Bizzego Andrea, Gabrieli Giulio, Neoh Michelle Jin Yee, Esposito Gianluca
Department of Psychology and Cognitive Science, University of Trento, 38068 Trento, Italy.
Psychology Program, Nanyang Technological University, Singapore 639818, Singapore.
Bioengineering (Basel). 2021 Nov 28;8(12):193. doi: 10.3390/bioengineering8120193.
Deep learning (DL) has greatly contributed to bioelectric signal processing, in particular to extract physiological markers. However, the efficacy and applicability of the results proposed in the literature is often constrained to the population represented by the data used to train the models. In this study, we investigate the issues related to applying a DL model on heterogeneous datasets. In particular, by focusing on heart beat detection from electrocardiogram signals (ECG), we show that the performance of a model trained on data from healthy subjects decreases when applied to patients with cardiac conditions and to signals collected with different devices. We then evaluate the use of transfer learning (TL) to adapt the model to the different datasets. In particular, we show that the classification performance is improved, even with datasets with a small sample size. These results suggest that a greater effort should be made towards the generalizability of DL models applied on bioelectric signals, in particular, by retrieving more representative datasets.
深度学习（DL）对生物电信号处理做出了巨大贡献，特别是在提取生理标志物方面。然而，文献中提出的结果的有效性和适用性往往局限于用于训练模型的数据所代表的人群。在本研究中，我们调查了将DL模型应用于异构数据集的相关问题。特别是，通过专注于从心电图信号（ECG）中检测心跳，我们表明，在健康受试者数据上训练的模型应用于患有心脏疾病的患者以及使用不同设备收集的信号时，其性能会下降。然后，我们评估了使用迁移学习（TL）使模型适应不同数据集的情况。特别是，我们表明，即使对于样本量较小的数据集，分类性能也会得到改善。这些结果表明，应做出更大努力来提高应用于生物电信号的DL模型的通用性，特别是通过获取更具代表性的数据集。
