Classification of electrocardiogram and auscultatory blood pressure signals using machine learning models
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...
| Autori principali: | , , , , |
|---|---|
| Natura: | Articolo |
| Lingua: | English |
| Pubblicazione: |
PERGAMON-ELSEVIER SCIENCE LTD, THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND
2015
|
| Soggetti: | |
| Accesso online: | http://eprints.um.edu.my/13942/1/Classification_of_electrocardiogram_and_auscultatory_blood_pressure.pdf |
| _version_ | 1825720442054246400 |
|---|---|
| author | Seera, M. Lim, C.P. Liew, W.S. Lim, E. Loo, C.K. |
| author_facet | Seera, M. Lim, C.P. Liew, W.S. Lim, E. Loo, C.K. |
| author_sort | Seera, M. |
| collection | UM |
| description | 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. (C) 2014 Elsevier Ltd. All rights reserved. |
| first_indexed | 2024-03-06T05:35:12Z |
| format | Article |
| id | um.eprints-13942 |
| institution | Universiti Malaya |
| language | English |
| last_indexed | 2024-03-06T05:35:12Z |
| publishDate | 2015 |
| publisher | PERGAMON-ELSEVIER SCIENCE LTD, THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND |
| record_format | dspace |
| spelling | um.eprints-139422015-09-17T03:38:05Z http://eprints.um.edu.my/13942/ Classification of electrocardiogram and auscultatory blood pressure signals using machine learning models Seera, M. Lim, C.P. Liew, W.S. Lim, E. Loo, C.K. T Technology (General) TA Engineering (General). Civil engineering (General) 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. (C) 2014 Elsevier Ltd. All rights reserved. PERGAMON-ELSEVIER SCIENCE LTD, THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND 2015-05-01 Article PeerReviewed application/pdf en http://eprints.um.edu.my/13942/1/Classification_of_electrocardiogram_and_auscultatory_blood_pressure.pdf Seera, M. and Lim, C.P. and Liew, W.S. and Lim, E. and Loo, C.K. (2015) Classification of electrocardiogram and auscultatory blood pressure signals using machine learning models. Expert Systems with Applications, 42 (7). pp. 3643-3652. ISSN 0957-4174, DOI https://doi.org/10.1016/j.eswa.2014.12.023 <https://doi.org/10.1016/j.eswa.2014.12.023>. http://ac.els-cdn.com/S095741741400801X/1-s2.0-S095741741400801X-main.pdf?_tid=bacd29d8-e7cf-11e4-a4fa-00000aab0f6c&acdnat=1429584190_a9c034bfc0490d7fb60fc36edb602f9c DOI 10.1016/j.eswa.2014.12.023 |
| spellingShingle | T Technology (General) TA Engineering (General). Civil engineering (General) Seera, M. Lim, C.P. Liew, W.S. Lim, E. Loo, C.K. Classification of electrocardiogram and auscultatory blood pressure signals using machine learning models |
| title | Classification of electrocardiogram and auscultatory blood pressure signals using machine learning models |
| title_full | Classification of electrocardiogram and auscultatory blood pressure signals using machine learning models |
| title_fullStr | Classification of electrocardiogram and auscultatory blood pressure signals using machine learning models |
| title_full_unstemmed | Classification of electrocardiogram and auscultatory blood pressure signals using machine learning models |
| title_short | Classification of electrocardiogram and auscultatory blood pressure signals using machine learning models |
| title_sort | classification of electrocardiogram and auscultatory blood pressure signals using machine learning models |
| topic | T Technology (General) TA Engineering (General). Civil engineering (General) |
| url | http://eprints.um.edu.my/13942/1/Classification_of_electrocardiogram_and_auscultatory_blood_pressure.pdf |
| work_keys_str_mv | AT seeram classificationofelectrocardiogramandauscultatorybloodpressuresignalsusingmachinelearningmodels AT limcp classificationofelectrocardiogramandauscultatorybloodpressuresignalsusingmachinelearningmodels AT liewws classificationofelectrocardiogramandauscultatorybloodpressuresignalsusingmachinelearningmodels AT lime classificationofelectrocardiogramandauscultatorybloodpressuresignalsusingmachinelearningmodels AT loock classificationofelectrocardiogramandauscultatorybloodpressuresignalsusingmachinelearningmodels |