  Portable, Wearable and Wireless electrocardiogram (ECG) Systems have the
potential to be used as point-of-care for cardiovascular disease diagnostic
systems. Such wearable and wireless ECG systems require automatic detection of
cardiovascular disease. Even in the primary care, automation of ECG diagnostic
systems will improve efficiency of ECG diagnosis and reduce the minimal
training requirement of local healthcare workers. However, few fully automatic
myocardial infarction (MI) disease detection algorithms have well been
developed. This paper presents a novel automatic MI classification algorithm
using second order ordinary differential equation (ODE) with time varying
coefficients, which simultaneously captures morphological and dynamic feature
of highly correlated ECG signals. By effectively estimating the unobserved
state variables and the parameters of the second order ODE, the accuracy of the
classification was significantly improved. The estimated time varying
coefficients of the second order ODE were used as an input to the support
vector machine (SVM) for the MI classification. The proposed method was applied
to the PTB diagnostic ECG database within Physionet. The overall sensitivity,
specificity, and classification accuracy of 12 lead ECGs for MI binary
classifications were 98.7%, 96.4% and 98.3%, respectively. We also found that
even using one lead ECG signals, we can reach accuracy as high as 97%.
Multiclass MI classification is a challenging task but the developed ODE
approach for 12 lead ECGs coupled with multiclass SVM reached 96.4% accuracy
for classifying 5 subgroups of MI and healthy controls.
