紧凑的潜在流形翻译：一种参数高效的基础模型用于跨模态和跨频率生理信号合成
Compact Latent Manifold Translation: A Parameter-Efficient Foundation Model for Cross-Modal and Cross-Frequency Physiological Signal Synthesis
AI总结：
本文提出CLMT模型，通过双阶段离散翻译范式解决生理信号跨模态和跨频率合成问题，显著提升临床检测精度和超分辨率性能。
摘要：
生理时间序列（如心电图和脉搏波容积图）的分析长期受到异质记录设备导致的模态和频率间隙的阻碍。现有基础模型通常依赖连续潜在空间，常导致严重的模态纠缠、缺乏高保真跨频率生成能力，并且计算成本高，阻碍边缘设备部署。本文提出紧凑潜在流形翻译（CLMT），一种高度参数高效（0.09B）的统一框架，通过新颖的双阶段离散翻译范式弥合这些差距。首先，我们引入一种通用分词器，利用分层残差向量量化（RVQ）将异质信号解耦为孤立、结构良好的离散潜在流形，有效防止跨模态干扰。其次，上下文引导的潜在翻译器通过整合静态生理先验，将复杂信号合成重新框架为纯粹的潜在序列翻译任务。大量评估显示，我们的0.09B模型显著优于大规模基线。在跨模态PPG到ECG合成中，它解决了时间相位漂移，并大幅提高了临床R波检测F1分数从0.37（基线）到0.83。此外，在极端跨频率超分辨率（25Hz到100Hz）中，它成功恢复高频诊断地标，实现了前所未有的皮尔逊相关系数0.9956。通过学习生物信号的通用离散语言，我们的方法为边缘部署的多模态医疗基础模型开辟了新路径。
英文摘要：
The analysis of physiological time series, such as electrocardiograms (ECG) and photoplethysmograms (PPG), is persistently hindered by modality and frequency gaps stemming from heterogeneous recording devices. Existing foundation models typically rely on continuous latent spaces, which frequently suffer from severe modality entanglement, lack high-fidelity cross-frequency generative capacity, and impose high computational costs that prohibit edge-device deployment. In this paper, we propose Compact Latent Manifold Translation (CLMT), a highly parameter-efficient (0.09B) unified framework that bridges these gaps through a novel two-stage discrete translation paradigm. First, we introduce a Universal Tokenizer utilizing Hierarchical Residual Vector Quantization (RVQ) to decouple heterogeneous signals into isolated, well-structured discrete latent manifolds, effectively preventing inter-modality interference. Second, a Context-Prompted Latent Translator maps these discrete tokens across modalities by integrating static physiological priors, reframing complex signal synthesis as a pure latent sequence translation task. Extensive evaluations demonstrate that our 0.09B model significantly outperforms massive baselines. In cross-modal PPG-to-ECG synthesis, it resolves temporal phase drift and dramatically improves the clinical R-peak detection F1-score from 0.37 (baseline) to 0.83. Furthermore, in extreme cross-frequency super-resolution (25Hz to 100Hz), it successfully recovers high-frequency diagnostic landmarks, achieving an unprecedented Pearson correlation of 0.9956. By learning a universal discrete language for biological signals with a fraction of the computational footprint, our approach sets a new trajectory for edge-deployable, multi-modal medical foundation models.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
