PAPERS.md β PhysioJEPA Reference Index
Oz Labs β April 2026 Covers every paper referenced across the full conversation and all project documents.
How to use this file
Three things per entry:
- What to use it for β the specific task or decision the agent needs this paper for
- Key numbers β exact figures the agent must not get wrong in code or prose
- Location β where to fetch the PDF
Read the tier before writing any code in that tier's domain. Do not cite a number that isn't in this file without fetching the source first.
Tier 1 β Implement from these
Read before writing any training code. Contains exact equations, hyperparameters, architecture details.
[T1-1] Weimann & Conrad β ECG-JEPA
arXiv: 2410.13867 Β· arxiv.org/pdf/2410.13867
Code: github.com/kweimann/ECG-JEPA β fork this
Use for: This is the codebase we fork. Before writing any encoder code, read Section 2 (architecture), Section 3 (data), Appendix A (hyperparameters).
- Patch tokenisation: 2D over (12 leads Γ time), patch size = 25 time steps at 500 Hz
- Masking: multi-block contiguous, 50% ratio, 4 target blocks
- EMA: Ο starts 0.996, cosine-annealed to 0.9999 over training
- Loss: L1 in latent space β no pixel decoder
- ViT-S: 12 layers, d=256, 8 heads, MLP ratio=4
Key numbers: PTB-XL all-statements AUC 0.945 β this is Baseline A in the experiment matrix. Training time ~26h on RTX 3090.
[T1-2] Assran et al. β I-JEPA
arXiv: 2301.08243 Β· arxiv.org/pdf/2301.08243
Code: github.com/facebookresearch/ijepa
Use for: The masking strategy foundation. Why multi-block contiguous > random masking (forces semantic prediction, not texture interpolation). The stop-gradient / EMA target encoder design justification. The predictor should be narrower than the encoder β this prevents shortcutting through the predictor.
Key numbers: ViT-H/14 ImageNet β scale reference only, not a target for us.
[T1-3] Bardes et al. β V-JEPA (Revisiting Feature Prediction)
arXiv: 2404.08471 Β· arxiv.org/pdf/2404.08471
Use for: Spatiotemporal tube masking β how to mask contiguous blocks across both spatial and temporal axes simultaneously. Template for PPG 1D+time representation. Two-encoder EMA recipe at scale. Why predicting in latent space beats pixel reconstruction for noisy signals β core justification for JEPA over MAE.
Key numbers: SSv2 top-1 77.3%.
[T1-4] Balestriero & LeCun β LeJEPA
arXiv: 2511.08544 Β· arxiv.org/pdf/2511.08544
Use for: Ablation A3 only (SIGReg). Do not implement SIGReg without reading this first.
- Theorem 1: isotropic Gaussian is the optimal JEPA embedding distribution
- SIGReg: K=128 random 1D projections w~N(0,I), KL(zΒ·w || N(0,1)) per projection, sum. O(Kd).
- Ξ» range: [0.01, 0.1]; start at 0.05
- Apply to pooled global representation only β not per-patch tokens
- ~50 lines of PyTorch
Key numbers: 79% ImageNet ViT-H/14 with only 2 loss terms.
[T1-5] Kim β CroPA-ECG-JEPA
arXiv: 2410.08559 Β· arxiv.org/pdf/2410.08559
Code: github.com/sehunfromdaegu/ECG_JEPA
Use for: Second ECG-JEPA implementation for debugging. Cross-Pattern Attention (CroPA) = inter-lead masked attention = inspiration for cardiac phase encoding in ablation A2. Also: 1D PE for predictor vs 2D for encoders β different from Weimann, compare before finalising.
Key numbers: Recovers HR and QRS duration from frozen representations without supervised training β target behaviour for PTT.
[T1-6] Botman et al. β Laya (LeJEPA for EEG)
arXiv: 2603.16281 Β· arxiv.org/pdf/2603.16281
Use for: Most direct prior to PhysioJEPA. Read before implementing ablation A3.
- SIGReg with aggressive Ξ» destabilises training on impulsive signals (QRS-like spikes in EEG)
- Mitigation: lower Ξ» (0.001β0.01), aggressive gradient clipping, apply to pooled global rep only
- Latent prediction outperforms reconstruction on EEG clinical tasks
Key numbers: Outperforms reconstruction baselines on EEG-Bench with 10% of pretraining data.
Tier 2 β Baseline numbers and comparisons
Read to correctly report comparison numbers. Getting baselines wrong is a rejection risk.
[T2-1] Nie et al. β AnyPPG
arXiv: 2511.01747 Β· arxiv.org/pdf/2511.01747
Use for: Primary contrastive baseline (Baseline C in experiment matrix).
- Exact loss: symmetric InfoNCE with learnable temperature Ο
- CRITICAL: ECGFounder encoder is FROZEN during AnyPPG training. ECG is a fixed supervisory signal. AnyPPG is not a jointly trained dual-encoder model.
- Architecture: Net1D (PPG branch), ECGFounder frozen (ECG branch)
- Trained on >100,000 hours
Key numbers: PPGβECG retrieval R@1=0.736, R@5=0.906, R@10=0.935. AF detection AUC ~0.90. Mean 9.1% AUC improvement over non-ECG-guided baselines.
