Automated detection of shockable ECG signals: A review
Version 2 2024-06-05, 12:06
Version 1 2021-01-01, 00:00
journal contribution
posted on 2024-06-05, 12:06 authored by M Hammad, RNVPS Kandala, A Abdelatey, M Abdar, M Zomorodi‐Moghadam, RS Tan, UR Acharya, J Pławiak, R Tadeusiewicz, V Makarenkov, N Sarrafzadegan, Abbas KhosraviAbbas Khosravi, S Nahavandi, AAA EL-Latif, P PławiakAutomated detection of shockable ECG signals: A review
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Location
Amsterdam, The NetherlandsLanguage
EnglishPublication classification
C1 Refereed article in a scholarly journalJournal
Information SciencesVolume
571Pagination
580-604ISSN
0020-0255eISSN
1872-6291Publisher
ElsevierUsage metrics
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No categories selectedKeywords
ALGORITHMArrhythmiaATRIAL-FIBRILLATIONCLASSIFICATIONComputer ScienceComputer Science, Information SystemsComputer-aided arrhythmia classification (CAAC)CONVOLUTION NEURAL-NETWORKDeep learningDEEP LEARNING APPROACHDIAGNOSISElectrocardiogram (ECG)Ensemble learningFeature extractionFeature selectionMachine learningMODELOptimizationREAL-TIME DETECTIONRECURRENCE PLOTSScience & TechnologySignal processingTechnologyTHREATENING VENTRICULAR-ARRHYTHMIAS4003 Biomedical engineering
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