AI 中文总结
该研究采用基于motif的轨迹框架分析23例SCA患者的Holter ECG，发现模式离散度可长时程检测SCA，模式一致性晚期变化显著，其无标签框架可实现个性化SCA预警，适配长时程可穿戴ECG监测。
AI 中文摘要
长时程心电图（ECG）中心脏骤停（SCA）的预警特征仍未得到充分表征。我们采用基于模式（motif）的轨迹框架量化了事件前的心电形态变化。对23例标注SCA患者的动态心电图（Holter ECG）进行分析，采用不重叠的10秒窗口，提取窗口级模式以量化不稳定性、一致性、离散度、异质性及个性化基线距离的轨迹。每条轨迹通过z分数归一化至早期基线，并对齐至心室颤动（VF）发作。异常负荷定义为滚动10分钟窗口内z≥3的窗口比例。跨形态指标，中位持续异常负荷发作发生在VF前5.8至8.4小时。模式离散度表现出最一致的长时程检测，100%患者在VF前≥1小时出现持续异常负荷，89%患者在VF前≥2小时出现；模式一致性表现出最强的晚期变化，最终10分钟内中位异常负荷为70%；形态偏差峰值出现在VF前约2小时。我们的无标签框架将纵向心电分析从事件检测方法转变为对心脏演变变化的连续表征，实现了个性化SCA预警，非常适用于长时程可穿戴心电监测。
英文摘要
Early warning signatures of sudden cardiac arrest (SCA) remain poorly characterised in long-duration ECG. We quantified pre-event changes in ECG morphology using a motif-based trajectory framework. Holter ECGs from 23 patients with annotated SCA were analysed over non-overlapping 10 s windows. Window-level motifs were extracted to quantify trajectories of instability, consistency, dispersion, heterogeneity, and personalised-baseline distance. Each trajectory was normalised to an early baseline using z-scores and aligned to ventricular fibrillation (VF) onset. Abnormal burden was defined as the proportion of windows with z>=3 within a rolling 10-minute window. Median sustained abnormal burden onset occurred 5.8-8.4 h before VF across morphological metrics. Motif dispersion showed the most consistent long-horizon detection, with sustained abnormal burden >=1 h before VF in 100% of patients and >=2 h in 89%. Motif consistency showed the strongest late-stage change, with 70% median abnormal burden in the final 10 min. Peak morphological deviation occurred ~2 h prior to VF. Our label-free framework transforms longitudinal ECG analysis from an event detection approach towards a continuous characterisation of evolving cardiac change, enabling a personalised early warning of SCA, well suited to long-duration wearable ECG monitoring.
CommentsAccepted to the Computing in Cardiology Conference (CinC) 2026
