Public articles linked to the same research event.
npj Digital Medicine This work introduces BenchECG, a standardized benchmark spanning eight public ECG datasets, 421,171 patients, 1,674,704 recordings, and ten tasks (classification, segmentation, detection, regression, survival analysis), used to evaluate public foundation models such as ST-MEM, ECG-JEPA, and ECGFounder; it also proposes xECG, a bidirectional xLSTM model pretrained with SimDINOv2 self-supervised learning, which achieves the best BenchECG score of 0.868±0.0030 (mean rank 1.50 under finetuning and 1.20 under linear probing), is the only public model to perform strongly across all datasets and task types, and leads on long-context tasks (sleep apnea AUROC 0.932±0.014; MIT-BIH arrhythmia F1 0.677±0.025) and computational efficiency (about 10x less time and about 7x less memory on PTB-XL).
This work introduces BenchECG, a standardized benchmark spanning eight public ECG datasets, 421,171 patients, 1,674,704 recordings, and ten tasks (classification, segmentation, detection, regression, survival analysis), used to evaluate public foundation models such as ST-MEM, ECG-JEPA, and ECGFounder; it also proposes xECG, a bidirectional xLSTM model pretrained with SimDINOv2 self-supervised learning, which achieves the best BenchECG score of 0.868±0.0030 (mean rank 1.50 under finetuning and 1.20 under linear probing), is the only public model to perform strongly across all datasets and task types, and leads on long-context tasks (sleep apnea AUROC 0.932±0.014; MIT-BIH arrhythmia F1 0.677±0.025) and computational efficiency (about 10x less time and about 7x less memory on PTB-XL).
This work introduces BenchECG, a standardized benchmark spanning eight public ECG datasets, 421,171 patients, 1,674,704 recordings, and ten tasks (classification, segmentation, detection, regression, survival analysis), used to evaluate public foundation models such as ST-MEM, ECG-JEPA, and ECGFounder; it also proposes xECG, a bidirectional xLSTM model pretrained with SimDINOv2 self-supervised learning, which achieves the best BenchECG score of 0.868±0.0030 (mean rank 1.50 under finetuning and 1.20 under linear probing), is the only public model to perform strongly across all datasets and task types, and leads on long-context tasks (sleep apnea AUROC 0.932±0.014; MIT-BIH arrhythmia F1 0.677±0.025) and computational efficiency (about 10x less time and about 7x less memory on PTB-XL).
This work introduces BenchECG, a standardized benchmark spanning eight public ECG datasets, 421,171 patients, 1,674,704 recordings, and ten tasks (classification, segmentation, detection, regression, survival analysis), used to evaluate public foundation models such as ST-MEM, ECG-JEPA, and ECGFounder; it also proposes xECG, a bidirectional xLSTM model pretrained with SimDINOv2 self-supervised learning, which achieves the best BenchECG score of 0.868±0.0030 (mean rank 1.50 under finetuning and 1.20 under linear probing), is the only public model to perform strongly across all datasets and task types, and leads on long-context tasks (sleep apnea AUROC 0.932±0.014; MIT-BIH arrhythmia F1 0.677±0.025) and computational efficiency (about 10x less time and about 7x less memory on PTB-XL).