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Automatic identifying of maternal ECG source when applying ICA in fetal ECG extraction

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Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
Independent component analysis (ICA) is usually used as a preliminary step for maternal electrocardiogram (ECG) QRS detection in fetal ECG extraction. When applying ICA to do this, a troublesome problem arises from how to automatically identify the separated maternal ECG component. In this paper we proposed a method called PRCH (short for Peak to peak entropy, R-R interval entropy, Correlation coefficient and Heart rate) for the automatic identifying. In the method, we defined four kinds of features, including amplitude, instantaneous heart rate, morphology and average heart rate, to characterize a signal, and determined some decision parameters through machine learning. Experiments and comparison with other three existed methods were given. Through taking metric F1 for evaluation, it showed that the proposed PRCH method has the highest identifying accuracy and generalization capability.
Twórcy
autor
  • School of Electronic Science and Engineering, Nanjing University, Xianlin Campus, Nanjing 210023, China
autor
  • School of Electronic Science and Engineering, Nanjing University, Xianlin Campus, Nanjing 210023, China
autor
  • School of Electronic Science and Engineering, Nanjing University, Xianlin Campus, Nanjing 210023, China
autor
  • School of Electronic Science and Engineering, Nanjing University, Xianlin Campus, Nanjing 210023, China
autor
  • School of Electronic Science and Engineering, Nanjing University, Xianlin Campus, Nanjing 210023, China, njhxliu@nju.edu.cn
autor
  • School of Electronic Science and Engineering, Nanjing University, Xianlin Campus, Nanjing 210023, China
autor
  • School of Electronic Science and Engineering, Nanjing University, Xianlin Campus, Nanjing 210023, China
Bibliografia
  • [1] Clifford GD, Silva I, Behar J, Moody GB. Non-invasive fetal ECG analysis. Physiol Meas 2014;35:1521–36.
  • [2] Sameni R, Clifford GD. A review of fetal ECG signal processing; issues and promising directions. Open Pacing Electrophysiol Therapy J 2010;3:4–20. http://dx.doi.org/10.2174/1876536X01003010004.
  • [3] Tsalaile T, Sameni R, Sanei S, Jutten C, Chambers J. Sequential blind source extraction for quasi-periodic signals with time-varing period. IEEE Trans Biomed Eng 2009;56(March (3)):646–55.
  • [4] Comani S, Mantini D, Lagatta A, Esposito F, Di Luzio S, Romani GL. Time course reconstruction of fetal cardiac signals from fMCG: independent component analysis versus adaptive maternal beat subtraction. Physiol Meas 2004;25:1305–21.
  • [5] Martens SMM, Rabotti C, Mischi M, Sluijter RJ. A robust fetal ECG detection method for abdominal recordings. Physiol Meas 2007;28:373–88.
  • [6] Vullings R, Peters CHL, Sluijter RJ, Mischi M, Oei SG, Bergmans JWM. Dynamic segmentation and linear prediction for maternal ECG removal in antenatal abdominal recordings. Physiol Meas 2009;30:291–307.
  • [7] Wei Z, Hongxing L, Aijun H, Xinbao N, Jianchun C. Singlelead fetal electrocardiogram estimation by means of combining R-peak detection, resampling and comb filter. Med Eng Phys 2010;32:708–19.
  • [8] Yan H, Liu H, Huang X, Zhao Y, Si J, Liu T. Invariant heart beat span versus variant heart beat intervals and its application to fetal ECG extraction. BioMed Eng Online 2014;13:163 [online].
  • [9] Zeng XP, Li SH, Li GJ, Zhou Y, Mo DH. Fetal ECG extraction by combining single-channel SVD and cyclostationarity-based blind source separation. Int J Signal Process Image Process Pattern Recognit 2013;6:367–76.
  • [10] Comon P. Independent component analysis, a new concept? Signal Process 1994;36:287–314.
  • [11] Camargo-Olivares JL, Martin-Clemente R, Hornillo-Mellado S, Elena MM, Roman I. The maternal abdominal ECG as input to MICA in the fetal ECG extraction problem. IEEE Signal Process Lett 2011;18(March (3)):161–4.
  • [12] Taralunga D, Ungureanu M, Strungaru R, Wolf W. Performance comparison of four ICA algorithms applied for fECG extraction from transabdominal recordings. 2011 10th International Symposium on Signals, Circuits and Systems (ISSCS); 2011. pp. 1–4.
  • [13] Ivanushkina N, Ivanko K, Lysenko E, Borovskiy I, Panasiuk O. Fetal electrocardiogram extraction from maternal abdominal signals. 2014 IEEE 34th International Conference on Electronics and Nanotechnology (ELNANO); 2014. pp. 334–8.
  • [14] Behar J, Oster J, Clifford GD. Combining and benchmarking methods of foetal ECG extraction without maternal or scalp electrode data. Physiol Meas 2014;35:1569–89.
  • [15] Varanini M, Tartarisco G, Billeci L, Macerata A, Pioggia G, Balocchi R. An efficient unsupervised fetal QRS complex detection from abdominal maternal ECG. Physiol Meas 2014;35:1607–19.
  • [16] Acharyya A, Maharatna K, Al-Hashimi BM, Mondal S. Robust channel identification scheme: solving permutation indeterminacy of ICA for artifacts removal from ECG. Engineering in Medicine and Biology Society (EMBC). 2010 Annual International Conference of the IEEE; 2010. pp. 1142–5.
  • [17] Agarwal A, Singh A, Acharyya A, Shafik RA, Ahamed SR. Energy-efficient and high-speed robust channel identification methodologhy to solve permutation indeterminacy in ICA for artifacts removal from ECG in remote healthcare. 2013 International Symposium on Electronic System Design (ISED); 2013. pp. 52–6.
  • [18] Wan H, Liu Q, Chai J. A method for extracting FECG based on ICA algorithm. 9th International Conference on Signal Processing, 2008 (ICSP 2008); 2008. pp. 2761–4.
  • [19] Jiemin Z, Xiaolin H, Qun G, Tiebing L, Ping L, Ying Z, et al. Some regularity on how to locate electrodes for higher fECG SNRs. Chin Phys B 2015;24(3).
  • [20] Maria CD, Liu C, Zheng D, Murray A, Langley P. Extraction fetal heart beats from maternal abdominal recordings: selection of the optimal principal components. Physiol Meas 2014;35:1649–64.
  • [21] Cardoso J-F. High-order contrasts for independent component analysis. Neural Comput 1999;11:157–92.
  • [22] Hyvärinen A, Oja E. A fast fixed-point algorithm for independent component analysis. Neural Comput 1997;9:1483–92.
  • [23] Pan J, Tompkins WJ. A real-time QRS detection algorithm. IEEE Trans Biomed Eng 1985;BME-32(March (3)):230–6.
  • [24] Martinez AM, Kak AC. PCA versus LDA. IEEE Trans Pattern Anal Mach Intell 2001;23(February (2)):228–33.
Uwagi
PL
Opracowanie rekordu w ramach umowy 509/P-DUN/2018 ze środków MNiSW przeznaczonych na działalność upowszechniającą naukę (2018).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-5c2c01cf-df58-4c15-9a99-8bdfdcdb73ee
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