{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,28]],"date-time":"2026-02-28T05:51:09Z","timestamp":1772257869853,"version":"3.50.1"},"reference-count":28,"publisher":"MDPI AG","issue":"24","license":[{"start":{"date-parts":[[2023,12,8]],"date-time":"2023-12-08T00:00:00Z","timestamp":1701993600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Atom Medical Corporation"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Fetal heart rate (FHR) monitoring, typically using Doppler ultrasound (DUS) signals, is an important technique for assessing fetal health. In this work, we develop a robust DUS-based FHR estimation approach complemented by DUS signal quality assessment (SQA) based on unsupervised representation learning in response to the drawbacks of previous DUS-based FHR estimation and DUS SQA methods. We improve the existing FHR estimation algorithm based on the autocorrelation function (ACF), which is the most widely used method for estimating FHR from DUS signals. Short-time Fourier transform (STFT) serves as a signal pre-processing technique that allows the extraction of both temporal and spectral information. In addition, we utilize double ACF calculations, employing the first one to determine an appropriate window size and the second one to estimate the FHR within changing windows. This approach enhances the robustness and adaptability of the algorithm. Furthermore, we tackle the challenge of low-quality signals impacting FHR estimation by introducing a DUS SQA method based on unsupervised representation learning. We employ a variational autoencoder (VAE) to train representations of pre-processed fetal DUS data and aggregate them into a signal quality index (SQI) using a self-organizing map (SOM). By incorporating the SQI and Kalman filter (KF), we refine the estimated FHRs, minimizing errors in the estimation process. Experimental results demonstrate that our proposed approach outperforms conventional methods in terms of accuracy and robustness.<\/jats:p>","DOI":"10.3390\/s23249698","type":"journal-article","created":{"date-parts":[[2023,12,8]],"date-time":"2023-12-08T03:03:33Z","timestamp":1702004613000},"page":"9698","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["A Robust Approach Assisted by Signal Quality Assessment for Fetal Heart Rate Estimation from Doppler Ultrasound Signal"],"prefix":"10.3390","volume":"23","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5094-3005","authenticated-orcid":false,"given":"Xintong","family":"Shi","sequence":"first","affiliation":[{"name":"Graduate School of Science and Technology, Keio University, Yokohama 223-8522, Japan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Natsuho","family":"Niida","sequence":"additional","affiliation":[{"name":"Graduate School of Science and Technology, Keio University, Yokohama 223-8522, Japan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kohei","family":"Yamamoto","sequence":"additional","affiliation":[{"name":"Department of Information and Computer Science, Keio University, Yokohama 223-8522, Japan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3961-1426","authenticated-orcid":false,"given":"Tomoaki","family":"Ohtsuki","sequence":"additional","affiliation":[{"name":"Department of Information and Computer Science, Keio University, Yokohama 223-8522, Japan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yutaka","family":"Matsui","sequence":"additional","affiliation":[{"name":"Atom Medical Co., Tokyo 113-0021, Japan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kazunari","family":"Owada","sequence":"additional","affiliation":[{"name":"Atom Medical Co., Tokyo 113-0021, Japan"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2023,12,8]]},"reference":[{"key":"ref_1","first-page":"CD007863","article-title":"Antenatal cardiotocography for fetal assessment","volume":"9","author":"Grivell","year":"2015","journal-title":"Cochrane Database Syst. Rev."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"22","DOI":"10.1016\/j.ejogrb.2004.01.001","article-title":"The quality of intrapartum fetal heart rate monitoring","volume":"116","author":"Bakker","year":"2004","journal-title":"Eur. J. Obstet. Gynecol. Reprod. Biol."},{"key":"ref_3","first-page":"4","article-title":"A review of fetal ECG signal processing; issues and promising directions","volume":"3","author":"Sameni","year":"2010","journal-title":"Open Pacing Electrophysiol. Ther. J."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"226","DOI":"10.1109\/TUFFC.2019.2943626","article-title":"Doppler ultrasound technology for fetal heart rate monitoring: A review","volume":"67","author":"Hamelmann","year":"2019","journal-title":"IEEE Trans. Ultrason. Ferroelectr. Freq. Control."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"585","DOI":"10.1088\/0967-3334\/25\/2\/015","article-title":"Beat-to-beat detection of fetal heart rate: Doppler ultrasound cardiotocography compared to direct ECG cardiotocography in time and frequency domain","volume":"25","author":"Peters","year":"2004","journal-title":"Physiol. Meas."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/1475-925X-10-92","article-title":"A novel technique for fetal heart rate estimation from Doppler ultrasound signal","volume":"10","author":"Jezewski","year":"2011","journal-title":"Biomed. Eng. Online"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"025005","DOI":"10.1088\/1361-6579\/ab033d","article-title":"An open source autocorrelation-based method for fetal heart rate estimation from one-dimensional Doppler ultrasound","volume":"40","author":"Valderrama","year":"2019","journal-title":"Physiol. Meas."},{"key":"ref_8","unstructured":"Rouvre, D., Kouam\u00e9, D., Tranquart, F., and Pourcelot, L. (2005, January 21). Empirical mode decomposition (EMD) for multi-gate, multi-transducer ultrasound Doppler fetal heart monitoring. Proceedings of the the 5th IEEE International Symposium on Signal Processing and Information Technology, Athens, Greece."