通过上下文感知的多心跳分析，提升心电图心律失常分类准确率。
DeepArrhythmia: Segment-Contextualized ECG Arrhythmia Classification via Selective Evidence Acquisition
- 融合原始信号与波形图像，定位心跳并结合多拍上下文判断
- 根据段落置信度动态选择使用简单或丰富的生理证据
- 适合需要高精度心律失常识别的医疗场景
心电图（ECG）心律失常检测通常将每个心跳视为孤立局部实例，但实际标签常依赖于多心跳节律上下文，包括时间间隔、代偿性停搏及心跳形态一致性。本文提出DeepArrhythmia，一种基于工具的多模态框架，实现段落级上下文的心跳级心律失常分类。给定一个多心跳ECG段，该框架结合原始信号与渲染波形图像，定位R波以识别心跳实例，并输出结构化的心跳级预测。系统通过专用工具解耦生理测量与证据整合：分别用于心跳定位、节律-形态数值提取和形态聚焦文本分析。利用段落级置信度在最小与丰富证据状态间路由，因更丰富的生理证据并非始终有效。此代理式设计融合节律上下文、显式生理基础与选择性证据获取，实现更可靠的决策。
原文摘要 · Abstract (English)
Beat-level Electrocardiography (ECG) arrhythmia detection aims to assign an arrhythmia class to each beat in a recording, yet many existing systems treat beats as isolated local instances. This is limiting because beat labels often depend on multi-beat rhythm context, including timing, compensatory pauses, and beat-to-beat morphological consistency. We present DeepArrhythmia, a tool-grounded multimodal framework for segment-contextualized beat-level ECG arrhythmia classification. Given a multi-beat ECG segment, DeepArrhythmia combines the raw ECG signal and a rendered waveform image, localizes R peaks to identify beat instances, and produces structured beat-level predictions. The framework decouples physiological measurement from evidence integration using specialized tools for beat localization, numerical rhythm--morphology extraction, and morphology-focused textual analysis. DeepArrhythmia uses segment-level confidence to route between minimal and rich evidence states, since richer physiological evidence is not uniformly useful. This agentic design integrates rhythm context, explicit physiological grounding, and selective evidence acquisition for decision making.
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