Abstract
This paper applies the nonlocal mean (NLM) method to denoise the simulated and real electroencephalograph signals. As a patch-based method, the NLM method calculates the weighted sum of a patch. The weight of each point is determined by the similarity between the points of the own patch and its neighbor. Based on the weighted sum, the noise is filtered out. In this study, the NLM denoising method is applied to signals with additive Gaussian white noise, spiking noise and specific frequency noise and the results are compared with that of the popular sym8 and db16 Wavelet threshold denoising (WTD) methods. The outcomes show that the NLM on average achieves 2.70 dB increase in improved signal to noise ratio (SNRimp) and 0.37 % drop in improved percentage distortion ratio compared with WTD. The moving adaptive shape patches-NLM performs better than the original NLM when the signals change dramatically. In addition, the performance of combined NLMWTD denoising method is also better than original WTD method (0.50–4.89 dB higher in SNRimp), especially, when the signal quality is poor.
Similar content being viewed by others
References
Repovš G (2010) Dealing with noise in EEG recording and data analysis. Inform Medica Sloven 15(1):18–25
Ryynanen O, Hyttinen J, Malmivuo J (2004) Study on the spatial resolution of EEG-effect of electrode density and measurement noise. In: 26th IEEE annual international conference on engineering in medicine and biology society (IEMBS), pp 4409–4412
Zandi AS, Dumont GA, Yedlin MJ, Lapeyrie P, Sudre C, Gaffet S (2011) Scalp EEG acquisition in a low-noise environment: a quantitative assessment. IEEE Trans Biomed Eng 58(8):2407–2417
He P, Wilson G, Russell C (2004) Removal of ocular artifacts from electro-encephalogram by adaptive filtering. Med Biol Eng Comput 42(3):407–412
Romero S, Mañanas MA, Barbanoj MJ (2008) A comparative study of automatic techniques for ocular artifact reduction in spontaneous EEG signals based on clinical target variables: a simulation case. Comput Biol Med 38(3):348–360
Yu L (2009) EEG de-noising based on wavelet transformation, In: 3rd international conference on bioinformatics and biomedical engineering (ICBBE), pp 1–4
Zhang L, Wu D, Zhi, L (2009) Method of removing noise from EEG signals based on HHT method. In: 1st international conference on information science and engineering (ICISE), pp 596–599
Walters-Williams J, Li Y (2011) A new approach to denoising EEG signals-merger of translation invariant wavelet and ICA. Int J Biom Bioinform 5(2):130–149
Nguyen-Ky, Wen P, Li Y, and Gray R. (2010) De-noising a raw EEG signal and measuring depth of anaesthesia for general anaesthesia patients. In: IEEE/complex medical engineering (CME) international conference, pp 254–259
Zhang Z, Kawabata H, Liu, ZQ (2000) EEG analysis using fast wavelet transform. In: IEEE international conference on systems, man, and cybernetics, pp 2958–2964
Buades A, Coll B, Morel JM (2005) A review of image denoising algorithms, with a new one. Multiscale Model Simul 4(2):490–530
Darbon J, Cunha A, Chan TF, Osher S, Jensen GJ (2008) Fast nonlocal filtering applied to electron cryomicroscopy. In: 5th IEEE international symposium on biomedical imaging: from nano to macro (ISBI), pp 1331–1334
Deledalle CA, Duval V, Salmon J (2012) Non-local methods with shape-adaptive patches (NLM-SAP). J Math Imaging Vis 43(2):103–120
Van De Ville D, Kocher M (2009) SURE-based non-local means. Signal Process Lett IEEE 16(11):973–976
Tracey BH, Miller EL (2012) Nonlocal means denoising of ECG signals. IEEE Trans Biomed Eng 59(9):2383–2386
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Li, T., Wen, P. & Jayamaha, S. Anaesthetic EEG signal denoise using improved nonlocal mean methods. Australas Phys Eng Sci Med 37, 431–437 (2014). https://doi.org/10.1007/s13246-014-0263-z
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s13246-014-0263-z