论文标题
没有明确噪声标签的嘈杂心电图信号的自动检测
Automatic Detection of Noisy Electrocardiogram Signals without Explicit Noise Labels
论文作者
论文摘要
心电图（ECG）信号有益于诊断心血管疾病，这是死亡的主要原因之一。但是，它们通常受到噪声伪像污染，并影响自动和手动诊断过程。对心电图信号的自动基于深度学习的检查可能导致诊断不正确，并且手动分析涉及拒绝临床医生对嘈杂的ECG样品的拒绝，这可能会花费更多时间。为了解决这一限制，我们提出了一个两阶段的深度学习框架，以自动检测嘈杂的ECG样品。通过对两个不同数据集的广泛实验和分析，我们观察到，基于深度学习的框架可以有效地检测到高度嘈杂的ECG样品。我们还研究了在一个数据集中学到的模型转移到另一个数据集中的转移，并观察到该框架有效地检测了嘈杂的ECG样本。
Electrocardiogram (ECG) signals are beneficial in diagnosing cardiovascular diseases, which are one of the leading causes of death. However, they are often contaminated by noise artifacts and affect the automatic and manual diagnosis process. Automatic deep learning-based examination of ECG signals can lead to inaccurate diagnosis, and manual analysis involves rejection of noisy ECG samples by clinicians, which might cost extra time. To address this limitation, we present a two-stage deep learning-based framework to automatically detect the noisy ECG samples. Through extensive experiments and analysis on two different datasets, we observe that the deep learning-based framework can detect slightly and highly noisy ECG samples effectively. We also study the transfer of the model learned on one dataset to another dataset and observe that the framework effectively detects noisy ECG samples.
