Accurate delineation of key waveforms in an ECG is a critical step in extracting relevant features to support the diagnosis and treatment of heart conditions. Although deep learning based methods using segmentation models to locate P, QRS, and T waves have shown promising results, their ability to handle arrhythmias has not been studied in any detail. In this paper we investigate the effect of arrhythmias on delineation quality and develop strategies to improve performance in such cases. We introduce a U-Net-like segmentation model for ECG delineation with a particular focus on diverse arrhythmias. This is followed by a post-processing algorithm which removes noise and automatically determines the boundaries of P, QRS, and T waves. Our model has been trained on a diverse dataset and evaluated against the LUDB and QTDB datasets to show strong performance, with F1-scores exceeding 99% for QRS and T waves, and over 97% for P waves in the LUDB dataset. Furthermore, we assess various models across a wide array of arrhythmias and observe that models with a strong performance on standard benchmarks may still perform poorly on arrhythmias that are underrepresented in these benchmarks, such as tachycardias. We propose solutions to address this discrepancy.

翻译：准确描记心电图中的关键波形是提取相关特征以支持心脏疾病诊断与治疗的关键步骤。尽管基于深度学习的分割模型定位P波、QRS波和T波的方法已展现出良好前景，但其处理心律失常的能力尚未得到深入研究。本文探究了心律失常对描记质量的影响，并制定了提升此类情况下性能的策略。我们提出了一种用于心电图描记的类U-Net分割模型，特别关注多种心律失常类型。随后采用后处理算法消除噪声并自动确定P波、QRS波和T波的边界。我们的模型在多样化数据集上进行训练，并在LUDB和QTDB数据集上评估，展现出优异性能：在LUDB数据集中，QRS波和T波的F1分数超过99%，P波F1分数超过97%。此外，我们评估了多种模型在广泛心律失常类型上的表现，发现在标准基准测试中表现优异的模型，在基准数据中代表性不足的心律失常（如心动过速）上仍可能表现不佳。我们提出了解决这一差异的方案。