Kurtograms of different signals.
<p>(A) Kurtogram of signal 0. (B) Kurtogram of signal 1. (C) Kurtogram of signal 2. X-axis represents the frequency bands, Y-axis represents the resolution levels, color intensity represents the kurtosis value (higher intensity indicates higher kurtosis). Kurtograms exhibit similar impulses co...
محفوظ في:
| المؤلف الرئيسي: | |
|---|---|
| مؤلفون آخرون: | |
| منشور في: |
2025
|
| الموضوعات: | |
| الوسوم: |
إضافة وسم
لا توجد وسوم, كن أول من يضع وسما على هذه التسجيلة!
|
| _version_ | 1852020393452240896 |
|---|---|
| author | Hang Zhao (143592) |
| author2 | Xiongfei Yin (21368130) |
| author2_role | author |
| author_facet | Hang Zhao (143592) Xiongfei Yin (21368130) |
| author_role | author |
| dc.creator.none.fl_str_mv | Hang Zhao (143592) Xiongfei Yin (21368130) |
| dc.date.none.fl_str_mv | 2025-05-15T15:01:35Z |
| dc.identifier.none.fl_str_mv | 10.1371/journal.pone.0321484.g008 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/figure/Kurtograms_of_different_signals_/29075244 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Physiology Biotechnology Ecology Space Science Biological Sciences not elsewhere classified Mathematical Sciences not elsewhere classified Information Systems not elsewhere classified variational mode decomposition signal processing fields pareto optimal front ablation study evaluated 200 representative points shown significant promise ecg signal processing crayfish optimization algorithm bih arrhythmia database finite element model simulate cardiac electrophysiology div >< p ecg signal classification deep attention modules attention network based deep attention model proposed deep vmd generated using mocoa attention network cardiac electrophysiology deep model arrhythmia classification proposed model deep vmd attention modeling significant anomalies lstm modules ecg signals ecg data bayesian optimization model based increasingly based classification strategy arrhythmia characterized world mit vmd achieves vmd ), two types spectral kurtosis recent research often neglect mocoa ). mathematical foundations kl divergence key parameters k </ human heart highest accuracy driven approaches dominated sorting |
| dc.title.none.fl_str_mv | Kurtograms of different signals. |
| dc.type.none.fl_str_mv | Image Figure info:eu-repo/semantics/publishedVersion image |
| description | <p>(A) Kurtogram of signal 0. (B) Kurtogram of signal 1. (C) Kurtogram of signal 2. X-axis represents the frequency bands, Y-axis represents the resolution levels, color intensity represents the kurtosis value (higher intensity indicates higher kurtosis). Kurtograms exhibit similar impulses components but differentiated characteristics of the kurtosis values across three signals.</p> |
| eu_rights_str_mv | openAccess |
| id | Manara_c076fe8cb47a6739ff553aba994f9d4e |
| identifier_str_mv | 10.1371/journal.pone.0321484.g008 |
| network_acronym_str | Manara |
| network_name_str | ManaraRepo |
| oai_identifier_str | oai:figshare.com:article/29075244 |
| publishDate | 2025 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | Kurtograms of different signals.Hang Zhao (143592)Xiongfei Yin (21368130)PhysiologyBiotechnologyEcologySpace ScienceBiological Sciences not elsewhere classifiedMathematical Sciences not elsewhere classifiedInformation Systems not elsewhere classifiedvariational mode decompositionsignal processing fieldspareto optimal frontablation study evaluated200 representative pointsshown significant promiseecg signal processingcrayfish optimization algorithmbih arrhythmia databasefinite element modelsimulate cardiac electrophysiologydiv >< pecg signal classificationdeep attention modulesattention network baseddeep attention modelproposed deep vmdgenerated using mocoaattention networkcardiac electrophysiologydeep modelarrhythmia classificationproposed modeldeep vmdattention modelingsignificant anomalieslstm modulesecg signalsecg databayesian optimizationmodel basedincreasingly basedclassification strategyarrhythmia characterizedworld mitvmd achievesvmd ),two typesspectral kurtosisrecent researchoften neglectmocoa ).mathematical foundationskl divergencekey parametersk </human hearthighest accuracydriven approachesdominated sorting<p>(A) Kurtogram of signal 0. (B) Kurtogram of signal 1. (C) Kurtogram of signal 2. X-axis represents the frequency bands, Y-axis represents the resolution levels, color intensity represents the kurtosis value (higher intensity indicates higher kurtosis). Kurtograms exhibit similar impulses components but differentiated characteristics of the kurtosis values across three signals.</p>2025-05-15T15:01:35ZImageFigureinfo:eu-repo/semantics/publishedVersionimage10.1371/journal.pone.0321484.g008https://figshare.com/articles/figure/Kurtograms_of_different_signals_/29075244CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/290752442025-05-15T15:01:35Z |
| spellingShingle | Kurtograms of different signals. Hang Zhao (143592) Physiology Biotechnology Ecology Space Science Biological Sciences not elsewhere classified Mathematical Sciences not elsewhere classified Information Systems not elsewhere classified variational mode decomposition signal processing fields pareto optimal front ablation study evaluated 200 representative points shown significant promise ecg signal processing crayfish optimization algorithm bih arrhythmia database finite element model simulate cardiac electrophysiology div >< p ecg signal classification deep attention modules attention network based deep attention model proposed deep vmd generated using mocoa attention network cardiac electrophysiology deep model arrhythmia classification proposed model deep vmd attention modeling significant anomalies lstm modules ecg signals ecg data bayesian optimization model based increasingly based classification strategy arrhythmia characterized world mit vmd achieves vmd ), two types spectral kurtosis recent research often neglect mocoa ). mathematical foundations kl divergence key parameters k </ human heart highest accuracy driven approaches dominated sorting |
| status_str | publishedVersion |
| title | Kurtograms of different signals. |
| title_full | Kurtograms of different signals. |
| title_fullStr | Kurtograms of different signals. |
| title_full_unstemmed | Kurtograms of different signals. |
| title_short | Kurtograms of different signals. |
| title_sort | Kurtograms of different signals. |
| topic | Physiology Biotechnology Ecology Space Science Biological Sciences not elsewhere classified Mathematical Sciences not elsewhere classified Information Systems not elsewhere classified variational mode decomposition signal processing fields pareto optimal front ablation study evaluated 200 representative points shown significant promise ecg signal processing crayfish optimization algorithm bih arrhythmia database finite element model simulate cardiac electrophysiology div >< p ecg signal classification deep attention modules attention network based deep attention model proposed deep vmd generated using mocoa attention network cardiac electrophysiology deep model arrhythmia classification proposed model deep vmd attention modeling significant anomalies lstm modules ecg signals ecg data bayesian optimization model based increasingly based classification strategy arrhythmia characterized world mit vmd achieves vmd ), two types spectral kurtosis recent research often neglect mocoa ). mathematical foundations kl divergence key parameters k </ human heart highest accuracy driven approaches dominated sorting |