{"id":"https://openalex.org/W7225733103","doi":"https://doi.org/10.21203/rs.3.rs-11396766/v1","title":"TensorECG: Adaptive-Rank Temporal\u2013Spatial Tensor Compression of Multi-lead ECG Signals with Wavelet and low bit residual refinement","display_name":"TensorECG: Adaptive-Rank Temporal\u2013Spatial Tensor Compression of Multi-lead ECG Signals with Wavelet and low bit residual refinement","publication_year":2026,"publication_date":"2026-11-10","ids":{"openalex":"https://openalex.org/W7225733103","doi":"https://doi.org/10.21203/rs.3.rs-11396766/v1"},"language":"en","primary_location":{"id":"doi:10.21203/rs.3.rs-11396766/v1","doi":"https://doi.org/10.21203/rs.3.rs-11396766/v1","is_oa":true,"landing_page_url":"https://doi.org/10.21203/rs.3.rs-11396766/v1","pdf_url":null,"source":{"id":"https://openalex.org/S4306525896","display_name":"Research Square","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Research Square","raw_type":"posted-content"},"volume":null,"type":"preprint","is_traditional":true,"indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.21203/rs.3.rs-11396766/v1","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5161394098","display_name":"Ashish Pratap","orcid":null,"observed_orcids":[]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Ashish Pratap","raw_affiliation_strings":["National Institute of Technology"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"National Institute of Technology","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5013602412","display_name":"Hariharan Muthusamy","orcid":null,"observed_orcids":[]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Hariharan Muthusamy","raw_affiliation_strings":["National Institute of Technology"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"National Institute of Technology","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5102868385","display_name":"S D Sudarsan","orcid":"https://orcid.org/0009-0006-5510-2276","observed_orcids":["https://orcid.org/0009-0006-5510-2276","https://orcid.org/0000-0002-7990-6686"]},"institutions":[{"id":"https://openalex.org/I1331500379","display_name":"Centre for Development of Advanced Computing","ror":"https://ror.org/022abst40","country_code":"IN","type":"facility","lineage":["https://openalex.org/I1331500379","https://openalex.org/I4210121746"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"S. D. Sudarsan","raw_affiliation_strings":["C-DAC"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"C-DAC","institution_ids":["https://openalex.org/I1331500379"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5069842087","display_name":"Ramesh Naidu Laveti","orcid":null,"observed_orcids":[]},"institutions":[{"id":"https://openalex.org/I1331500379","display_name":"Centre for Development of Advanced Computing","ror":"https://ror.org/022abst40","country_code":"IN","type":"facility","lineage":["https://openalex.org/I1331500379","https://openalex.org/I4210121746"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Ramesh Naidu Laveti","raw_affiliation_strings":["C-DAC"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"C-DAC","institution_ids":["https://openalex.org/I1331500379"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11021","display_name":"ECG Monitoring and Analysis","score":0.9829000234603882,"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":0.9829000234603882,"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/T12303","display_name":"Tensor decomposition and applications","score":0.006899999920278788,"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/T10901","display_name":"Advanced Data Compression Techniques","score":0.005900000222027302,"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":[],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6272000074386597},{"id":"https://openalex.org/C81081738","wikidata":"https://www.wikidata.org/wiki/Q55542","display_name":"Lossless compression","level":3,"score":0.6261000037193298},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.5819000005722046},{"id":"https://openalex.org/C155512373","wikidata":"https://www.wikidata.org/wiki/Q287450","display_name":"Residual","level":2,"score":0.5644999742507935},{"id":"https://openalex.org/C47432892","wikidata":"https://www.wikidata.org/wiki/Q831390","display_name":"Wavelet","level":2,"score":0.5321999788284302},{"id":"https://openalex.org/C78548338","wikidata":"https://www.wikidata.org/wiki/Q2493","display_name":"Data