Classification of electrocardiogram and auscultatory blood pressure signals using machine learning models

Manjeevan Seera, Chee Peng Lim, Wei Shiung Liew, Einly Lim, Chu Kiong Loo

Research output: Contribution to journalArticleResearchpeer-review

18 Citations (Scopus)

Abstract

In 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.

Original languageEnglish
Pages (from-to)3643-3652
Number of pages10
JournalExpert Systems with Applications
Volume42
Issue number7
DOIs
Publication statusPublished - 1 May 2015
Externally publishedYes

Keywords

  • Auscultatory blood pressure
  • Data classification
  • Electrocardiogram
  • Machine learning
  • Medical signals

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