TY - GEN
T1 - A Hybrid System for Myocardial Infarction Classification with Derived Vectorcardiography
AU - Chuang, Yu Hung
AU - Lee, Ching Yu
AU - Chen, Yin Husan
AU - Chang, Wen Whei
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2023
Y1 - 2023
N2 - The 12-lead electrocardiography (ECG) remains the most rapid and widely used diagnostic test for patients with myocardial infarction (MI). Most wearable ECG devices only provide single limb-lead measurement, limiting their practical applicability for MI diagnosis. The ability to transform from single-lead ECG to 3-lead vectorcardiography (VCG) enables wider use of wearable devices in clinical diagnostics. This study presents a patient-specific transformation for VCG synthesis using temporal convolutional networks in variational mode decomposition domain. MI-induced changes in morphological and temporal wave features are extracted from the derived VCG via spline curve approximation. After feature extraction, a multilayer perceptron classifier is used to classify different types of MI. Experiments on the PTB diagnostic database show that the proposed system achieves satisfactory performance in differentiating MI patients from healthy subjects and localizing infarcted area.
AB - The 12-lead electrocardiography (ECG) remains the most rapid and widely used diagnostic test for patients with myocardial infarction (MI). Most wearable ECG devices only provide single limb-lead measurement, limiting their practical applicability for MI diagnosis. The ability to transform from single-lead ECG to 3-lead vectorcardiography (VCG) enables wider use of wearable devices in clinical diagnostics. This study presents a patient-specific transformation for VCG synthesis using temporal convolutional networks in variational mode decomposition domain. MI-induced changes in morphological and temporal wave features are extracted from the derived VCG via spline curve approximation. After feature extraction, a multilayer perceptron classifier is used to classify different types of MI. Experiments on the PTB diagnostic database show that the proposed system achieves satisfactory performance in differentiating MI patients from healthy subjects and localizing infarcted area.
KW - myocardial infarction
KW - spline curve fitting
KW - temporal convolutional network
KW - variational mode decomposition
KW - vectorcardiography
UR - http://www.scopus.com/inward/record.url?scp=85169290824&partnerID=8YFLogxK
U2 - 10.1109/ICUFN57995.2023.10200755
DO - 10.1109/ICUFN57995.2023.10200755
M3 - Conference contribution
AN - SCOPUS:85169290824
T3 - International Conference on Ubiquitous and Future Networks, ICUFN
SP - 468
EP - 473
BT - ICUFN 2023 - 14th International Conference on Ubiquitous and Future Networks
PB - IEEE Computer Society
T2 - 14th International Conference on Ubiquitous and Future Networks, ICUFN 2023
Y2 - 4 July 2023 through 7 July 2023
ER -