A Novel Cluster-Based Method for Single-channel Fetal Electrocardiogram Detection

Yang Hong, Hongling Zhu, Xiaoyun Yang, Cheng Cheng, Ye Yuan

Abstract: Fetal electrocardiography (FECG) is a promising technology for non-invasive fetal monitoring. However, due to the low amplitude and non-stationary characteristics of the FECG signal, it is difficult to extract it from maternal abdominal signals. Moreover, most FECG extraction methods are based on multiple channels, which make it difficult to achieve fetal monitoring outside the clinic. This paper proposes an efficient cluster-based method for accurate FECG extraction and fetal QRS detection only using one channel signal. We designed min-max-min group as the basis for feature extraction. The extracted features are used to distinguish the different components of the abdominal signal, and finally extract the FECG signal. To verify the effectiveness of our algorithm, we conducted experiments on a public dataset and a dataset record from the Tongji Hospital. Experimental results show that our method can achieve an accuracy rate of more than 96% which is better than other algorithms.