A segment-wise reconstruction method based on bidirectional long short term memory for Power Line Interference suppression

• Autorzy: Qiu Y. , Huang K. , Xiao F. , Shen H.

Identyfikatory

DOI

10.1016/j.bbe.2018.01.003

• Języki publikacji: EN

Abstrakty

EN

The overlap between the signal components of Power Line Interference (PLI) and biomedical signals in the frequency domain makes the filtered results prone to severe distortion. Electrocardiogram (ECG) is a type of biomedical electronic signal used for cardiac diagnosis. The objective of this work is to suppress the PLI components from biomedical signals with minimal distortion, and the object of study is mainly the ECG signals. In this study, we propose a novel segment-wise reconstruction method to suppress the PLI in biomedical signals based on the Bidirectional Recurrent Neural Networks with Long Short Term Memory (Bi-LSTM). Experiments are conducted on both synthetic and real signals, and quantitative comparisons are made with a traditional IIR notch filter and two state-of-the-art methods in the literature. The results show that by our method, the output Signal-to-Noise Ratio (SNR) is improved by more than 7 dB and the settling time for step response is reduced to 0.09 s on average. The results also demonstrate that our method has enough generalization ability for unforeseen signals without retraining.

Słowa kluczowe

EN

electrocardiogram; interference suppression; long short term memory; power line interference; recurrent neural network

PL

elektrokardiogram; tłumienie zakłóceń; rekurencyjna sieć neuronowa

• Wydawca: Nałęcz Institute of Biocybernetics and Biomedical Engineering of the Polish Academy of Sciences ; Elsevier
• Czasopismo: Biocybernetics and Biomedical Engineering
• Rocznik: 2018
• Tom: Vol. 38, no. 2
• Strony: 217--227
• Opis fizyczny: Bibliogr. 26 poz., rys., tab., wykr.

Twórcy

autor

Qiu Y.

• Institute of VLSI Design, Zhejiang University, 38 Zheda Road, Hangzhou, Zhejiang Province, 310027, PR China

autor

Huang K.

• Institute of VLSI Design, Zhejiang University, 38 Zheda Road, Hangzhou, Zhejiang Province, 310027, PR China

autor

Xiao F.

• Institute of VLSI Design, Zhejiang University, 38 Zheda Road, Hangzhou, Zhejiang Province, 310027, PR China

autor

Shen H.

• Institute of VLSI Design, Zhejiang University, 38 Zheda Road, Hangzhou, Zhejiang Province, 310027, PR China

Bibliografia

• [1] Pei S-C, Tseng C-C. Elimination of AC interference in electrocardiogram using IIR notch filter with transient suppression. IEEE Trans Biomed Eng 1995;42(11):1128–32. http://dx.doi.org/10.1109/10.469385.
• [2] Gokhale PS. ECG signal de-noising using discrete wavelet transform for removal of 50 Hz PLI noise. Int J Emerg Technol Adv Eng 2012;2:81–5.
• [3] Jebaraj J, Arumugam R. Ensemble empirical mode decomposition-based optimised power line interference removal algorithm for electrocardiogram signal. IET Signal Process 2016;10:583–91 (8).
• [4] Ziarani AK, Konrad A. A nonlinear adaptive method of elimination of power line interference in ECG signals. IEEE Trans Biomed Eng 2002;49(6):540–7.
• [5] Martens SMM, Mischi M, Oei SG, Bergmans JWM. An improved adaptive power line interference canceller for electrocardiography. IEEE Trans Biomed Eng 2006;53 (11):2220–31.
• [6] Poungponsri S, Yu X. An adaptive filtering approach for electrocardiogram (ECG) signal noise reduction using neural networks. Neurocomputing 2013;117:206–13.
• [7] Warmerdam GJJ, Vullings R, Schmitt L, Laar JOEHV, Bergmans JWM. A fixed-lag Kalman smoother to filter power line interference in electrocardiogram recordings. IEEE Trans Biomed Eng 2017;64(8):1852–61.
• [8] Schuster M, Paliwal KK. Bidirectional recurrent neural networks. IEEE Trans Signal Process 1997;45(11):2673–81.
• [9] Hochreiter S, Schmidhuber J. Long short-term memory. Neural Comput 1997;9(8):1735–80.
• [10] Hanson J, Yang Y, Paliwal K, Zhou Y. Improving protein disorder prediction by deep bidirectional long short-term memory recurrent neural networks. Bioinformatics 2017;33 (5):685.
• [11] Singh B, Marks TK, Jones M, Tuzel O, Shao M. A multi-stream bi-directional recurrent neural network for fine-grained action detection. The IEEE Conference on Computer Vision and Pattern Recognition (CVPR); 2016.
• [12] Bengio Y, Simard P, Frasconi P. Learning long-term dependencies with gradient descent is difficult. IEEE Trans Neural Netw 1994;5(2):157–66.
• [13] Graves A, Schmidhuber J. Framewise phoneme classification with bidirectional lstm and other neural network architectures. Neural Netw 2005;18(5):602–10.
• [14] Mozer MC. A focused backpropagation algorithm for temporal pattern recognition. Complex Syst 1989;3(4):349–81.
• [15] Kingma DP, Ba J. Adam: a method for stochastic optimization. The 3rd International Conference for Learning Representations; 2015.
• [16] Prechelt L. Automatic early stopping using cross validation: quantifying the criteria. Neural Netw 1998;11(4):761–7.
• [17] Chollet F. Keras; 2015, https://github.com/fchollet/keras.
• [18] Abadi M, Agarwal A, Barham P, Brevdo E, Chen Z, Citro C, et al. TensorFlow: large-scale machine learning on heterogeneous systems, software available from tensorflow.org; 2015, http://tensorflow.org/.
• [19] The MIT-BIH Arrhythmia Database, http://www.physionet.org/physiobank/database/mitdb/.
• [20] Goldberger AL, Amaral LAN, Glass L, Hausdorff JM, Ivanov PC, Mark RG, et al. PhysioBank, PhysioToolkit, and PhysioNet: components of a new research resource for complex physiologic signals. Circulation 2000;101(23): e215–20.
• [21] Ichimaru Y, Moody G. Development of the polysomnographic database on CD-ROM. Psychiatry Clin Neurosci 1999;53(2):175–7. http://dx.doi.org/10.1046/j.1440-1819.1999.00527.x.
• [22] The MIT-BIH Polysomnographic Database, https://www.physionet.org/physiobank/database/slpdb/.
• [23] Moor BD, Gersem PD, Schutter BD, Favoreel W. Daisy: a database for identification of systems. Journal A 1997;38:4–5.
• [24] Tay T-T, Mareels I, Moore JB. High performance control. Springer Science & Business Media; 2012.
• [25] Yeh Y-C, Wang W-J. QRS complexes detection for ECG signal: the difference operation method. Comput Methods Programs Biomed 2008;91(3):245–54.
• [26] Beaty HW, Fink DG. Standard handbook for electrical engineers. McGraw-Hill Education; 2012.

Uwagi

PL

Opracowanie rekordu w ramach umowy 509/P-DUN/2018 ze środków MNiSW przeznaczonych na działalność upowszechniającą naukę (2018).