Optimal template selecting combined with non-liner template matching for Doppler fetal heart rate extraction_Journal of Biomedical Engineering

Authors：

XU Tianyi ^1,2 ,  CAI Ping ^1,2 , LIU Xiaohua ³ , MA Yixin ^1,2

1. Department of instrument, School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai 200240, P.R.China;
2. Shanghai Engineering Research Center for Intelligent Diagnosis and Treatment Instrument, Shanghai 200240, P.R.China;
3. The International Peace Maternity & Child Health Hospital of China welfare institute, Shanghai 200030, P.R.China;

Corresponding author：

CAI Ping, Email: pcai@sjtu.edu.cn

Keywords：

Doppler ultrasound; fetal heart rate; template matching; short-time Fourier transform

DOI：

10.7507/1001-5515.201812010

Video：

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Abstract Full text Figures/Tables Video References Cited by

The ultrasound Doppler fetal heart rate measurement is the gold standard of fetal heart rate counting. However, the existing fetal heart rate extraction algorithms are not designed specifically to suppress the high maternal interference during the second stage of labor, and false detection occurrences are common during labor. With this background, a method combining time-frequency frame template library optimal selecting and non-linear template matching is proposed. The method contributes a template library, and the optimal template can be selected to match the signal frame. After the short-time Fourier transform of the signal, the difference between the signal and the template is optimized by leaky rectified linear unit (LReLU) function frame by frame. The heart rate was calculated from the peak of the matching curve and the heart rate was calculated. By comparing the proposed method with the autocorrelation method, the results show that the detection accuracy of the proposed method is improved by 20% on average, and the non-linear template matching of 23% samples is at least 50% higher than the autocorrelation method. This paper designs the algorithm by analyzing the characteristics of the interference and signal mixing. We hope that this paper will provide a new idea for fetal heart rate extraction which not only focuses on the original signal.

Citation： XU Tianyi, CAI Ping, LIU Xiaohua, MA Yixin. Optimal template selecting combined with non-liner template matching for Doppler fetal heart rate extraction. Journal of Biomedical Engineering, 2019, 36(4): 557-564. doi: 10.7507/1001-5515.201812010 Copy

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13.	Canadas-Quesada F J, Ruiz-Reyes N, Carabias-Orti J, et al. A non-negative matrix factorization approach based on spectro-temporal clustering to extract heart sounds. Applied Acoustics, 2017, 125: 7-19.
14.	冯爱玲. 基于短时傅里叶变换的胎心率检测算法与实现. 广州: 广东工业大学, 2014.
15.	Xie Kan, Zhang Haochuan, Xie Shengli, et al. Method and apparatus for detecting instantaneous fetal heart rate of Doppler fetal heart sound based on time-frequency analysis. US20180116628A1, 2018-05-03.
16.	Matsunaga D, Izumi S, Okuno K, et al. Non-contact and noise tolerant heart rate monitoring using microwave doppler sensor and range imagery//2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 2015: 6118-6121.
17.	Nakai Y, Izumi S, Nakano M, et al. Noise tolerant QRS detection using template matching with short-term autocorrelation//2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 2014: 34-37.
18.	Zhou Weidong, Li Yingyuan. EEG real-time feedback based on STFT and coherence analysis// The 23rd Annual International Conference of the IEEE Engineering in Medicine & Biology Society. Turkey: IEEE, 2001.
19.	Kojima K. Proceedings of the fifth Berkeley symposium on mathematical statistics and probability. Am J Hum Genet, 1969, 21(4): 407-408.
20.	Maas A L, Hannun A Y, Ng A Y. Rectifier nonlinearities improve neural network acoustic models// The 30th International Conference on Machine Learning(ICML 2013), 2013, 28: 6.