[T2-2] Wagner et al. β PTB-XL
arXiv: 2004.13701 Β· arxiv.org/pdf/2004.13701
Use for: ECG evaluation benchmark. Task definitions, train/test/val splits, and label hierarchy. Must replicate Weimann's exact split for comparison.
Key numbers: Weimann ECG-JEPA AUC 0.945 all-statements = Baseline A target.
[T2-3] Charlton et al. β Towards Ubiquitous BP Monitoring via PTT (review)
URL: pmc.ncbi.nlm.nih.gov/articles/PMC4515215/
Use for: Before writing E4 rollout coherence physiological consistency checks. PTT definition, normal range, PTTβBP and HRβPTT relationships. Per-patient calibration required for absolute BP β do not claim uncalibrated absolute BP from PTT.
Key numbers: Normal PTT 100β400ms (ICU adults). Within-patient tracking ~10 mmHg MAE with calibration.
[T2-4] Assran et al. β V-JEPA 2 (including V-JEPA 2-AC)
arXiv: 2506.09985 Β· arxiv.org/pdf/2506.09985
Use for: Architecture D future work template. Two-stage recipe: action-free pretraining β action-conditioned fine-tuning with frozen encoder.
Key numbers: <62 hours of robot interaction data for Stage 2. SSv2 top-1 77.3%.
Tier 3 β Related work framing
Read to correctly describe prior work and differentiate PhysioJEPA.
[T3-1] Sarkar & Etemad β CardioGAN
arXiv: 2010.00104 Β· arxiv.org/pdf/2010.00104
Code: github.com/pritamqu/ppg2ecg-cardiogan
Use for: First major cross-modal ECG-PPG paper (AAAI 2021).
- Uses CycleGAN backbone with attention-based generators and dual time/frequency discriminators
- NOT reconstruction/L1, NOT InfoNCE β adversarial + cycle consistency loss
- t=0 alignment β discards lag. Do NOT call this "pixel reconstruction."
[T3-2] Liu, Wang & Wang β TSTA-Net
PMLR: proceedings.mlr.press/v278/liu25d.html
Use for: Hierarchical contrastive ECG-PPG baseline (PMLR 2025).
- Hierarchical contrastive learning β NOT raw InfoNCE
- 9.3% higher AF F1 vs prior SSL methods
- Still t=0 aligned
[T3-3] Fang et al. β PPGFlowECG
arXiv: 2509.19774 Β· arxiv.org/pdf/2509.19774
Use for: Two-stage generative translation baseline.
- Stage 1: InfoNCE instance alignment (CardioAlign encoder, shared weights)
- Stage 2: rectified flow generation from aligned latents
- Figure 1 explicitly shows ECG precedes PPG temporally but the architecture does not exploit this
- Do NOT describe as "rectified flow only" β InfoNCE is in Stage 1
[T3-4] Dong et al. β Brain-JEPA (NeurIPS 2024 Spotlight)
arXiv: 2409.19407 Β· arxiv.org/pdf/2409.19407
Code: github.com/hzlab/2024_Dong_Li_NeurIPS_Brain-JEPA
Use for: Cardiac phase encoding inspiration (ablation A2). Brain Gradient Positioning β our cardiac phase PE. Hard phase boundaries fail during AF β use soft Gaussian encoding over cardiac landmarks.
Key numbers: NeurIPS 2024 Spotlight. UK Biobank 40k patients.
[T3-5] Hojjati et al. β EEG-VJEPA
arXiv: 2507.03633 Β· arxiv.org/pdf/2507.03633
Code: github.com/amir-hojjati/eeg-vjepa
Use for: V-JEPA adapted to 1D physiological signal β most direct predecessor. How to reshape multi-channel 1D signal into 3D tensor treated as "video." UMAP showing pathological clustering without labels.
Key numbers: TUH fine-tuned accuracy 85.8%, AUROC 88.5%. Frozen probe 83.3%.
[T3-6] Munim et al. β EchoJEPA
arXiv: 2602.02603 Β· arxiv.org/pdf/2602.02603
Use for: Strongest empirical evidence that JEPA > MAE for noisy medical signals. Use in intro to justify JEPA over MAE.
Key numbers: JEPA degrades 2% under perturbation vs 17% for VideoMAE. 79% accuracy at 1% labels. 20% LVEF improvement.
[T3-7] Wu, Lei et al. β SurgMotion
arXiv: 2602.05638 Β· arxiv.org/pdf/2602.05638
Use for: One-sentence citation alongside EchoJEPA: "JEPA's noise rejection under clinical signal artifacts has been validated in echocardiography [EchoJEPA] and surgical video [SurgMotion]."
[T3-8] LeCun β A Path Towards Autonomous Machine Intelligence (JEPA position paper)
URL: openreview.net/pdf?id=BZ5a1r-kVsf
Use for: One intro citation: "A world model should predict consequences of actions in abstract representation space [LeCun 2022]."
