Unsupervised Representation Learning-based Doppler Ultrasound Signal Quality Assessment

Xintong Shi, Kohei Yamamoto, Tomoaki Ohtsuki, Yutaka Matsui, Kazunari Owada

Research output: Chapter in Book/Report/Conference proceedingConference contribution


The Doppler ultrasound (DUS) transducer has been widely used for fetal heart rate (FHR) monitoring. However, the fetal DUS signals from the transducers can be corrupted by several interference sources such as maternal and fetal movements, which makes FHR estimation using fetal DUS signals challenging. Fetal DUS signal quality assessment (SQA) can help to remove or interpolate unreliable FHRs estimated from noisy signals to improve the accuracy of FHR estimation. There are some existing approaches for fetal DUS SQA, and most of these approaches with high accuracy are based on supervised learning-based algorithms and human-defined properties. Nonetheless, the fetal DUS datasets with quality-level annotations are limited, and human-defined properties place a limitation on mining more deep information related to signal quality in fetal DUS signals. In this paper, we propose an unsupervised representation learning-based fetal DUS SQA for the improvement of FHR estimation performance. We firstly learn representations of pre-processed fetal DUS data from variational autoencoder (VAE) and then combine these representations as one signal quality index (SQI) using a self-organizing map (SOM). Finally, we apply the combined SQI and a Kalman filter (KF) to estimate fetal RR intervals (FRRI) for reducing the errors of FHR estimation. The experimental results showed that our proposed method could reduce the averaged root mean squared error (RMSE) of FRRI and averaged absolute error (AAE) of FHR.

Original languageEnglish
Title of host publication2022 IEEE Global Communications Conference, GLOBECOM 2022 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages6
ISBN (Electronic)9781665435406
Publication statusPublished - 2022
Event2022 IEEE Global Communications Conference, GLOBECOM 2022 - Virtual, Online, Brazil
Duration: 2022 Dec 42022 Dec 8

Publication series

Name2022 IEEE Global Communications Conference, GLOBECOM 2022 - Proceedings


Conference2022 IEEE Global Communications Conference, GLOBECOM 2022
CityVirtual, Online


  • Fetal doppler ultrasound signal
  • Kalman filter
  • Signal quality assessment
  • Unsupervised representation learning
  • Variational autoencoder

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Networks and Communications
  • Hardware and Architecture
  • Signal Processing
  • Renewable Energy, Sustainability and the Environment
  • Safety, Risk, Reliability and Quality


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