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

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

Research output: Contribution to journalArticlepeer-review

2 Citations (Scopus)


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.

Original languageEnglish
Pages (from-to)1207-1211
Number of pages5
JournalIEEE Signal Processing Letters
Publication statusPublished - 2022


  • Discriminator
  • fetal electrocardiography signal
  • heart rate monitoring
  • multi-layer hybrid network

ASJC Scopus subject areas

  • Signal Processing
  • Electrical and Electronic Engineering
  • Applied Mathematics


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