Heart failure classification using deep learning to extract spatiotemporal features from ECG.

Background: Heart failure is a syndrome with complex clinical manifestations. Due to increasing population aging, heart failure has become a major medical problem worldwide. In this study, we used the MIMIC-III public database to extract the temporal and spatial characteristics of electrocardiogram (ECG) signals from patients with heart failure.

[(99)Tc(m)-MIBI exercise stress single photon emission computed tomography myocardial perfusion imaging of patients with myocardial bridge].

Objective: To observe the exercise single photon emission computed tomography (SPECT) myocardial perfusion imaging of patients with myocardial bridge and assess the association between myocardial ischemia and extent of myocardial systolic compression.

Methods: Seventeen patients with myocardial bridge diagnosed by coronary angiogram were included and underwent exercise SPECT myocardial perfusion imaging.

Results: Abnormal SPECT perfusion imaging was evidenced in 12 out of 17 patients with myocardial bridge (2 out of 6 patients with systolic compression induced stenosis < 50%, 3 out of 4 patients with systolic compression induced stenosis between 50% - 75% and 7 out of 7 patients with the systolic compression induced stenosis between 75% - 100%).