STADIUS Center for Dynamical Systems, Signal Processing and Data Analytics, Department of Electrical Engineering (ESAT), KU Leuven, 3001 Leuven, Belgium.
IMEC, 3001 Leuven, Belgium.
Sensors (Basel). 2021 Jan 19;21(2):662. doi: 10.3390/s21020662.
The electrocardiogram (ECG) is an important diagnostic tool for identifying cardiac problems. Nowadays, new ways to record ECG signals outside of the hospital are being investigated. A promising technique is capacitively coupled ECG (ccECG), which allows ECG signals to be recorded through insulating materials. However, as the ECG is no longer recorded in a controlled environment, this inevitably implies the presence of more artefacts. Artefact detection algorithms are used to detect and remove these. Typically, the training of a new algorithm requires a lot of ground truth data, which is costly to obtain. As many labelled contact ECG datasets exist, we could avoid the use of labelling new ccECG signals by making use of previous knowledge. Transfer learning can be used for this purpose. Here, we applied transfer learning to optimise the performance of an artefact detection model, trained on contact ECG, towards ccECG. We used ECG recordings from three different datasets, recorded with three recording devices. We showed that the accuracy of a contact-ECG classifier improved between 5 and 8% by means of transfer learning when tested on a ccECG dataset. Furthermore, we showed that only 20 segments of the ccECG dataset are sufficient to significantly increase the accuracy.
心电图（ECG）是识别心脏问题的重要诊断工具。如今，人们正在研究在医院外记录心电图信号的新方法。一种很有前途的技术是电容耦合心电图（ccECG），它可以通过绝缘材料记录心电图信号。然而，由于心电图不再在受控环境中记录，这不可避免地意味着会有更多的伪迹。使用人工制品检测算法来检测和去除这些伪迹。通常，新算法的训练需要大量的地面实况数据，这是很昂贵的。由于存在许多标记的接触式心电图数据集，我们可以通过利用以前的知识来避免对新的 ccECG 信号进行标记。迁移学习可用于此目的。在这里，我们应用迁移学习来优化基于接触式心电图训练的人工制品检测模型在 ccECG 上的性能。我们使用了来自三个不同数据集的心电图记录，使用三个记录设备进行了记录。我们表明，通过迁移学习，当在 ccECG 数据集上进行测试时，接触式 ECG 分类器的准确性提高了 5%至 8%。此外，我们还表明，只需要 ccECG 数据集的 20 个片段就可以显著提高准确性。
