"cited_by_count":11,"citation_normalized_percentile":{"value":0.68610645,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":96},"biblio":{"volume":"48","issue":null,"first_page":"1","last_page":"4"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10429","display_name":"EEG and Brain-Computer Interfaces","score":0.9998000264167786,"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":0.9998000264167786,"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/T10323","display_name":"Analog and Mixed-Signal Circuit Design","score":0.9994999766349792,"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"}},{"id":"https://openalex.org/T11601","display_name":"Neuroscience and Neural Engineering","score":0.9979000091552734,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6907585859298706},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5683842301368713},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.5173951387405396},{"id":"https://openalex.org/keywords/noise","display_name":"Noise (video)","score":0.4917800724506378},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.48322638869285583},{"id":"https://openalex.org/keywords/sensitivity","display_name":"Sensitivity (control systems)","score":0.4653604328632355},{"id":"https://openalex.org/keywords/cmos","display_name":"CMOS","score":0.453104704618454},{"id":"https://openalex.org/keywords/latency","display_name":"Latency (audio)","score":0.43483108282089233},{"id":"https://openalex.org/keywords/linearity","display_name":"Linearity","score":0.4201039671897888},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.41601794958114624},{"id":"https://openalex.org/keywords/electronic-engineering","display_name":"Electronic engineering","score":0.3616187870502472},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.18648377060890198}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6907585859298706},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5683842301368713},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.5173951387405396},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.4917800724506378},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.48322638869285583},{"id":"https://openalex.org/C21200559","wikidata":"https://www.wikidata.org/wiki/Q7451068","display_name":"Sensitivity (control systems)","level":2,"score":0.4653604328632355},{"id":"https://openalex.org/C46362747","wikidata":"https://www.wikidata.org/wiki/Q173431","display_name":"CMOS","level":2,"score":0.453104704618454},{"id":"https://openalex.org/C82876162","wikidata":"https://www.wikidata.org/wiki/Q17096504","display_name":"Latency (audio)","level":2,"score":0.43483108282089233},{"id":"https://openalex.org/C77170095","wikidata":"https://www.wikidata.org/wiki/Q1753188","display_name":"Linearity","level":2,"score":0.4201039671897888},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.41601794958114624},{"id":"https://openalex.org/C24326235","wikidata":"https://www.wikidata.org/wiki/Q126095","display_name":"Electronic engineering","level":1,"score":0.3616187870502472},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.18648377060890198},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","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/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"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:10.1109/cicc.2015.7338456","is_oa":false,"landing_page_url":"https://doi.org/10.1109/cicc.2015.7338456","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2015 IEEE Custom Integrated Circuits Conference (CICC)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.5,"display_name":"Affordable and clean energy","id":"https://metadata.un.org/sdg/7"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":6,"referenced_works":["https://openalex.org/W1964632160","https://openalex.org/W2020192881","https://openalex.org/W2086659790","https://openalex.org/W2138190513","https://openalex.org/W2169766445","https://openalex.org/W6680455596"],"related_works":["https://openalex.org/W3014521742","https://openalex.org/W2023858428","https://openalex.org/W4247324130","https://openalex.org/W2538259895","https://openalex.org/W2126963364","https://openalex.org/W4400804331","https://openalex.org/W2617868873","https://openalex.org/W2218509615","https://openalex.org/W2051287254","https://openalex.org/W2113057816"],"abstract_inverted_index":{"This":[0,73],"paper":[1],"presents":[2],"a":[3,29,66,70,79],"seizure-detection":[4],"system":[5,40],"wherein":[6],"the":[7,11,25,33,75,111,142],"accuracy":[8],"required":[9],"of":[10,19,27,65,110,119,141],"analog":[12,45],"frontend":[13],"is":[14,98],"substantially":[15],"relaxed.":[16],"Typically,":[17],"readout":[18],"electroencephalogram":[20],"(EEG)":[21],"signals":[22],"would":[23],"dominate":[24],"energy":[26],"such":[28],"system,":[30],"due":[31],"to":[32,54,77,94,130,147],"precision":[34],"(noise,":[35],"linearity)":[36],"requirements.":[37],"The":[38,82,125],"presented":[39],"performs":[41],"data":[42],"conversion":[43],"and":[44],"multiplication":[46],"for":[47],"EEG":[48],"feature":[49,57,126],"extraction":[50],"via":[51],"simple":[52],"circuits":[53],"demonstrate":[55],"that":[56],"errors":[58,101,127],"can":[59],"be":[60],"overcome":[61],"by":[62],"appropriate":[63],"retraining":[64,140],"classification":[67,143],"model,":[68],"using":[69],"machine-learning":[71],"algorithm.":[72],"precludes":[74],"need":[76],"design":[78],"high-precision":[80],"frontend.":[81],"prototype,":[83],"in":[84,88],"32nm":[85],"CMOS,":[86],"results":[87],"features":[89],"whose":[90],"RMS":[91],"error":[92],"normalized":[93],"their":[95],"ideal":[96,105,108],"values":[97],"1.16":[99],"(i.e.":[100],"are":[102],"larger":[103],"than":[104],"values).":[106],"An":[107],"implementation":[109],"seizure":[112],"detector":[113],"exhibits":[114],"sensitivity,":[115],"latency,":[116],"false":[117,137],"alarms":[118],"5/5,":[120,131,148],"2.0":[121],"sec.,":[122,133,150],"8,":[123],"respectively.":[124],"degrade":[128],"this":[129,146],"3.6":[132],"443,":[134],"causing":[135],"high":[136],"alarms;":[138],"but":[139],"model":[144],"restores":[145],"3.4":[149],"4.":[151]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":1},{"year":2022,"cited_by_count":1},{"year":2021,"cited_by_count":1},{"year":2020,"cited_by_count":1},{"year":2019,"cited_by_count":2},{"year":2018,"cited_by_count":2},{"year":2015,"cited_by_count":2}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
