用隐式微分方程建模心电波形，实现低采样率下高精度心律失常分类
A novel approach to classification of ECG arrhythmia types with latent ODEs
- 用隐式常微分方程建模连续心电信号，从单通道高频数据中提取稳定特征
- 在360Hz、90Hz、45Hz三种采样率下，宏平均AUC均超0.97，性能几乎无损
- 适合开发小型可穿戴设备，支持长期心电监测，兼顾续航与诊断精度
12导联高采样率心电图是心律失常检测的临床金标准，但其短时、点测特性常遗漏间歇性事件。可穿戴心电设备虽支持长期监测，却因电池限制导致采样率不规则且较低，难以进行形态学分析。本文提出端到端分类流程：训练隐式常微分方程（latent ODE）以建模连续心电信号，从高采样率单通道信号中生成鲁棒特征向量。通过将初始360 Hz心电图下采样至90 Hz和45 Hz，每条波形构建三个隐向量。随后使用梯度提升树对这些向量进行分类，并测试不同采样率下的鲁棒性。结果显示，在360 Hz、90 Hz和45 Hz下，宏平均AUC-ROC分别为0.984、0.978和0.976，性能几乎无衰减，表明可在不牺牲诊断精度的前提下缓解信号保真度与电池寿命之间的权衡。该方法有助于小型化可穿戴设备，推动心脏健康长期监测。
原文摘要 · Abstract (English)
12-lead ECGs with high sampling frequency are the clinical gold standard for arrhythmia detection, but their short-term, spot-check nature often misses intermittent events. Wearable ECGs enable long-term monitoring but suffer from irregular, lower sampling frequencies due to battery constraints, making morphology analysis challenging. We present an end-to-end classification pipeline to address these issues. We train a latent ODE to model continuous ECG waveforms and create robust feature vectors from high-frequency single-channel signals. We construct three latent vectors per waveform via downsampling the initial 360 Hz ECG to 90 Hz and 45 Hz. We then use a gradient boosted tree to classify these vectors and test robustness across frequencies. Performance shows minimal degradation, with macro-averaged AUC-ROC values of 0.984, 0.978, and 0.976 at 360 Hz, 90 Hz, and 45 Hz, respectively, suggesting a way to sidestep the trade-off between signal fidelity and battery life. This enables smaller wearables, promoting long-term monitoring of cardiac health.
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