# PRISM-CTG: A Foundation Model for Cardiotocography Analysis with Multi-View SSL

arXiv: https://arxiv.org/abs/2605.02917 · HTML: https://mlsanity.com/paper/2605.02917/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 23 of its 44 references that are in the map.
- First posted on arXiv 9 Apr 2026.

## Builds on (most cited first, 23 of 23)

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- [Large Brain Model for Learning Generic Representations with Tremendous EEG Data in BCI](https://mlsanity.com/paper/2405.18765/) (2024)
- [Self-supervised Learning with Random-projection Quantizer for Speech Recognition](https://mlsanity.com/paper/2202.01855/) (2022)
- [PaPaGei: Open Foundation Models for Optical Physiological Signals](https://mlsanity.com/paper/2410.20542/) (2024)
- [EEGFormer: Towards Transferable and Interpretable Large-Scale EEG Foundation Model](https://mlsanity.com/paper/2401.10278/) (2024)
- [SleepFM: Multi-modal Representation Learning for Sleep Across Brain Activity, ECG and Respiratory Signals](https://mlsanity.com/paper/2405.17766/) (2024)
- [ECG Semantic Integrator (ESI): A Foundation ECG Model Pretrained with LLM-Enhanced Cardiological Text](https://mlsanity.com/paper/2405.19366/) (2024)
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- [From Token to Rhythm: A Multi-Scale Approach for ECG-Language Pretraining](https://mlsanity.com/paper/2506.21803/) (2025)
- [AnyPPG: An ECG-Guided PPG Foundation Model Trained on Over 100,000 Hours of Recordings for Holistic Health Profiling](https://mlsanity.com/paper/2511.01747/) (2025)
- [In Pursuit of Pixel Supervision for Visual Pre-training](https://mlsanity.com/paper/2512.15715/) (2025)
- [Scaling Representation Learning from Ubiquitous ECG with State-Space Models](https://mlsanity.com/paper/2309.15292/) (2023)
- [CLEF: Clinically-Guided Contrastive Learning for Electrocardiogram Foundation Models](https://mlsanity.com/paper/2512.02180/) (2025)
- [PatchCTG: Patch Cardiotocography Transformer for Antepartum Fetal Health Monitoring](https://mlsanity.com/paper/2411.07796/) (2024)
- [The OxMat dataset: a multimodal resource for the development of AI-driven technologies in maternal and newborn child health](https://mlsanity.com/paper/2404.08024/) (2024)
- [ALFEE: Adaptive Large Foundation Model for EEG Representation](https://mlsanity.com/paper/2505.06291/) (2025)
- [TolerantECG: A Foundation Model for Imperfect Electrocardiogram](https://mlsanity.com/paper/2507.09887/) (2025)

## Abstract (arXiv)

Supervised deep learning models for automated CTG analysis are typically constrained by narrowly curated labelled datasets and limited patient cohorts, leaving substantial volumes of physiologically informative clinical recordings untapped. To address this limitation, we propose Physiology-aware Representation Learning via Integrated Self-supervision and Metadata for CTG (PRISM-CTG), a clinically grounded self-supervised foundation model (FM) for CTG that leverages large-scale unlabelled recordings to learn transferable domain-level representations. PRISM-CTG is pretrained using a multi-view self-supervised framework that jointly optimises 3 complementary pretext objectives: random-projected guided masked signal reconstruction, clinical variable prediction, and feature classification. Each objective is associated with a dedicated task-specific token, enabling specialised representation learning, while controlled cross-attention facilitates information exchange across clinical context. By reframing patient metadata and domain knowledge, which are often underutilised in conventional training as prediction targets, Prism-CTG transforms readily available clinical information into additional supervisory targets that guide clinically meaningful representation learning. Extensive experiments across 7 downstream CTG tasks in both antepartum and intrapartum domains demonstrated that PRISM-CTG consistently outperforms in-domain and SSL baselines. Notably, PRISM-CTG demonstrated strong generalisation under external validation on 2 datasets, while achieving comparable performance to studies trained on substantially larger, privately labelled datasets. To our knowledge, this is the first study to introduce large-scale FM for CTG that learns domain-level representations.