TY - GEN
T1 - Linear and Nonlinear Features for Myocardial Infarction Detection Using Support Vector Machine on 12-Lead ECG Recordings
AU - Arenas, Wilson J.
AU - Zequera, Martha L.
AU - Altuve, Miguel
AU - Sotelo, Silvia A.
N1 - Publisher Copyright:
© 2021, Springer Nature Switzerland AG.
PY - 2021
Y1 - 2021
N2 - The development of non-invasive techniques to assess cardiovascular risks has grown rapidly. In this sense, a multi-lead electrocardiogram (ECG) provides useful information to diagnose myocardial infarction (MI), the leading cause of death worldwide. In this paper we used a support vector machine (SVM) to detect MI by exploiting temporal, morphological and nonlinear features extracted from 12-lead ECG recording from the PTB Diagnostic ECG database. Temporal features correspond to QT, ST-T and RR intervals, morphological features were extracted from P and T waves, and QRS complexes, and nonlinear features correspond to the sample entropy of QT, ST-T and RR intervals. A 10-fold Monte Carlo cross-validation was implemented by randomly splitting the data set into training (70%) and test (30%) sets with balanced classes. Sensitivity of 97.33%, specificity of 96.67%, and accuracy of 97.00% were obtained by jointly exploiting temporal, morphological and nonlinear features by the SVM. The inclusion of entropy favors the detection of healthy control cases because the information of signal regularity improves the specificity of classification.
AB - The development of non-invasive techniques to assess cardiovascular risks has grown rapidly. In this sense, a multi-lead electrocardiogram (ECG) provides useful information to diagnose myocardial infarction (MI), the leading cause of death worldwide. In this paper we used a support vector machine (SVM) to detect MI by exploiting temporal, morphological and nonlinear features extracted from 12-lead ECG recording from the PTB Diagnostic ECG database. Temporal features correspond to QT, ST-T and RR intervals, morphological features were extracted from P and T waves, and QRS complexes, and nonlinear features correspond to the sample entropy of QT, ST-T and RR intervals. A 10-fold Monte Carlo cross-validation was implemented by randomly splitting the data set into training (70%) and test (30%) sets with balanced classes. Sensitivity of 97.33%, specificity of 96.67%, and accuracy of 97.00% were obtained by jointly exploiting temporal, morphological and nonlinear features by the SVM. The inclusion of entropy favors the detection of healthy control cases because the information of signal regularity improves the specificity of classification.
KW - Classification
KW - Entropy
KW - Myocardial infarction
KW - Support vector machine
UR - http://www.scopus.com/inward/record.url?scp=85097629515&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-64610-3_85
DO - 10.1007/978-3-030-64610-3_85
M3 - Conference contribution
AN - SCOPUS:85097629515
SN - 9783030646097
T3 - IFMBE Proceedings
SP - 758
EP - 766
BT - 8th European Medical and Biological Engineering Conference - Proceedings of the EMBEC 2020
A2 - Jarm, Tomaz
A2 - Cvetkoska, Aleksandra
A2 - Mahnič-Kalamiza, Samo
A2 - Miklavcic, Damijan
PB - Springer Science and Business Media Deutschland GmbH
T2 - 8th European Medical and Biological Engineering Conference, EMBEC 2020
Y2 - 29 November 2020 through 3 December 2020
ER -