摘要：心电图（ECG）和脑电图（EEG）等生理时间序列具有复杂的时间结构、显著的数据采集差异，同时对透明决策有很高需求。尽管深度模型能够实现较高的检测性能，但它们通常无法充分解释某个片段为何异常、局部异常如何随时间相互关联，以及某次检测是否属于更广泛的重复模式。我们提出 Signal2Symbol，这是一个用于可解释生物信号异常检测的神经符号框架。该方法首先利用学习得到的 VQ-VAE（Vector Quantized Variational Autoencoder，向量量化变分自编码器）码本，或采用 SAX（Symbolic Aggregate approXimation，符号聚合近似）基线，将 ECG/EEG 信号转换为符号序列。随后，方法构建经二元组增强的 token 窗口事务，并通过最小稀有项集挖掘所得的稀有项集证据对异常进行评分。检测到的异常窗口会被合并为区间，并使用 Allen 区间代数建立关联，从而支持升级链、伪迹重叠和跨通道同步等复合时序解释。最后，我们引入一种基于形式概念分析（FCA）的稀有时序概念格，依据共享的稀有符号证据、Allen 时序关系、通道上下文和鲁棒性属性对异常区间进行分组。由此产生的 Galois 格可将大量局部检测结果压缩为可解释的时序符号异常类别。我们在三个公开基准上进行了评估：MIT-BIH Arrhythmia（心电图心搏级）、PTB-XL（心电图记录级）以及 Bonn EEG 数据集（脑电图片段级）。我们还在加性噪声和基线漂移扰动下对鲁棒性进行了压力测试。结果凸显了神经符号 token 化对时序异常分析的价值，并表明 Allen/FCA 推理能够为局部检测提供紧凑、可解释的总结。

Abstract:Physiological time series such as electrocardiograms (ECG) and electroencephalograms (EEG) exhibit complex temporal structure, substantial acquisition variability, and a strong need for transparent decision-making. Although deep models can achieve high detection performance, they often provide limited insight into why a segment is anomalous, how local anomalies relate over time, and whether a detection belongs to a broader recurring pattern. We propose Signal2Symbol, a neuro-symbolic framework for explainable biosignal anomaly detection. The method first converts ECG/EEG signals into symbolic sequences using either a learned VQ-VAE (Vector Quantized Variational Autoencoder) codebook or a SAX (Symbolic Aggregate approXimation) baseline. It then constructs bigram enriched token-window transactions and scores anomalies through rare itemset evidence derived from minimal rare itemset mining. Detected anomalous windows are merged into intervals and related using Allen interval algebra, enabling composite temporal explanations such as escalation chains, artifact overlap, and cross-channel synchrony. Finally, we introduce a rare temporal concept lattice based on Formal Concept Analysis (FCA), which groups anomalous intervals by shared rare symbolic evidence, Allen temporal relations, channel context, and robustness attributes. The resulting Galois lattice compresses many local detections into interpretable families of temporal-symbolic anomalies. We evaluate on three public benchmarks: MIT-BIH Arrhythmia (beat-level ECG), PTB-XL (record-level ECG), and the Bonn EEG dataset (segment-level EEG). We stress-test robustness under additive noise and baseline-wander perturbations. The results highlight the value of neuro-symbolic tokenization for temporal anomaly analysis and show that Allen/FCA reasoning provides compact, interpretable summaries of local detections.