compression","level":2,"score":0.5092999935150146},{"id":"https://openalex.org/C111030470","wikidata":"https://www.wikidata.org/wiki/Q1430460","display_name":"Curse of dimensionality","level":2,"score":0.4726000130176544},{"id":"https://openalex.org/C106301342","wikidata":"https://www.wikidata.org/wiki/Q4117933","display_name":"Entropy (arrow of time)","level":2,"score":0.4262999892234802},{"id":"https://openalex.org/C152124472","wikidata":"https://www.wikidata.org/wiki/Q1204361","display_name":"Redundancy (engineering)","level":2,"score":0.4180000126361847},{"id":"https://openalex.org/C155281189","wikidata":"https://www.wikidata.org/wiki/Q3518150","display_name":"Tensor (intrinsic definition)","level":2,"score":0.41339999437332153},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.41200000047683716},{"id":"https://openalex.org/C196216189","wikidata":"https://www.wikidata.org/wiki/Q2867","display_name":"Wavelet transform","level":3,"score":0.40610000491142273},{"id":"https://openalex.org/C104267543","wikidata":"https://www.wikidata.org/wiki/Q208163","display_name":"Signal processing","level":3,"score":0.4032000005245209},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3919999897480011},{"id":"https://openalex.org/C197424946","wikidata":"https://www.wikidata.org/wiki/Q1165717","display_name":"Waveform","level":3,"score":0.36480000615119934},{"id":"https://openalex.org/C109308471","wikidata":"https://www.wikidata.org/wiki/Q2046647","display_name":"Karhunen\u2013Lo\u00e8ve theorem","level":2,"score":0.3560999929904938},{"id":"https://openalex.org/C46286280","wikidata":"https://www.wikidata.org/wiki/Q2414958","display_name":"Discrete wavelet transform","level":4,"score":0.3531999886035919},{"id":"https://openalex.org/C189809214","wikidata":"https://www.wikidata.org/wiki/Q829522","display_name":"Beat (acoustics)","level":2,"score":0.34299999475479126},{"id":"https://openalex.org/C185429906","wikidata":"https://www.wikidata.org/wiki/Q1130160","display_name":"Estimator","level":2,"score":0.3424000144004822},{"id":"https://openalex.org/C121927907","wikidata":"https://www.wikidata.org/wiki/Q1952516","display_name":"Multiresolution analysis","level":5,"score":0.335099995136261},{"id":"https://openalex.org/C1769480","wikidata":"https://www.wikidata.org/wiki/Q1345239","display_name":"Entropy encoding","level":3,"score":0.31349998712539673},{"id":"https://openalex.org/C70958404","wikidata":"https://www.wikidata.org/wiki/Q7512728","display_name":"Signal reconstruction","level":4,"score":0.30959999561309814},{"id":"https://openalex.org/C25797200","wikidata":"https://www.wikidata.org/wiki/Q828137","display_name":"Compression ratio","level":3,"score":0.30390000343322754},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.2980000078678131},{"id":"https://openalex.org/C2221639","wikidata":"https://www.wikidata.org/wiki/Q2877","display_name":"Discrete cosine transform","level":3,"score":0.29760000109672546},{"id":"https://openalex.org/C186370098","wikidata":"https://www.wikidata.org/wiki/Q442787","display_name":"Energy (signal processing)","level":2,"score":0.2937999963760376},{"id":"https://openalex.org/C2778192920","wikidata":"https://www.wikidata.org/wiki/Q16874989","display_name":"Signal compression","level":4,"score":0.28949999809265137},{"id":"https://openalex.org/C5806529","wikidata":"https://www.wikidata.org/wiki/Q2365325","display_name":"Orthonormal basis","level":2,"score":0.2872999906539917},{"id":"https://openalex.org/C27438332","wikidata":"https://www.wikidata.org/wiki/Q2873","display_name":"Principal component analysis","level":2,"score":0.28450000286102295},{"id":"https://openalex.org/C163294075","wikidata":"https://www.wikidata.org/wiki/Q581861","display_name":"Noise reduction","level":2,"score":0.2842999994754791},{"id":"https://openalex.org/C28490314","wikidata":"https://www.wikidata.org/wiki/Q189436","display_name":"Speech recognition","level":1,"score":0.28110000491142273},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.2768999934196472},{"id":"https://openalex.org/C180016635","wikidata":"https://www.wikidata.org/wiki/Q2712821","display_name":"Compression (physics)","level":2,"score":0.27469998598098755},{"id":"https://openalex.org/C180205008","wikidata":"https://www.wikidata.org/wiki/Q159190","display_name":"Amplitude","level":2,"score":0.26010000705718994},{"id":"https://openalex.org/C70518039","wikidata":"https://www.wikidata.org/wiki/Q16000077","display_name":"Dimensionality reduction","level":2,"score":0.2524000108242035}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.21203/rs.3.rs-11396766/v1","doi":"https://doi.org/10.21203/rs.3.rs-11396766/v1","is_oa":true,"landing_page_url":"https://doi.org/10.21203/rs.3.rs-11396766/v1","pdf_url":null,"source":{"id":"https://openalex.org/S4306525896","display_name":"Research Square","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Research Square","raw_type":"posted-content"}],"best_oa_location":{"id":"doi:10.21203/rs.3.rs-11396766/v1","doi":"https://doi.org/10.21203/rs.3.rs-11396766/v1","is_oa":true,"landing_page_url":"https://doi.org/10.21203/rs.3.rs-11396766/v1","pdf_url":null,"source":{"id":"https://openalex.org/S4306525896","display_name":"Research Square","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Research Square","raw_type":"posted-content"},"sustainable_development_goals":[],"study_designs":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Abstract":[0],"The":[1],"rapid":[2],"growth":[3],"of":[4,41,87,124,214],"long-duration":[5],"ECG":[6,58],"recordings":[7],"from":[8,84],"wearable":[9],"and":[10,21,25,43,91,119,142,156,191,199,220,226],"ambulatory":[11],"monitors":[12],"has":[13],"created":[14],"a":[15,52,69,77,92,135,186,196],"compression":[16,212],"bottleneck":[17],"for":[18,56,173,193],"wireless":[19],"transmission":[20],"storage.":[22],"Existing":[23],"block-":[24],"transform-based":[26],"methods":[27],"largely":[28],"exploit":[29],"redundancy":[30],"along":[31],"only":[32],"one":[33],"or":[34],"two":[35],"dimensions,":[36],"offering":[37],"limited":[38],"joint":[39],"modeling":[40],"inter-beat":[42],"inter-lead":[44,81],"correlations.":[45,158],"TensorECG":[46],"addresses":[47],"this":[48],"gap":[49],"by":[50],"introducing":[51],"three-way":[53],"tensor":[54,137,146],"representation":[55],"multi-lead":[57],"that":[59],"extends":[60],"conventional":[61],"matrix-based":[62],"approaches":[63],"to":[64,106,110],"three":[65,129,200],"physiologically":[66],"meaningful":[67],"axes:":[68],"temporal":[70],"axis":[71,79,94],"capturing":[72,80,154],"self-similarity":[73],"across":[74,216],"successive":[75],"heartbeats,":[76],"spatial":[78],"correlations":[82],"arising":[83],"different":[85],"projections":[86],"cardiac":[88],"electrical":[89],"activity,":[90],"morphological":[93,126],"representing":[95],"individual":[96],"beat":[97,108,155],"waveform":[98],"shapes.":[99],"A":[100],"discrete":[101],"wavelet":[102],"transform":[103],"is":[104,147,182],"applied":[105],"each":[107],"prior":[109],"tensorization,":[111],"concentrating":[112],"signal":[113],"energy":[114],"into":[115],"few":[116],"significant":[117],"coefficients":[118,160],"reducing":[120],"the":[121,125,143,178],"effective":[122],"dimensionality":[123],"axis.":[127],"These":[128],"dimensions":[130],"are":[131,161],"jointly":[132,153],"represented":[133],"in":[134],"unified":[136],"rather":[138],"than":[139],"modeled":[140],"separately,":[141],"resulting":[144],"coefficient":[145],"compressed":[148],"via":[149],"rank-r":[150],"Tensor-Train":[151],"SVD,":[152],"lead":[157],"TT-core":[159],"entropy":[162],"coded,":[163],"with":[164],"an":[165],"optional":[166],"low-bit":[167],"residual-refinement":[168],"layer":[169],"entropy-coding":[170],"reconstruction":[171],"error":[172],"additional":[174],"rate\u2013fidelity":[175],"control.":[176],"TT-rank,":[177],"key":[179],"sensitive":[180],"hyperparameter,":[181],"tuned":[183],"once":[184],"on":[185,195],"35-record":[187],"MIT-BIH":[188],"training":[189],"split":[190,198],"fixed":[192],"testing":[194],"held-out":[197],"PhysioNet":[201],"databases":[202],"(2\u201315":[203],"leads,":[204],"250\u20131000":[205],"Hz).":[206],"An":[207],"adaptive-rank":[208],"DWT+TT":[209],"variant":[210],"achieves":[211],"ratios":[213],"18.9\u201351.6":[215],"4\u201312\\%":[217],"PRD":[218],"targets":[219],"clinical":[221],"validation":[222],"showing":[223],"sub-millivolt":[224],"amplitude":[225],"sub-millisecond":[227],"interval":[228],"errors":[229]},"counts_by_year":[],"updated_date":"2026-11-11T12:16:26.678958","created_date":"2026-11-11T00:00:00"}