FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W4207024335> ?p ?o ?g. }

• W4207024335 endingPage "e825" @default.
• W4207024335 startingPage "e825" @default.
• W4207024335 abstract "Electrocardiogram (ECG) signal classification plays a critical role in the automatic diagnosis of heart abnormalities. While most ECG signal patterns cannot be recognized by a human interpreter, they can be detected with precision using artificial intelligence approaches, making the ECG a powerful non-invasive biomarker. However, performing rapid and accurate ECG signal classification is difficult due to the low amplitude, complexity, and non-linearity. The widely-available deep learning (DL) method we propose has presented an opportunity to substantially improve the accuracy of automated ECG classification analysis using rhythm or beat features. Unfortunately, a comprehensive and general evaluation of the specific DL architecture for ECG analysis across a wide variety of rhythm and beat features has not been previously reported. Some previous studies have been concerned with detecting ECG class abnormalities only through rhythm or beat features separately.This study proposes a single architecture based on the DL method with one-dimensional convolutional neural network (1D-CNN) architecture, to automatically classify 24 patterns of ECG signals through both rhythm and beat. To validate the proposed model, five databases which consisted of nine-class of ECG-base rhythm and 15-class of ECG-based beat were used in this study. The proposed DL network was applied and studied with varying datasets with different frequency samplings in intra and inter-patient scheme.Using a 10-fold cross-validation scheme, the performance results had an accuracy of 99.98%, a sensitivity of 99.90%, a specificity of 99.89%, a precision of 99.90%, and an F1-score of 99.99% for ECG rhythm classification. Additionally, for ECG beat classification, the model obtained an accuracy of 99.87%, a sensitivity of 96.97%, a specificity of 99.89%, a precision of 92.23%, and an F1-score of 94.39%. In conclusion, this study provides clinicians with an advanced methodology for detecting and discriminating heart abnormalities between different ECG rhythm and beat assessments by using one outstanding proposed DL architecture." @default.
• W4207024335 created "2022-01-26" @default.
• W4207024335 creator A5003977561 @default.
• W4207024335 creator A5016885569 @default.
• W4207024335 creator A5026221046 @default.
• W4207024335 creator A5050287425 @default.
• W4207024335 creator A5056918819 @default.
• W4207024335 creator A5057982368 @default.
• W4207024335 creator A5059685921 @default.
• W4207024335 creator A5082623277 @default.
• W4207024335 date "2022-01-25" @default.
• W4207024335 modified "2023-09-26" @default.
• W4207024335 title "Deep learning-based electrocardiogram rhythm and beat features for heart abnormality classification" @default.
• W4207024335 cites W1964897957 @default.
• W4207024335 cites W1972902097 @default.
• W4207024335 cites W2047098613 @default.
• W4207024335 cites W2052873190 @default.
• W4207024335 cites W2086672934 @default.
• W4207024335 cites W2095409369 @default.
• W4207024335 cites W2112380741 @default.
• W4207024335 cites W2140779726 @default.
• W4207024335 cites W2157209869 @default.
• W4207024335 cites W2162800060 @default.
• W4207024335 cites W2166457531 @default.
• W4207024335 cites W2291961022 @default.
• W4207024335 cites W2473690925 @default.
• W4207024335 cites W2591436332 @default.
• W4207024335 cites W2702116941 @default.
• W4207024335 cites W2735213458 @default.
• W4207024335 cites W2765350348 @default.
• W4207024335 cites W2793062918 @default.
• W4207024335 cites W2795340004 @default.
• W4207024335 cites W2806806521 @default.
• W4207024335 cites W2888456553 @default.
• W4207024335 cites W2888673273 @default.
• W4207024335 cites W2889838428 @default.
• W4207024335 cites W2901226889 @default.
• W4207024335 cites W2902644322 @default.
• W4207024335 cites W2919115771 @default.
• W4207024335 cites W2944062336 @default.
• W4207024335 cites W2948995481 @default.
• W4207024335 cites W2963668841 @default.
• W4207024335 cites W2974415621 @default.
• W4207024335 cites W2979570803 @default.
• W4207024335 cites W2994403349 @default.
• W4207024335 cites W2994720754 @default.
• W4207024335 cites W3000744080 @default.
• W4207024335 cites W3001683732 @default.
• W4207024335 cites W3012755169 @default.
• W4207024335 cites W3028005831 @default.
• W4207024335 cites W3040780524 @default.
• W4207024335 cites W3083983389 @default.
• W4207024335 cites W3109676774 @default.
• W4207024335 cites W3125713641 @default.
• W4207024335 cites W3127640593 @default.
• W4207024335 cites W3127657277 @default.
• W4207024335 cites W3131121326 @default.
• W4207024335 cites W3174069785 @default.
• W4207024335 cites W779288015 @default.
• W4207024335 doi "https://doi.org/10.7717/peerj-cs.825" @default.
• W4207024335 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/35174263" @default.
• W4207024335 hasPublicationYear "2022" @default.
• W4207024335 type Work @default.
• W4207024335 citedByCount "7" @default.
• W4207024335 countsByYear W42070243352023 @default.
• W4207024335 crossrefType "journal-article" @default.
• W4207024335 hasAuthorship W4207024335A5003977561 @default.
• W4207024335 hasAuthorship W4207024335A5016885569 @default.
• W4207024335 hasAuthorship W4207024335A5026221046 @default.
• W4207024335 hasAuthorship W4207024335A5050287425 @default.
• W4207024335 hasAuthorship W4207024335A5056918819 @default.
• W4207024335 hasAuthorship W4207024335A5057982368 @default.
• W4207024335 hasAuthorship W4207024335A5059685921 @default.
• W4207024335 hasAuthorship W4207024335A5082623277 @default.
• W4207024335 hasBestOaLocation W42070243351 @default.
• W4207024335 hasConcept C108583219 @default.
• W4207024335 hasConcept C119857082 @default.
• W4207024335 hasConcept C121332964 @default.
• W4207024335 hasConcept C126322002 @default.
• W4207024335 hasConcept C135343436 @default.
• W4207024335 hasConcept C153180895 @default.
• W4207024335 hasConcept C154945302 @default.
• W4207024335 hasConcept C189809214 @default.
• W4207024335 hasConcept C24890656 @default.
• W4207024335 hasConcept C28490314 @default.
• W4207024335 hasConcept C41008148 @default.
• W4207024335 hasConcept C50644808 @default.
• W4207024335 hasConcept C71924100 @default.
• W4207024335 hasConcept C81363708 @default.
• W4207024335 hasConceptScore W4207024335C108583219 @default.
• W4207024335 hasConceptScore W4207024335C119857082 @default.
• W4207024335 hasConceptScore W4207024335C121332964 @default.
• W4207024335 hasConceptScore W4207024335C126322002 @default.
• W4207024335 hasConceptScore W4207024335C135343436 @default.
• W4207024335 hasConceptScore W4207024335C153180895 @default.
• W4207024335 hasConceptScore W4207024335C154945302 @default.
• W4207024335 hasConceptScore W4207024335C189809214 @default.
• W4207024335 hasConceptScore W4207024335C24890656 @default.

• next