TY - JOUR
T1 - Identification of myocardial infarction (MI) using spatio-temporal heart dynamics
AU - Yang, Hui
AU - Bukkapatnam, Satish T.S.
AU - Le, Trung
AU - Komanduri, Ranga
N1 - Funding Information:
The authors would like to thank the National Science Foundation ( CMMI-0729552 and CMMI-0830023 ) as well as OSU/Tulsa Center for Health Sciences (CHS) (Dr. J. Hess and Dr. B. Benjamin) for the support of the research. One of the authors (RK) also wishes to thank A.H. Nelson Jr. Endowed Chair in Engineering for additional financial support. Thanks are also due to Mrs. Sharon Green for excellent editorial assistance. Conflict of interest statement
PY - 2012/5
Y1 - 2012/5
N2 - Cardiovascular disorders, such as myocardial infarction (MI) are the leading causes of mortality in the world. This paper presents an approach that uses novel spatio-temporal patterns of the vectorcardiogram (VCG) signals for the identification of various types of MI. In contrast to the traditional electrocardiogram (ECG) approaches, the 3D cardiac VCG signal is partitioned into 8 octants for localized analysis of the heart's electrical activities. The proposed method was tested using the PhysioNet PTB database for 368 MIs and 80 healthy control (HC) recordings, each of which includes 12-lead ECG and 3-lead VCG. Significant differences are found in the VCG spatial distribution between MI and HC groups. Furthermore, classification and regression tree (CART) analysis was used to demonstrate that VCG octant features can distinguish MIs from HCs with a sensitivity (accuracy of MI identification) of 97.28% and a specificity (accuracy of HC identification) of 95.00%, which is promising compared to the previously reported results using other ECG databases. The results indicate that the present approach provides an effective way for monitoring, post-processing, and interpretation of ECG data, and hopefully can impact the current cardiac diagnostic practice.
AB - Cardiovascular disorders, such as myocardial infarction (MI) are the leading causes of mortality in the world. This paper presents an approach that uses novel spatio-temporal patterns of the vectorcardiogram (VCG) signals for the identification of various types of MI. In contrast to the traditional electrocardiogram (ECG) approaches, the 3D cardiac VCG signal is partitioned into 8 octants for localized analysis of the heart's electrical activities. The proposed method was tested using the PhysioNet PTB database for 368 MIs and 80 healthy control (HC) recordings, each of which includes 12-lead ECG and 3-lead VCG. Significant differences are found in the VCG spatial distribution between MI and HC groups. Furthermore, classification and regression tree (CART) analysis was used to demonstrate that VCG octant features can distinguish MIs from HCs with a sensitivity (accuracy of MI identification) of 97.28% and a specificity (accuracy of HC identification) of 95.00%, which is promising compared to the previously reported results using other ECG databases. The results indicate that the present approach provides an effective way for monitoring, post-processing, and interpretation of ECG data, and hopefully can impact the current cardiac diagnostic practice.
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U2 - 10.1016/j.medengphy.2011.08.009
DO - 10.1016/j.medengphy.2011.08.009
M3 - Article
C2 - 21940193
AN - SCOPUS:84859777131
SN - 1350-4533
VL - 34
SP - 485
EP - 497
JO - Medical Engineering and Physics
JF - Medical Engineering and Physics
IS - 4
ER -