发表机构
New York University; Koc University（纽约大学; 科奇大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
Signal2Symbol通过神经符号框架将生理信号转为符号序列，结合稀有项集挖掘与Allen/FCA推理，实现可解释的时序异常检测与复合时间解释。
AI 中文摘要
生理时间序列（如心电图（ECG）和脑电图（EEG））表现出复杂的时间结构、显著的采集变异性，并且强烈需要透明的决策过程。尽管深度模型能够实现高检测性能，但它们通常对为何某个片段异常、局部异常如何随时间关联，以及检测结果是否属于更广泛的重复模式提供有限的洞察。我们提出了Signal2Symbol，一个用于可解释生物信号异常检测的神经符号框架。该方法首先使用学习的VQ-VAE（向量量化变分自编码器）码本或SAX（符号聚合近似）基线将ECG/EEG信号转换为符号序列。然后构建基于二元组增强的标记窗口事务，并通过从最小稀有项集挖掘中获得的稀有项集证据对异常进行评分。检测到的异常窗口被合并为区间，并使用Allen区间代数进行关联，从而生成复合时间解释，如升级链、伪影重叠和跨通道同步。最后，我们引入基于形式概念分析（FCA）的稀有时间概念格，该格根据共享的稀有符号证据、Allen时间关系、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格将许多局部检测压缩为可解释的时间符号异常族。我们在三个公开基准上进行了评估：MIT-BIH心律失常（心跳级ECG）、PTB-XL（记录级ECG）和Bonn EEG数据集（分段级EEG）。我们在加性噪声和基线漂移扰动下进行了鲁棒性压力测试。结果突显了神经符号标记化在时间异常分析中的价值，并表明Allen/FCA推理提供了局部检测的紧凑、可解释的摘要。
英文摘要
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.