[T3-9] Abbaspourazad et al. β Apple Heart Study Foundation Model
arXiv: 2312.05409 Β· arxiv.org/pdf/2312.05409
Published: ICLR 2024
Use for: Prior art on wearable-scale PPG+ECG foundation models. InfoNCE + KoLeo, participant-level positives, Apple Watch data. Shows ECG more discriminative than PPG β context for why cross-modal training helps PPG.
Tier 4 β Evaluation methodology and datasets
Read when writing the evaluation harness code.
[T4-1] Pimentel et al. β BIDMC PPG and Respiration Dataset
PhysioNet: physionet.org/content/bidmc/1.0.0/
Use for: Fallback dataset if E0 fails.
- WFDB format, 53 recordings Γ 8 min, 125 Hz
- Signals: Lead II ECG + fingertip PPG + impedance respiration
- Labels: HR, RR, SpO2 β no AF labels (use for HR probe only)
Key numbers: 53 patients, ~7 hours total, 125 Hz.
[T4-2] Moody et al. β MIMIC-IV Waveform Database
PhysioNet: physionet.org/content/mimic4wdb/0.1.0/
Use for: Understanding HuggingFace mirror provenance.
- v0.1.0: 200 records from 198 patients; upcoming release ~10,000 records
- MIMIC-IV-ECG module: ~800k ECGs across ~160k patients, 500 Hz, 10s, 12-lead β AF label source candidate
[T4-3] Kachuee et al. β Cuffless BP Estimation Dataset (UCI)
UCI: archive.ics.uci.edu/dataset/340
Use for: E5a PTT probe evaluation.
- 12,000 records, 942 patients β patient ID removed β population-level evaluation only
- PPG + ABP at 125 Hz, derived from MIMIC-II
Key numbers: AAMI standard β€5 mmHg mean Β± 8 mmHg SD.
[T4-4] Goldberger et al. β PhysioBank, PhysioToolkit, PhysioNet
DOI: 10.1161/01.CIR.101.23.e215
Use for: Required citation whenever using BIDMC, MIMIC waveforms, or any PhysioNet dataset. One line in methods: "Data obtained from PhysioNet [Goldberger et al., 2000]."
Tier 5 β Context and intellectual lineage
Do not read these to implement anything. One citation each.
[T5-1] Ha & Schmidhuber β World Models
arXiv: 1803.10122
Use for: Intro citation only. "World models learn a compressed latent representation and a transition function [Ha & Schmidhuber, 2018]."
[T5-2] Bardes et al. β VICReg
arXiv: 2105.04906
Use for: Related work only. "VICReg requires hand-crafted augmentations that JEPA avoids."
[T5-3] Ronan et al. β VICReg for Brugada ECG Detection
DOI: 10.1038/s41598-025-94130-x
Use for: One sentence. "VICReg-based SSL has been applied to ECG classification [Ronan et al., 2025] but requires augmentation engineering."
[T5-4] Johnson et al. β MIMIC-IV (clinical database paper)
DOI: 10.1038/s41597-022-01899-x
Use for: Required data citation whenever using MIMIC-IV derived data. "MIMIC-IV [Johnson et al., 2023], a freely accessible EHR database."
[T5-5] CLIMB multimodal clinical benchmark
arXiv: 2503.07667
Use for: ECG-JEPA performance in multimodal settings. "ECG-JEPA outperforms general time-series models like UniTS by 36.8% on ECG tasks [CLIMB, 2025]." One citation in intro.
Quick reference: numbers the agent must not get wrong
| Claim | Correct value | Source |
|---|---|---|
| ECG-JEPA PTB-XL AUC | 0.945 all-statements | T1-1 Weimann |
| AnyPPG PPGβECG R@1 | 0.736 | T2-1 Nie |
| AnyPPG AUC improvement | 9.1% over non-ECG baselines | T2-1 Nie |
| AnyPPG ECGFounder | FROZEN during training | T2-1 Nie |
| EchoJEPA JEPA perturbation | 2% degradation | T3-6 Munim |
| EchoJEPA MAE perturbation | 17% degradation | T3-6 Munim |
| EchoJEPA 1% label accuracy | 79% | T3-6 Munim |
| Normal PTT range (ICU) | 100β400ms | T2-3 Charlton |
| BIDMC size | 53 recordings Γ 8 min @ 125 Hz | T4-1 Pimentel |
| V-JEPA 2-AC interaction data | <62 hours | T2-4 Assran |
| EEG-VJEPA TUH AUROC | 88.5% fine-tuned | T3-5 Hojjati |
| CardioGAN objective | CycleGAN adversarial β not reconstruction | T3-1 Sarkar |
| TSTA-Net objective | Hierarchical contrastive β not raw InfoNCE | T3-2 Liu |
| PPGFlowECG Stage 1 | InfoNCE alignment, then rectified flow | T3-3 Fang |
| BP calibration requirement | Per-patient calibration required for absolute values | T2-3 Charlton |
File locations in repo
docs/papers/*.pdf
This is the complete reference index. Fetch from arXiv if a PDF is missing. Never cite a number not in this file without verifying the source first.