A self-supervised ECG forecasting model, trained only on normal signals, detects heartbeat and arrhythmia anomalies by measuring the error between its forecast and the real signal, achieving 83.84% anomaly accuracy and 85.46% normal accuracy on MIT-BIH Arrhythmia.
Title resolution pending
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
cs.CV 1years
2025 1verdicts
CONDITIONAL 1representative citing papers
citing papers explorer
-
FADE: Forecasting for Anomaly Detection on ECG
A self-supervised ECG forecasting model, trained only on normal signals, detects heartbeat and arrhythmia anomalies by measuring the error between its forecast and the real signal, achieving 83.84% anomaly accuracy and 85.46% normal accuracy on MIT-BIH Arrhythmia.