Automated Localization of Myocardial Infarction From Vectorcardiographic via Tensor Decomposition.

Objective: Myocardial infarction (MI) causes rapid and permanent damage to the heart muscle. Therefore, it can deteriorate the myocardial structure and function if not timely diagnosed and treated. However, it is difficult to determine the precise localization of MI based on vectorcardiogram (VCG) due to the existing studies ignore the spatiotemporal features of VCG.

[ST segment morphological classification based on support vector machine multi feature fusion].

ST segment morphology is closely related to cardiovascular disease. It is used not only for characterizing different diseases, but also for predicting the severity of the disease. However, the short duration, low energy, variable morphology and interference from various noises make ST segment morphology classification a difficult task.

Localization of myocardial infarction with multi-lead ECG based on DenseNet.

Background And Objective: Myocardial infarction (MI) is a critical acute ischemic heart disease, which can be early diagnosed by electrocardiogram (ECG). However, the most research of MI localization pay more attention on the specific changes in every ECG lead independent. In our study, the research envisages the development of a novel multi-lead MI localization approach based on the densely connected convolutional network (DenseNet).

[Detection of inferior myocardial infarction based on densely connected convolutional neural network].

Inferior myocardial infarction is an acute ischemic heart disease with high mortality, which is easy to induce life-threatening complications such as arrhythmia, heart failure and cardiogenic shock. Therefore, it is of great clinical value to carry out accurate and efficient early diagnosis of inferior myocardial infarction. Electrocardiogram is the most sensitive means for early diagnosis of inferior myocardial infarction.

Automatic diagnosis of premature ventricular contraction based on Lyapunov exponents and LVQ neural network.

Premature ventricular contraction (PVC) is a common type of abnormal heartbeat. Without early diagnosis and proper treatment, PVC may result in serious harms. Diagnosis of PVC is of great importance in goal-directed treatment and preoperation prognosis.