Time-series ECG Imputation Using a Pattern-Based Masking Framework.

The utilization of continuous ECG monitoring has become an integral part of modern hospital-based care. However, missing data presents significant challenges in deploying real-time ECG-based predictive systems. Research on the implementation of imputation techniques on time-series ECG is limited. Furthermore, the performance of imputation techniques is typically benchmarked using random masking, which may not reflect the real-world missingness patterns encountered in clinical practice. This study aimed to evaluate and benchmark a range of imputation methods, from conventional statistical approaches to state-of-the-art deep learning models, using continuous ECG time-series data under different missingness conditions, including both random (conventional) and observed pattern-based (realistic) missingness. Time-domain features were extracted from continuous 12-lead Holter recordings (ranging from 2.5 to 4 hours per patient) from a pilot cohort of 40 patients. Missingness was introduced using random and pattern-based masking. We compared seven imputation methods: global mean, linear interpolation,