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"641","DOI":"10.3389\/fphys.2017.00641","article-title":"A hybrid EMD-kurtosis method for estimating fetal heart rate from continuous Doppler signals","volume":"8","author":"Kimura","year":"2017","journal-title":"Front. Physiol."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"511","DOI":"10.3389\/fphys.2017.00511","article-title":"Template-based quality assessment of the Doppler ultrasound signal for fetal monitoring","volume":"8","author":"Valderrama","year":"2017","journal-title":"Front. Physiol."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Marzbanrad, F., Kimura, Y., Endo, M., Palaniswami, M., and Khandoker, A.H. (2015, January 6\u20139). Classification of Doppler ultrasound signal quality for the application of fetal valve motion identification. Proceedings of the 2015 Computing in Cardiology Conference (CinC), Nice, France.","DOI":"10.1109\/CIC.2015.7408662"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"882","DOI":"10.1109\/TBME.2021.3108621","article-title":"Electrocardiogram quality assessment using unsupervised deep learning","volume":"69","author":"Seeuws","year":"2021","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Pereira, J., and Silveira, M. (March, January 27). Learning representations from healthcare time series data for unsupervised anomaly detection. Proceedings of the 2019 IEEE International Conference on Big Data and Smart Computing (BigComp), Kyoto, Japan.","DOI":"10.1109\/BIGCOMP.2019.8679157"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Kohonen, T., Nieminen, I.T., and Honkela, T. (2009, January 8\u201310). On the quantization error in SOM vs. VQ: A critical and systematic study. Proceedings of the Advances in Self-Organizing Maps, St. Augustine, FL, USA.","DOI":"10.1007\/978-3-642-02397-2_16"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"15","DOI":"10.1088\/0967-3334\/29\/1\/002","article-title":"Robust heart rate estimation from multiple asynchronous noisy sources using signal quality indices and a Kalman filter","volume":"29","author":"Li","year":"2007","journal-title":"Physiol. Meas."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"2793","DOI":"10.1109\/TBME.2017.2675543","article-title":"Non-invasive fetal ECG signal quality assessment for multichannel heart rate estimation","volume":"64","author":"Andreotti","year":"2017","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Shi, X., Yamamoto, K., Ohtsuki, T., Matsui, Y., and Owada, K. (2022, January 4\u20138). Unsupervised Representation Learning-based Doppler Ultrasound Signal Quality Assessment. Proceedings of the 2022 IEEE Global Communications Conference, Rio de Janeiro, Brazil.","DOI":"10.1109\/GLOBECOM48099.2022.10000857"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Roy, M.S., Gupta, R., and Sharma, K.D. (2020, January 7\u20139). Photoplethysmogram signal quality evaluation by unsupervised learning approach. Proceedings of the 2020 IEEE Applied Signal Processing Conference (ASPCON), Kolkata, India.","DOI":"10.1109\/ASPCON49795.2020.9276733"},{"key":"ref_19","unstructured":"Lei, Q., Yi, J., Vaculin, R., Wu, L., and Dhillon, I.S. (2017). Similarity preserving representation learning for time series clustering. arXiv."},{"key":"ref_20","unstructured":"Heckert, N.A., and Filliben, J.J. (2003). Nist\/Sematech e-Handbook of Statistical Methods, NIST. Chapter 1: Exploratory Data Analysis."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Rui, T., Zhang, S., Ren, T., Tang, J., and Zou, J. (2018, January 21\u201322). Data Reconstruction based on supervised deep auto-encoder. Proceedings of the Pacific Rim Conference on Multimedia, Hefei, China.","DOI":"10.1007\/978-3-319-77383-4_85"},{"key":"ref_22","unstructured":"Kingma, D.P., and Welling, M. (2013). Auto-encoding variational bayes. arXiv."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Ghosal, P., Sarkar, D., Kundu, S., Roy, S., Sinha, A., and Ganguli, S. (2017, January 2\u20133). Ecg beat quality assessment using self organizing map. Proceedings of the 2017 4th International Conference on Opto-Electronics and Applied Optics (Optronix), Kolkata, India.","DOI":"10.1109\/OPTRONIX.2017.8349994"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"60369","DOI":"10.1109\/ACCESS.2018.2875737","article-title":"Spectrogram-based non-contact RRI estimation by accurate peak detection algorithm","volume":"6","author":"Yamamoto","year":"2018","journal-title":"IEEE Access"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Schmidt, S., Toft, E., Holst-Hansen, C., Graff, C., and Struijk, J. (2008, January 14\u201317). Segmentation of heart sound recordings from an electronic stethoscope by a duration dependent Hidden-Markov Model. Proceedings of the 2008 Computers in Cardiology, Bologna, Italy.","DOI":"10.1109\/CIC.2008.4749049"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"60806","DOI":"10.1109\/ACCESS.2019.2912036","article-title":"Noise reduction in ECG signals using fully convolutional denoising autoencoders","volume":"7","author":"Chiang","year":"2019","journal-title":"IEEE Access"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"35","DOI":"10.1115\/1.3662552","article-title":"A New Approach to Linear Filtering and Prediction Problems","volume":"82","author":"Kalman","year":"1960","journal-title":"J. Basic Eng."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Liu, S.H., Li, R.X., Wang, J.J., Chen, W., and Su, C.H. (2020). Classification of photoplethysmographic signal quality with deep convolution neural networks for accurate measurement of cardiac stroke volume. Appl. Sci., 10.","DOI":"10.3390\/app10134612"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/24\/9698\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T21:35:11Z","timestamp":1760132111000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/24\/9698"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,12,8]]},"references-count":28,"journal-issue":{"issue":"24","published-online":{"date-parts":[[2023,12]]}},"alternative-id":["s23249698"],"URL":"https:\/\/doi.org\/10.3390\/s23249698","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,12,8]]}}}