Model weights not public. Contact creators for more information.
Self-supervised representation-learning method for 12-lead ECG signals, designed to reduce reliance on large labeled datasets for downstream ECG classification. TSSL exploits two structural properties of ECG data: temporally, it encourages stable representations for the same individual across time while keeping different leads distinguishable; spatially, it enforces consistency in the relationships between signals and their representations across the different leads of a single recording. Evaluated on three public ECG datasets (CPSC2018, Chapman, PTB-XL), TSSL-pretrained models approached the performance of fully supervised training while using only about 10% of the labeled data.
Architecture
Hybrid
Self-supervised pretraining framework combining a temporal consistency objective (stable per-individual, per-lead representations across time) with a spatial consistency objective (cross-lead relational consistency) for 12-lead ECG representation learning
Added to catalog
2026-08-14
12-lead ECG multi-label arrhythmia-classification dataset released for the 2018 China Physiological Signal Challenge.
45,152 12-lead, 10-second ECGs from Chapman University / Shaoxing People's Hospital / Ningbo First Hospital with arrhythmia diagnoses; ~56% male / 44% female.
52% male / 48% female; age range 0-95 (median ~62). 21,837 10-second 12-lead ECG records.
Self-supervised ECG representation/embedding for downstream fine-tuning on ECG classification tasks (e.g. arrhythmia detection from CPSC2018/Chapman/PTB-XL)