1. Kwon J Y, Park I Y. Fetal heart rate monitoring: from Doppler to computerized analysis. Obstetrics and Gynecology Science, 2016, 59(2): 79-84.
2. Adithya P C, Sankar R, Moreno W A, et al. Trends in fetal monitoring through phonocardiography: challenges and future directions. Biomed Signal Process Control, 2017, 33: 289-305.
3. Paquette S, Moretti F, O'reilly K, et al. The incidence of maternal artefact during intrapartum fetal heart rate monitoring. Journal of Obstetrics and Gynaecology Canada, 2014, 36(11): 962-968.
4. 郭晓辉, 杨斌, 陈美一, 等. 第二产程中胎心电子外监护和母体心率联合监测的临床价值. 中国实用妇科与产科杂志, 2005, 21(9): 555-556.
5. 洪海洁. 分析第二产程中胎心电子外监护和母体心率联合监测的临床价值. 医学信息, 2016, 29(1): 48-49.
6. Rao Jian, Zeng Yonghua, Chen Dewei, et al. Apparatus and method for automatically identifying fetal heart rate baseline. US 9232901B2, 2016-01-12.
7. Voicu I, Menigot S, Kouame D, et al. New estimators and guidelines for better use of fetal heart rate estimators with Doppler ultrasound devices. Comput Math Methods Med, 2014, 2014: 784862.
8. Jezewski J, Worbel J, Horoba K, et al. Advances in Doppler ultrasound FHR monitoring. Klin Perinat Ginekol, 1995, 3: 241-251.
9. Voicu I, Kouame D, Fournier-Massignan M, et al. Estimating fetal heart rate from multiple Doppler ultrasound signals// International Conference on Advancements of Medicine and Health Care through Technology. Springer Berlin Heidelberg, 2009: 185-190.
10. Ruffo M, Cesarelli M, Romano M, et al. A simulating software of fetal phonocardiographic signals//The 10th IEEE International Conference on Information Technology and Applications in Biomedicine. Corfu, Greece: IEEE, 2010.
11. Al-Angari H M, Kimura Y, Hadjileontiadis L J, et al. A hybrid EMD-kurtosis method for estimating fetal heart rate from continuous Doppler signals. Frontiers in Physiology, 2017, 8: 641.
12. Papadaniil C D, Hadjileontiadis L J. Efficient heart sound segmentation and extraction using ensemble empirical mode decomposition and kurtosis features. IEEE J Biomed Health Inform, 2014, 18(4): 1138-1152.
13. Canadas-Quesada F J, Ruiz-Reyes N, Carabias-Orti J, et al. A non-negative matrix factorization approach based on spectro-temporal clustering to extract heart sounds. Applied Acoustics, 2017, 125: 7-19.
14. 冯爱玲. 基于短时傅里叶变换的胎心率检测算法与实现. 广州: 广东工业大学, 2014.
15. Xie Kan, Zhang Haochuan, Xie Shengli, et al. Method and apparatus for detecting instantaneous fetal heart rate of Doppler fetal heart sound based on time-frequency analysis. US20180116628A1, 2018-05-03.
16. Matsunaga D, Izumi S, Okuno K, et al. Non-contact and noise tolerant heart rate monitoring using microwave doppler sensor and range imagery//2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 2015: 6118-6121.
17. Nakai Y, Izumi S, Nakano M, et al. Noise tolerant QRS detection using template matching with short-term autocorrelation//2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 2014: 34-37.
18. Zhou Weidong, Li Yingyuan. EEG real-time feedback based on STFT and coherence analysis// The 23rd Annual International Conference of the IEEE Engineering in Medicine & Biology Society. Turkey: IEEE, 2001.
19. Kojima K. Proceedings of the fifth Berkeley symposium on mathematical statistics and probability. Am J Hum Genet, 1969, 21(4): 407-408.
20. Maas A L, Hannun A Y, Ng A Y. Rectifier nonlinearities improve neural network acoustic models// The 30th International Conference on Machine Learning(ICML 2013), 2013, 28: 6.

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