Transformer imputation in CTG: a length-dependent evaluation of reconstruction methods

Objective. Computer and artificial intelligence (AI) analyses are being increasingly used in intrapartum cardiotocography (CTG). However, fetal heart rate (FHR) signal loss, which frequently occurs in clinical practice, hinders visual interpretation and reduces accuracy. Although the impact is well recognized, there is no consensus on the maximum continuous gap length that can be reliably reconstructed under clinical conditions. Therefore, we aim to identify suitable imputation methods and clarify the clinical limits of valid missing-segment lengths. Methods. Using an open FHR dataset (CTU-UHB), we extracted continuous segments and artificially introduced Removed data of varying lengths. Using performance metrics such as difference and similarity, we compared the performance among a Transformer-based model and linear and spline interpolation. Additionally, we quantified the similarity between the Pre-impute and removed data to assess task difficulty. Results. We analyzed 2727 segments from 552 cases across multiple gap lengths. In terms of numerical accuracy root mean square error (RMSE), spline consistently performed significantly worse than others. The Transformer generally maintained a better mean accuracy than linear interpolation, although significant differences were observed only under specific conditions. Conversely, for waveform preservation (correlation), the Transformer consistently outperformed linear interpolation. Notably, in highly complex imputation tasks, the Transformer proved most robust, yielding the lowest RMSE and highest correlation. However, performance systematically degraded for all methods as gap lengths increased. Conclusion. The Transformer provides an effective baseline for FHR imputation under clinical conditions, achieving a favorable balance between waveform and numerical accuracy. By clarifying the clinical limits of valid missing-segment lengths—specifically the decline in reliability beyond 30 s—this study provides guidance for standardizing preprocessing in future CTG AI research and clinical implementation. Significance. For imputing intrapartum FHR data, the Transformer generally improves waveform reproducibility over linear interpolation for short-to-moderate gaps. Defining the reliability limit provides a crucial baseline for future CTG AI.