School of Engineering, Computing and Mathematics, Oxford Brookes University.
G.E. Pukhov Institute for Modelling in Energy Engineering.
Stud Health Technol Inform. 2021 Oct 27;285:147-152. doi: 10.3233/SHTI210588.
The paper introduces a multilayer long short-term memory (LSTM) based auto-encoder network to spot abnormalities in fetal ECG. The LSTM network was used to detect patterns in the time series, reconstruct errors and classify a given segment as an anomaly or not. The proposed anomaly detection method provides a filtering procedure able to reproduce ECG variability based on the semi-supervised paradigm. Experiments show that the proposed method can learn better features than the traditional approach without any prior knowledge and subject to proper signal identification can facilitate the analysis of fetal ECG signals in daily life.
本文提出了一种基于多层长短时记忆（LSTM）自动编码器网络的胎儿心电图异常检测方法。该 LSTM 网络用于检测时间序列中的模式，重建误差，并将给定片段分类为异常或正常。所提出的异常检测方法提供了一种基于半监督范例的能够再现 ECG 可变性的过滤过程。实验表明，与传统方法相比，该方法无需任何先验知识即可学习更好的特征，并且在适当的信号识别下，可以促进日常生活中胎儿心电图信号的分析。
