Matches in SemOpenAlex for { <https://semopenalex.org/work/W4313006586> ?p ?o ?g. }
Showing items 1 to 71 of
71
with 100 items per page.
- W4313006586 abstract "The most effective method used for the diagnosis of heart diseases is the Electrocardiogram (ECG). The shape of the ECG signal and the time interval between its various components gives useful details about any underlying heart disease. Any dysfunction of the heart is called as cardiac arrhythmia. The electrical impulses of the heart are blocked due to the cardiac arrhythmia called Bundle Branch Block (BBB) which can be observed as an irregular ECG wave. The BBB beats can indicate serious heart disease. The precise and quick detection of cardiac arrhythmias from the ECG signal can save lives and can also reduce the diagnostics cost. This study presents a machine learning technique for the automatic detection of BBB. In this method both morphological and statistical features were calculated from the ECG signals available in the standard MIT BIH database to classify them as normal, Left Bundle Branch Block (LBBB) and Right Bundle Branch Block (RBBB). ECG records in the MIT- BIH arrhythmia database containing Normal sinus rhythm, RBBB, and LBBB were used in the study. The suitability of the features extracted was evaluated using three classifiers, support vector machine, k-nearest neighbours and linear discriminant analysis. The accuracy of the technique is highly promising for all the three classifiers with k-nearest neighbours giving the highest accuracy of 98.2%. Since the ECG waveforms of patients with the same cardiac disorder is similar in shape, the proposed method is subject independent. The proposed technique is thus a reliable and simple method involving less computational complexity for the automatic detection of bundle branch block. This system can reduce the effort of cardiologists thereby enabling them to concentrate more on treatment of the patients." @default.
- W4313006586 created "2023-01-05" @default.
- W4313006586 creator A5025921882 @default.
- W4313006586 creator A5029780312 @default.
- W4313006586 creator A5089605838 @default.
- W4313006586 date "2022-08-14" @default.
- W4313006586 modified "2023-10-14" @default.
- W4313006586 title "Detection of Bundle Branch Blocks using Machine Learning Techniques" @default.
- W4313006586 doi "https://doi.org/10.52549/ijeei.v10i3.3852" @default.
- W4313006586 hasPublicationYear "2022" @default.
- W4313006586 type Work @default.
- W4313006586 citedByCount "0" @default.
- W4313006586 crossrefType "journal-article" @default.
- W4313006586 hasAuthorship W4313006586A5025921882 @default.
- W4313006586 hasAuthorship W4313006586A5029780312 @default.
- W4313006586 hasAuthorship W4313006586A5089605838 @default.
- W4313006586 hasBestOaLocation W43130065861 @default.
- W4313006586 hasConcept C111773187 @default.
- W4313006586 hasConcept C12267149 @default.
- W4313006586 hasConcept C153180895 @default.
- W4313006586 hasConcept C154945302 @default.
- W4313006586 hasConcept C164705383 @default.
- W4313006586 hasConcept C2524010 @default.
- W4313006586 hasConcept C2777210771 @default.
- W4313006586 hasConcept C2777233412 @default.
- W4313006586 hasConcept C2777473070 @default.
- W4313006586 hasConcept C2778198053 @default.
- W4313006586 hasConcept C2779161974 @default.
- W4313006586 hasConcept C2780040984 @default.
- W4313006586 hasConcept C2780350126 @default.
- W4313006586 hasConcept C2988455589 @default.
- W4313006586 hasConcept C33923547 @default.
- W4313006586 hasConcept C41008148 @default.
- W4313006586 hasConcept C69738355 @default.
- W4313006586 hasConcept C71924100 @default.
- W4313006586 hasConceptScore W4313006586C111773187 @default.
- W4313006586 hasConceptScore W4313006586C12267149 @default.
- W4313006586 hasConceptScore W4313006586C153180895 @default.
- W4313006586 hasConceptScore W4313006586C154945302 @default.
- W4313006586 hasConceptScore W4313006586C164705383 @default.
- W4313006586 hasConceptScore W4313006586C2524010 @default.
- W4313006586 hasConceptScore W4313006586C2777210771 @default.
- W4313006586 hasConceptScore W4313006586C2777233412 @default.
- W4313006586 hasConceptScore W4313006586C2777473070 @default.
- W4313006586 hasConceptScore W4313006586C2778198053 @default.
- W4313006586 hasConceptScore W4313006586C2779161974 @default.
- W4313006586 hasConceptScore W4313006586C2780040984 @default.
- W4313006586 hasConceptScore W4313006586C2780350126 @default.
- W4313006586 hasConceptScore W4313006586C2988455589 @default.
- W4313006586 hasConceptScore W4313006586C33923547 @default.
- W4313006586 hasConceptScore W4313006586C41008148 @default.
- W4313006586 hasConceptScore W4313006586C69738355 @default.
- W4313006586 hasConceptScore W4313006586C71924100 @default.
- W4313006586 hasIssue "3" @default.
- W4313006586 hasLocation W43130065861 @default.
- W4313006586 hasOpenAccess W4313006586 @default.
- W4313006586 hasPrimaryLocation W43130065861 @default.
- W4313006586 hasRelatedWork W1412556241 @default.
- W4313006586 hasRelatedWork W1982666179 @default.
- W4313006586 hasRelatedWork W2000930505 @default.
- W4313006586 hasRelatedWork W2038469892 @default.
- W4313006586 hasRelatedWork W2096912805 @default.
- W4313006586 hasRelatedWork W2136848994 @default.
- W4313006586 hasRelatedWork W2162152368 @default.
- W4313006586 hasRelatedWork W2380881116 @default.
- W4313006586 hasRelatedWork W4240796021 @default.
- W4313006586 hasRelatedWork W597432948 @default.
- W4313006586 hasVolume "10" @default.
- W4313006586 isParatext "false" @default.
- W4313006586 isRetracted "false" @default.
- W4313006586 workType "article" @default.