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Huang Y, Song I S, Rana P, et al. Fast diagnosis of bowel activities. In: Proceedings of International Joint Conference on Neural Networks, Anchorage, 2017. 3042–3049
Uluşar Ü D. Recovery of gastrointestinal tract motility detection using naive Bayesian and minimum statistics. Comput Biol Med, 2014, 51: 223–228
Öztaş A S, Türk E, Uluşar Ü D, et al. Bioacoustic sensor system for automatic detection of bowel sounds. In: Proceedings of IEEE Medical Technologies National Conference (TIPTEKNO), Bodrum, 2015
Spiegel B M R, Kaneshiro M, Russell M M, et al. Validation of an acoustic gastrointestinal surveillance biosensor for postoperative ileus. J Gastrointest Surg, 2014, 18: 1795–1803
Amon M, Jager F. Electrocardiogram ST-segment morphology delineation method using orthogonal transformations. PLoS ONE, 2016, 11: 0148814
Khalil I, Khalil A, Rehman S U, et al. Classification of ECG signals using Legendre moments. Int J Bioinform Biomed Eng, 2015, 1: 284–291
Yin Y, Jiang H J, Yang W D, et al. Intestinal motility assessment based on Legendre fitting of logarithmic bowel sound spectrum. Electron Lett, 2016, 52: 1364–1366
Yin Y, Yang W D, Jiang H J, et al. Bowel sound based digestion state recognition using artificial neural network. In: Proceedings of IEEE Biomedical Circuits and Systems Conference, Atlanta, 2015
Bray D, Reilly R B, Haskin L, et al. Assessing motility though abdominal sound monitoring. In: Proceedings of the 19th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Chicago, 1997. 2398–2400
Sheu M J, Lin P Y, Chen J Y, et al. Higher-order-statistics-based fractal dimension for noisy bowel sound detection. IEEE Signal Process Lett, 2015, 22: 789–793
This work was supported in part by National Natural Science Foundation of China (Grant Nos. 61474070, 61431166002, 61661166010), and Beijing Engineering Research Center (Grant No. BG0149). The authors would like to thank the doctors who contributed to the experiments, including Ms. Huili KAN from Department of Anesthesiology, Liaocheng People’s Hospital and Mr. Jianjun LI from Department of Gastrointestinal Surgery, Liaocheng People’s Hospital.
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Yin, Y., Jiang, H., Feng, S. et al. Bowel sound recognition using SVM classification in a wearable health monitoring system. Sci. China Inf. Sci. 61, 084301 (2018). https://doi.org/10.1007/s11432-018-9395-5