面向深度学习心电图分类的基线漂移容忍信号编码
Baseline Drift Tolerant Signal Encoding for ECG Classification with Deep Learning
AI总结：
本文提出派生峰值（DP）编码，利用信号一阶和二阶导数过零点生成带符号尖峰，对偏移和缩放伪影具有不变性，在PTB-XL数据集上训练1D-ResNet-18进行心电图分类，显著提升了对基线漂移、偏移和重缩放等常见伪影的鲁棒性。
摘要：
基线漂移、重缩放和噪声等常见伪影严重限制了基于机器学习的自动心电图分析与解释的性能。本研究提出了一种非参数方法——派生峰值（Derived Peak，DP）编码，该方法根据信号一阶和二阶时间导数的过零点生成带符号的尖峰。值得注意的是，DP编码对偏移和缩放伪影具有不变性，并且由于不存在用户自定义参数，其实现进一步简化。使用DP编码对来自PTB-XL数据集（n=18,869名参与者）的12导联心电图数据进行编码，并将其输入到1D-ResNet-18模型中，训练模型用于识别心肌梗死、传导障碍和ST段异常。通过在编码前用心电图数据加入正弦基线漂移、偏移、重缩放和噪声来评估对伪影的鲁棒性。加入这些伪影导致先前技术中的其他七种方法准确率显著下降，而DP编码在漂移、偏移和重缩放下保持了0.88的基线AUC。在存在偏移（1mV偏移下的AUC：0.91对0.62）和重缩放伪影（AUC 0.91对0.79）的情况下，DP相比未编码输入取得了更优的性能。因此，DP编码是一种简单的方法，可用于提高自动心电图分析与解释对常见心电图伪影的鲁棒性。
英文摘要：
Common artefacts such as baseline drift, rescaling, and noise critically limit the performance of machine learningbased automated ECG analysis and interpretation. This study proposes Derived Peak (DP) encoding, a non-parametric method that generates signed spikes corresponding to zero crossings of the signals first and second-order time derivatives. Notably, DP encoding is invariant to shift and scaling artefacts, and its implementation is further simplified by the absence of userdefined parameters. DP encoding was used to encode the 12-lead ECG data from the PTB-XL dataset (n=18,869 participants) and was fed to 1D-ResNet-18 models trained to identify myocardial infarction, conductive deficits and ST-segment abnormalities. Robustness to artefacts was assessed by corrupting ECG data with sinusoidal baseline drift, shift, rescaling and noise, before encoding. The addition of these artefacts resulted in a significant drop in accuracy for seven other methods from prior art, while DP encoding maintained a baseline AUC of 0.88 under drift, shift and rescaling. DP achieved superior performance to unencoded inputs in the presence of shift (AUC under 1mV shift: 0.91 vs 0.62), and rescaling artefacts (AUC 0.91 vs 0.79). Thus, DP encoding is a simple method by which robustness to common ECG artefacts may be improved for automated ECG analysis and interpretation.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
