Generic placeholder image

Current Medical Imaging

Editor-in-Chief

ISSN (Print): 1573-4056
ISSN (Online): 1875-6603

Research Article

Feature Extraction, Risky Classifications and Fault Diagnosis on Rolling Bearings of EEG Signals Denoised using Stationary Wavelet Transform of Patient Monitoring and IoT

Author(s): Vinothini Ramasamy Vellalapalayam*, Thangaraj Ponnusamy and Sakthisudhan Karuppanan

Volume 15, Issue 8, 2019

Page: [718 - 748] Pages: 31

DOI: 10.2174/1573405614666180322144219

Price: $65

conference banner
Abstract

Background: One of the primary causes of sleep disorders, depth of anesthesia, coma, encephalopathy’s and brain death in the world is epilepsy. EEG is most often used to diagnose epilepsy which causes the abnormalities in EEG readings. Different high-resolution anatomical imaging techniques are used to detect these abnormalities like MRI, PET, CT, etc.

Methods: SWT method will be an enhanced system from wavelet transform. It may be fit for the signal for time-invariant on the break down also enhance those force of indicator denoising. SWT additionally employs upsampled technique at every level of decay for those signs. The decay of SWT produces the coefficients from claiming close estimation and points in the same length.

The DWT will be actualized by a channel bank that decomposes those indicators over progressive coarser approximations. The output of the low pass and high pass filter coefficients is decomposed to the next level and further proceeds up to N levels. The yield of the wavelet decay may be that close estimation and the point of interest coefficients which would get to each level of decay. This system consists of five main processing steps: acquisition, pre-processing, feature extraction, feature selection and classification.

Results: This paper overviews some of the current state-of-the-art IOT systems and presents the statistical-based algorithm used for each processing step.

Conclusion: This paper also provides a comparison of the performance of the existing approaches.

Keywords: Denoised electroencephalography signals, discrete stationary wavelet transform, electroencephalography signals, feature extraction, internet of things, stationary wavelet transform, thresholding.

