Timesynth: A Temporal Fidelity Framework for Health Signal Digital Twins

2026-07-01

AI Summary

Existing digital twin models for physiological signals rely on point-wise evaluation metrics, which fail to capture critical dynamic characteristics such as oscillatory behavior, frequency, phase, and state transitions, often leading to misinterpretation. This work proposes TimeSynth, the first systematic evaluation framework designed specifically for assessing dynamic fidelity in synthetic physiological signals. TimeSynth integrates a parameterized generator fitted to real signals with multidimensional diagnostic tools to enable quantitative analysis of frequency spectra, phase coherence, and state-transition dynamics. Comparative evaluations across multiple neural network architectures reveal that models emphasizing local temporal structure better preserve these dynamic properties. Notably, even when conventional metrics appear comparable, phase errors can differ by up to 53° (approximately 123 ms), underscoring the necessity of aligning model architecture with the specific requirements of the target application.

Abstract

Forecasting models for health-signal digital twins must preserve the oscillatory, frequency, phase, and state-transition dynamics of physiological signals, yet the pointwise metrics used to benchmark them cannot detect when these fundamental properties are lost. We show that this blind spot misranks models: across 11 architectures, models with comparable pointwise error diverge by up to 53° in phase accuracy, equivalent to roughly 123 ms for a 1.2 Hz cardiac rhythm and invisible to standard metrics. To enable development of models that escape such failures, we introduce TimeSynth, a controlled benchmarking framework with two reusable components: a physiologically grounded generator producing signals with analytically known ground-truth dynamics from parametric models fitted to real electroencephalography, electrocardiography and photoplethysmogram signals, along with diagnostics quantifying amplitude, frequency, phase, and state-transition fidelity. Linear and full-sequence attention models systematically lose frequency and phase information despite acceptable amplitude error, whereas architectures with localized temporal structure better preserve dynamical fidelity and adapt to observable state transitions; none, however, reliably preserves stochastic switching. Because the dominant determinant of fidelity is architectural, model choice becomes a principled, use-case-driven decision rather than a search for a single winner. TimeSynth thus supplies the controlled preclinical stress test missing before models are coupled to patient data, with a reusable generator and diagnostics for fidelity-aware development.

Problem

• digital twins
• physiological signals
• temporal fidelity
• phase accuracy
• benchmarking

Innovation

• TimeSynth
• temporal fidelity
• digital twins
• physiological signal modeling
• phase accuracy