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. METHODS: We developed a NYHA functional classification model for heart failure based on a deep learning method. We introduced an integrating attention mechanism based on the CNN-LSTM-SE model, segmenting the ECG signal into 2 to 20 s long segments. Ablation experiments showed that the 12 s ECG signal segments could be used with the proposed deep learning model for superior classification of heart failure. RESULTS: The accuracy, positive predictive value, sensitivity, and specificity of the NYHA functional classification method were 99.09, 98.9855, 99.033, and 99.649%, respectively. CONCLUSIONS: The comprehensive performance of this model exceeds similar methods and can be used to assist in clinical medical diagnoses.

[(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%). CONCLUSION: Exercise stress SPECT myocardial perfusion imaging could detect myocardial ischemia in patients with myocardial bridge and abnormal perfusion is positively related to the extent of systolic compression induced stenosis.