TF-C Pretrain ECG

- Paper: Self-Supervised Contrastive Pre-Training For Time Series via Time-Frequency Consistency - Paper link: - Github repo: https://github.com/mims-harvard/TFC-pretraining - Project website: <br> <strong>ECG</strong> is taken as a subset from the 2017 PhysioNet Challenge that focuses on ECG recording classification. The single lead ECG measures four different underlying conditions of cardiac arrhythmias. More specifically, these classes correspond to the recordings of normal sinus rhythm, atrial fibrillation (AF), alternative rhythm, or others (too noisy to be classified). The recordings are sampled at 300 Hz. Furthermore, the dataset is imbalanced, with much fewer samples from the atrial fibrillation and noisy classes out of all four. To preprocess the dataset, we use the code from the CLOCS paper, which applied fixed-length window of 1,500 observations to divide up the long recordings into short samples of 5 seconds in duration that is still physiologically meaningful.