[Paper] Lost in Time? A Meta-Learning Framework for Time-Shift-Tolerant Physiological Signal Transformation

Source: arXiv - 2511.21500v1

Overview

The paper introduces ShiftSyncNet, a meta‑learning framework that automatically corrects temporal misalignments when converting non‑invasive physiological signals (e.g., PPG, BCG) into clinically useful ones such as arterial blood pressure (ABP). By learning to “sync” the two signals during training, the method boosts transformation accuracy without the need for hand‑crafted alignment heuristics, making continuous, low‑cost health monitoring more reliable.

Key Contributions

• Bi‑level meta‑learning architecture that jointly trains a transformation network (TransNet) and a time‑shift correction network (SyncNet).
• Fourier‑based phase‑shift module that learns per‑sample offsets and applies them as differentiable time‑shifts, preserving the signal’s spectral content.
• Robustness to diverse misalignment patterns (constant, random, and subject‑specific shifts) demonstrated on one industrial and two public multimodal datasets.
• Significant performance gains over strong baselines (up to +12.8% in ABP peak‑error reduction).
• A unified perspective that frames temporal misalignment as a label‑noise problem solvable by meta‑learning, opening the door for similar solutions in other time‑series domains.

Methodology

1. Problem formulation – The task is a supervised regression: given a source signal (x) (e.g., PPG) predict a target signal (y) (e.g., ABP). In practice, (y) is often recorded with an unknown time offset (\Delta t), turning the supervision into noisy labels.

2. Network components

   • TransNet: a conventional encoder‑decoder (or 1‑D CNN/Transformer) that maps the source signal to the target domain.
   • SyncNet: a lightweight network that ingests the source–target pair and outputs a scalar offset (\hat{\Delta t}). The offset is converted to a phase shift in the Fourier domain and applied to the target signal, producing an aligned supervision (\tilde{y}).

3. Bi‑level optimization

   • Inner loop: With the current offset estimate, SyncNet aligns the target and TransNet is trained to minimize the loss between its prediction (\hat{y}) and the aligned target (\tilde{y}).
   • Outer loop: SyncNet’s parameters are updated to minimize the validation loss of TransNet, effectively teaching SyncNet to propose offsets that lead to the best downstream transformation performance.

4. Training tricks

   • Differentiable Fourier phase shift ensures gradients flow through the alignment operation.
   • Curriculum on shift magnitude gradually increases the allowed offset range, stabilizing early training.

The whole pipeline runs end‑to‑end on standard deep‑learning frameworks (PyTorch/TensorFlow) and requires only the raw paired signals—no manual annotation of alignment points.

Results & Findings

• Peak detection (systolic/diastolic) error dropped by >15 % across all datasets, confirming that the model recovers clinically relevant landmarks.
• Ablation studies showed that removing the Fourier‑phase step or training SyncNet without the outer‑loop validation loss caused performance to revert to baseline levels, highlighting the necessity of both components.
• Robustness tests with synthetic random shifts (±200 ms) demonstrated that ShiftSyncNet maintained <5 % degradation, whereas conventional models failed catastrophically.

Practical Implications

• Wearable device pipelines can embed ShiftSyncNet to clean up misaligned sensor streams on‑device, reducing the need for post‑processing or costly calibration phases.
• Edge‑AI deployments benefit from the lightweight SyncNet (≈ 0.2 M parameters) that adds negligible latency while delivering higher‑fidelity blood‑pressure estimates.
• Data‑collection workflows become more forgiving: manufacturers no longer need ultra‑precise hardware synchronization, lowering BOM costs and simplifying multi‑sensor integration.
• Generalization to other domains—e.g., audio‑visual lip‑reading, multimodal robotics, or ECG‑EEG fusion—where time‑drift between modalities is a known pain point.

For developers, the codebase can be dropped into existing PyTorch training loops, and the meta‑learning schedule can be tuned with standard optimizers (AdamW) and learning‑rate schedulers.

Limitations & Future Work

• Assumes a single global shift per training pair; more complex warping (non‑linear drift) is not addressed.
• The current experiments focus on relatively short recordings (≤ 5 min); scalability to long‑term continuous monitoring remains to be validated.
• Meta‑learning overhead adds extra compute during training (bi‑level loops), though inference cost stays low.
• Future research directions include extending SyncNet to learn time‑varying offsets, integrating uncertainty estimation for the corrected labels, and evaluating the framework on multimodal datasets beyond cardiovascular signals (e.g., sleep staging with EEG‑EOG).

Authors

• Qian Hong
• Cheng Bian
• Xiao Zhou
• Xiaoyu Li
• Yelei Li
• Zijing Zeng

Paper Information

• arXiv ID: 2511.21500v1
• Categories: cs.LG
• Published: November 26, 2025
• PDF: Download PDF