Graphical Abstract
[1]
Abbasion S, Rafsanjani A, Farshidianfar A, Irani N. Rolling element bearing multi fault classification based on wavelet denoising and support vector machine. Mech Syst Signal Process 2007; 21: 2933-45.
[http://dx.doi.org/10.1016/j.ymssp.2007.02.003]
[2]
Brige L, Massart P. Gaussian model selection. J Eur Math Soc 2001; 3(3): 203-6.
[http://dx.doi.org/10.1007/s100970100031]
[3]
Cherif LH, Debbal SM, Bereksi-Reguig F. Choice of the wavelet analyzing in the phonocardiogram signal analysis using discrete and the packet wavelet transform. Expert Syst Appl 2010; 37(2): 913-8.
[http://dx.doi.org/10.1016/j.eswa.2009.09.036]
[4]
Cohen A, Daubechies I, Feauveau JC. Biorthogonal bases of compactly supported wavelets. Commun Pure Appl Math 1992; 45: 485-560.
[http://dx.doi.org/10.1002/cpa.3160450502]
[5]
Ingrid D. Ten lectures on wavelets. 9th ed. SIAM 2006.
[6]
Daubechies. The wavelet transform, time-frequency localization and signal analysis. IEEE Trans Inf Theory 1990; 36(5): 961-1005.
[http://dx.doi.org/10.1109/18.57199]
[7]
Donoho DL, Johnstone IM. Denoising by soft thresholding. IEEE Trans Inf Theory 1995; 41(3): 613-27.
[http://dx.doi.org/10.1109/18.382009]
[8]
Gao R, Yan R. Non-stationary signal processing for bearing health monitoring. Int J Manuf Res 2006; 1(1): 18-40.
[http://dx.doi.org/10.1504/IJMR.2006.010701]
[9]
Wang G-Y, Zhao X-Q, Wang X. Speech enhancement based on the combination of spectral subtraction and wavelet thresholding. In: International Conference on Apperceiving Computing and Intelligence Analysis. Chengdu, China. IEEE . 136-39.
[http://dx.doi.org/10.1109/ICACIA.2009.5361134]
[10]
Johnstone IM, Silverman BGV. Wavelet threshold estimators for data with correlated noise. J R Stat Soc B 1997; 59(2): 319-51.
[http://dx.doi.org/10.1111/1467-9868.00071]
[11]
Kim JS, Lee JH, Kim JH, Baek J, Kim SS. Fault detection of cycle based signals using wavelet transform in FAB processes. Int J Precis Eng Manuf 2010; 11(2): 237-46.
[http://dx.doi.org/10.1007/s12541-010-0027-y]
[12]
Zhou SY, Sun BC, Shi JJ. An SPC monitoring system for cycle based waveform signals using Haar wavelet transform. IEEE Trans Autom Sci Eng 2009; 3(1): 60-72.
[http://dx.doi.org/10.1109/TASE.2005.859655]
[13]
Sugumaran V, Ramachandhran KI. Wavelet selection using decision tree for fault diagnosis of roller bearing. Int J Appl Eng Res 2009; 4(2): 201-5.
[14]
Rajesh NA, Chandralingam S, Anjaneyulu T, Satyanarayana K. Denoising EOG signal using stationary wavelet transform. Meas Sci Rev 2012; 12(2): 46-51.
[15]
Stephane M. A wavelet tour of signal processing. Academic Press 2008.
[16]
Matlab Wavelet tool box. Available from: . http://in.mathworks.com/help/wavelet/ref/thselect.html
[17]
Luisier F, Blu T, Unser M. A new SURE approach to image denoising: interscale orthonormal wavelet thresholding. IEEE Trans Image Process 2007; 16(3): 593-606.
[http://dx.doi.org/10.1109/TIP.2007.891064] [PMID: 17357721]
[18]
Dixon AMR, Allstot EG, Gangopadhyay D, Allstot DJ. Compressed sensing system considerations for ECG and EMG wireless biosensors. IEEE Trans Biomed Circuits Syst 2012; 6(2): 156-66.
[http://dx.doi.org/10.1109/TBCAS.2012.2193668] [PMID: 23852980]
[19]
Kumar BM, Lavanya RV. Signal denoising with soft threshold by using Chui-Lian (CL) multi wavelet. Int J Elect Comm Technol 2011; 2(1): 38-42.
[20]
Rosas-Orea MCE, Hernandez-Diaz M, Alarcon AV, Guerrero-Ojeda LG. A comparative simulation study of wavelet based denoising algorithms. In: 15th International Conference on Electronics, Communications and Computers. Puebla, Mexico. 2005; pp. 125-30.
[21]
Larobina M, Arturo B, Marco S. A review of commercially available imaging sytems. Curr Med Imaging 2006; 2(2): 187-92.
[http://dx.doi.org/10.2174/157340506776930610]
[22]
Verma N, Verma AK. Performance analysis of wavelet thresholding methods in denoising of audio signals of some Indian musical instruments. IJESTR 2012; 4(5): 2047-53.
[23]
Pantelopoulos A, Bourbakis NG. A survey on wearable sensor-based systems for health monitoring and prognosis. IEEE Trans Syst Man Cybern Syst 2010; 40(1): 1-12.
[http://dx.doi.org/10.1109/TSMCC.2009.2032660]
[24]
Li J, Chen X, Huang X, et al. Secure distributed deduplication systems with improved reliability. IEEE Trans Comput 2015; 64(12): 3569-79.
[25]
Sardy S. Minimax threshold for denoising complex signals with Waveshrink. IEEE Trans Signal Process 2000; 48(4): 1023-8.
[http://dx.doi.org/10.1109/78.827536]
[26]
Zhang T, Lu J, Hu F, Hao Q. Bluetooth low energy for wearable sensor-based healthcare systems. In: IEEE Healthcare Innovation Conference . (HIC) Seattle, WA, USA 2014; pp. 251-4.
[27]
Perera C, Qin Y, Estrella JC, Reiff-Marganiec S, Vasilakos AV. Fog computing for sustainable smart cities: A survey. ACM Computing Surveys (CSUR) 2017; 50(3): 32.
[28]
Gia TN, Jiang M, Rahmani AM, Westerlund T, Liljeberg P, Tenhunen H. Fog computing in healthcare internet of things: A case study on ecg feature extraction. In: International Conference on Computer and Information Technology; Ubiquitous Computing and Communications; Dependable, Autonomic and Secure Computing; Pervasive Intelligence and Computing Liverpool, UK. 356-63.
[29]
Rahmani A-M, Thanigaivelan NK, Gia TN, et al. Smart e-health gateway: Bringing intelligence to internet-of-things based ubiquitous healthcare systems. IEEE T Biomed Circ S 2015; 6(2): 826-34.
[http://dx.doi.org/10.1109/CCNC.2015.7158084]
[30]
Babapour MF, Abbaspour T-FA, Aghaeizadeh ZR, Akhlaghpoor S, Chen YW. A novel wavelet based multi-scale statistical shape model-analysis for the liver application: segmentation and classification. Current Medical Imaging 2010; 6(3): 145-55.

Rights & Permissions Print Cite
© 2024 Bentham Science Publishers | Privacy Policy