Li Chenyang, Sun Le, Peng Dandan, Subramani Sudha, Nicolas Shangwe Charmant
Engineering Research Center of Digital Forensics, Ministry of Education, Nanjing University of Information Science and Technology, Nanjing, China.
Department of Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology (CICAEET), Nanjing University of Information Science and Technology, Nanjing, China.
Health Inf Sci Syst. 2022 Aug 25;10(1):19. doi: 10.1007/s13755-022-00192-w. eCollection 2022 Dec.
Automatic classification of ECG signals has become a research hotspot, and most of the research work in this field is currently aimed at single-label classification. However, a segment of ECG signal may contain more than two cardiac diseases, and single-label classification cannot accurately judge all possibilities. Besides, single-label classification performs classification in units of segmented beats, which destroys the contextual relevance of signal data. Therefore, studying the multi-label classification of ECG signals becomes more critical. This study proposes a method based on the multi-label question transformation method-binary correlation and classifies ECG signals by constructing a deep sequence model. Binary correlation simplifies the learning difficulty of deep learning models and converts multi-label problems into multiple binary classification problems. The experimental results are as follows: F1 score is 0.767, Hamming Loss is 0.073, Coverage is 3.4, and Ranking Loss is 0.262. It performs better than existing work.
心电图信号的自动分类已成为研究热点，目前该领域的大部分研究工作都针对单标签分类。然而，一段心电图信号可能包含两种以上的心脏疾病，单标签分类无法准确判断所有可能性。此外，单标签分类以分段心搏为单位进行分类，这破坏了信号数据的上下文相关性。因此，研究心电图信号的多标签分类变得更加关键。本研究提出了一种基于多标签问题转换方法——二元相关性的方法，并通过构建深度序列模型对心电图信号进行分类。二元相关性简化了深度学习模型的学习难度，并将多标签问题转化为多个二元分类问题。实验结果如下：F1分数为0.767，汉明损失为0.073，覆盖度为3.4，排序损失为0.262。其性能优于现有工作。
