Classification of myocardial infarction based on hybrid feature extraction and artificial intelligence tools by adopting tunable-Q wavelet transform (TQWT), variational mode decomposition (VMD) and neural networks

Highlights：

• We present a new pattern-recognition based method for detection of myocardial infarction.

• Myocardial infarction can be classified based on disparity of cardiac system dynamics and neural networks.

• Discriminative features can be extracted through novel feature extraction methods based on TQWT, VMD and PSR.

• Cardiac system dynamics can be modelled and identified by neural networks.

• We show good classification performance on the well-known and publicly available PTB database.