A Multi-Layer Hybrid Network With Its Application in Fetal Heart Rate Monitoring

Lu Wang, Tomoaki Ohtsuki, Kazunari Owada, Naoki Honma, Hayato Hayashi

研究成果: Article査読

抄録

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.

本文言語English
ページ(範囲)1207-1211
ページ数5
ジャーナルIEEE Signal Processing Letters
29
DOI
出版ステータスPublished - 2022

ASJC Scopus subject areas

  • 信号処理
  • 電子工学および電気工学
  • 応用数学

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