Signal2Symbol: Neuro-Symbolic Temporal Reasoning for Explainable Physiological Time-Series Anomaly Detection

Signal2Symbol, a neuro-symbolic framework for explainable biosignal anomaly detection, converts ECG and EEG signals into symbolic sequences via a learned VQ-VAE codebook or a SAX baseline, then scores anomalies using rare itemset evidence from minimal rare itemset mining, according to the arXiv paper 2609.26820v1. The framework merges detected anomalous windows into intervals related by Allen interval algebra and groups them into a rare temporal concept lattice based on Formal Concept Analysis, producing composite explanations such as escalation chains, artifact overlap, and cross-channel synchrony. Evaluated on MIT-BIH Arrhythmia, PTB-XL, and the Bonn EEG dataset under additive noise and baseline-wander perturbations, the method is reported to compress many local detections into compact, interpretable families of temporal-symbolic anomalies.

arXiv:2609.26820v1 Announce Type: new 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.