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Classification of electrocardiogram and auscultatory blood pressure signals using machine learning models
journal contribution
posted on 2015-05-01, 00:00 authored by M Seera, Chee Peng LimChee Peng Lim, W S Liew, E Lim, C K LooIn this paper, two real-world medical classification problems using electrocardiogram (ECG) and auscultatory blood pressure (Korotkoff) signals are examined. A total of nine machine learning models are applied to perform classification of the medical data sets. A number of useful performance metrics which include accuracy, sensitivity, specificity, as well as the area under the receiver operating characteristic curve are computed. In addition to the original data sets, noisy data sets are generated to evaluate the robustness of the classifiers against noise. The 10-fold cross validation method is used to compute the performance statistics, in order to ensure statistically reliable results pertaining to classification of the ECG and Korotkoff signals are produced. The outcomes indicate that while logistic regression models perform the best with the original data set, ensemble machine learning models achieve good accuracy rates with noisy data sets.
History
Journal
Expert systems with applicationsVolume
42Issue
7Pagination
3643 - 3652Publisher
ElsevierLocation
Amsterdam, The NetherlandsPublisher DOI
ISSN
0957-4174Language
engPublication classification
C Journal article; C1 Refereed article in a scholarly journalCopyright notice
2015, ElsevierUsage metrics
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No categories selectedKeywords
Auscultatory blood pressureData classificationElectrocardiogramMachine learningMedical signalsScience & TechnologyTechnologyComputer Science, Artificial IntelligenceEngineering, Electrical & ElectronicOperations Research & Management ScienceComputer ScienceEngineeringSUPPORT VECTOR MACHINESNEURAL-NETWORK MODELARRHYTHMIA CLASSIFICATIONECG ARRHYTHMIACOMPARING PERFORMANCESLOGISTIC-REGRESSIONBIOMEDICAL SIGNALSWAVELET TRANSFORMALGORITHMSELECTION
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