Key result
A proposed multi-layer hybrid network with an adaptive template for removing the maternal cycle demonstrated better performance in fetal heartbeat detection compared with classical approaches.
Why the study?
Fetal heart rate monitoring is challenging because the observed fetal ECG signal typically has a very low signal-to-noise ratio.
Does a multi-layer hybrid network improve the accuracy of heartbeat detection in fetal ECG monitoring compared to classical approaches?
Does a multi-layer hybrid network improve the accuracy of heartbeat detection in fetal ECG monitoring compared to classical approaches?
A novel multi-layer hybrid network improves the accuracy of fetal heartbeat detection from low signal-to-noise ratio ECG recordings by effectively removing the maternal cycle.
May aid low-SNR fetal ECG analysis; leaves open prospective clinical validation before practice change.
Fetal heart rate monitoring is an enormous challenge since the observed fetal electrocardiography (ECG) signal is typically characterized by a very low signal-to-noise ratio (SNR). In this letter, we aim to improve the accuracy of heartbeat detection by proposing an adaptive template for removing the maternal cycle. The template is formed by a matrix, each row of which consists of an abdominal recorded signal (ADS). It can be updated by integrating the incoming cycle while removing the contribution of the previous recording. This process is conducted by considering a discriminator to adapt the non-stationarity of each incoming cycle. Furthermore, to suppress the morphological change caused by noise, we propose a novel multi-layer hybrid network to reconstruct the chest maternal ECG (chest mECG) morphology from a set of templates. The approach has a deep structure of each layer consisting of a reservoir layer and an encoder layer. The reservoir layer explores multi-scale dynamics by transforming the input series into a high-dimensional space. The encoder layer achieves the collection of the encoder features from the output of the reservoir layer. Once the model is built, the output weight of a direct connection is trained by solving a regression problem. Experimental results show that the proposed method has a better performance compared with some classical approaches.
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Wang et al. (2022) studied Fetal heart rate monitoring. Multi-layer hybrid network with adaptive template vs. Classical approaches was evaluated on Accuracy of heartbeat detection. A proposed multi-layer hybrid network with an adaptive template for removing the maternal cycle demonstrated better performance in fetal heartbeat detection compared with classical approaches.
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