Generic placeholder image

Current Medical Imaging

Editor-in-Chief

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

Research Article

Applying Auto-Regressive Model’s Yule-Walker Approach to Amyotrophic Lateral Sclerosis (ALS) patients’ Data

Author(s): Mridu Sahu*, Saumya Vishwal , Srungaram Usha Srivalli, Naresh Kumar Nagwani, Shrish Verma and Sneha Shukla

Volume 15, Issue 8, 2019

Page: [749 - 760] Pages: 12

DOI: 10.2174/1573405614666180322143503

Price: $65

Abstract

Objective: The purpose of this study is to identifying time series analysis and mathematical model fitting on electroencephalography channels that are placed on Amyotrophic Lateral Sclerosis (ALS) patients with P300 based brain-computer interface (BCI).

Methods: Amyotrophic Lateral Sclerosis (ALS) or motor neuron diseases are a rapidly progressing neurological disease that attacks and kills neurons responsible for controlling voluntary muscles. There is no cure and treatment effective to reverse, to halt the disease progression. A Brain- Computer Interface enables disable person to communicate & interact with each other and with the environment. To bypass the important motor difficulties present in ALS patient, BCI is useful. An input for BCI system is patient's brain signals and these signals are converted into external operations, for brain signals detection, Electroencephalography (EEG) is normally used. P300 based BCI is used to record the reading of EEG brain signals with the help of non-invasive placement of channels. In EEG, channel analysis Autoregressive (AR) model is a widely used. In the present study, Yule-Walker approach of AR model has been used for channel data fitting. Model fitting as a form of digitization is majorly required for good understanding of the dataset, and also for data prediction.

Results: Fourth order of the mathematical curve for this dataset is selected. The reason is the high accuracy obtained in the 4th order of Autoregressive model (97.51±0.64).

Conclusion: In proposed Auto Regressive (AR) model has been used for Amyotrophic Lateral Sclerosis (ALS) patient data analysis. The 4th order of Yule Walker auto-regressive model is giving best fitting on this problem.

Keywords: Amyotrophic lateral sclerosis, auto-regressive model, brain computer interface, electroencephalography, model fitting, P300 speller, time series model, Yule-Walker.

Graphical Abstract
[1]
Mehta P. Prevalence of amyotrophic lateral sclerosis - United States, 2010-2011. Am J Public Health 2015; 105(6): e7-9.
[http://dx.doi.org/10.2105/AJPH.2015.302747] [PMID: 25970478]
[2]
Zarei S, Carr K, Reiley L, et al. A comprehensive review of amyotrophic lateral sclerosis. Surg Neurol Int 2015; 6: 171.
[http://dx.doi.org/10.4103/2152-7806.169561] [PMID: 26629397]
[3]
Cipresso P, Carelli L, Solca F, et al. The use of P300-based BCIs in amyotrophic lateral sclerosis: From augmentative and alternative communication to cognitive assessment. Brain Behav 2012; 2(4): 479-98.
[http://dx.doi.org/10.1002/brb3.57] [PMID: 22950051]
[4]
Hobson EV, McDermott CJ. Supportive and symptomatic management of amyotrophic lateral sclerosis. Nat Rev Neurol 2016; 12(9): 526-38.
[http://dx.doi.org/10.1038/nrneurol.2016.111] [PMID: 27514291]
[5]
Zinman L, Cudkowicz M. Emerging targets and treatments in amyotrophic lateral sclerosis. Lancet Neurol 2011; 10(5): 481-90.
[http://dx.doi.org/10.1016/S1474-4422(11)70024-2] [PMID: 21511200]
[6]
Conwit RA. Preventing familial ALS: A clinical trial may be feasible but is an efficacy trial warranted? J Neurosci 2006; 251.
[http://dx.doi.org/10.1016/j.jns.2006.07.009]
[7]
Al-Chalabi A, Leigh PN. Recent advances in amyotrophic lateral sclerosis. Curr Opin Neurol 2000; 13(4): 397-405.
[http://dx.doi.org/10.1097/00019052-200008000-00006] [PMID: 10970056]
[8]
Radunović A, Mitsumoto H, Leigh PN. Clinical care of patients with amyotrophic lateral sclerosis. Lancet Neurol 2007; 6(10): 913-25.
[http://dx.doi.org/10.1016/S1474-4422(07)70244-2] [PMID: 17884681]
[9]
Amiri S, Fazel-Rezai R, Asadpour V. A review of hybrid brain-computer interface systems. Adv Hum Comput Interact 2013; 2013: 1.
[http://dx.doi.org/10.1155/2013/187024]
[10]
Kübler A, Nijboer F, Mellinger J, et al. Patients with ALS can use sensorimotor rhythms to operate a brain-computer interface. Neurology 2005; 64(10): 1775-7.
[http://dx.doi.org/10.1212/01.WNL.0000158616.43002.6D] [PMID: 15911809]
[11]
Schalk G, Mellinger J. A practical guide to brain-computer interfacing with BCI2000. Springer 2010.
[12]
Niedermeyer E, da Silva FL, Eds. Electroencephalography: basic principles, clinical applications, and related fields. Philadelphia, USA: Lippincott Williams & Wilkins 2005.
[13]
Khalil BA, Misulis KE. Atlas of EEG & seizure semiology. Philadelphia, USA: Elsevier 2006.
[14]
Jasper HH. The 10/20 international electrode system. EEG Clin Neurophysiol 1958; 10: 371-5.
[15]
Chatrian GE, Lettich E, Nelson PL. Ten percent electrode system for topographic studies of spontaneous and evoked EEG activities. Am J EEG Technol 1985; 25(2): 83-92.
[http://dx.doi.org/10.1080/00029238.1985.11080163]
[16]
Klem GH. LuÈders HO, Jasper HH, Elger C. The ten-twenty electrode system of the International Federation. Electroencephalogr Clin Neurophysiol 1999; 52(3): 3-6.
[17]
Luck SJ. LuÈders HO, Jasper HH, Elger C The ten-twenty electrode system of the International Federation An introduction to the event-related potential technique. MA, USA: MIT Press 2014.
[18]
Jin J, Sellers EW, Zhou S, Zhang Y, Wang X, Cichocki AA. P300 brain-computer interface based on a modification of the mismatch negativity paradigm. Int J Neural Syst 2015; 25(3)1550011
[http://dx.doi.org/10.1142/S0129065715500112] [PMID: 25804352]
[19]
McCane LM, Heckman SM, McFarland DJ, et al. P300-based brain-computer interface (BCI) event-related potentials (ERPs): People with amyotrophic lateral sclerosis (ALS) vs. age-matched controls. Clin Neurophysiol 2015; 126(11): 2124-31.
[http://dx.doi.org/10.1016/j.clinph.2015.01.013] [PMID: 25703940]
[20]
Mainsah BO, Collins LM, Colwell KA, et al. Increasing BCI communication rates with dynamic stopping towards more practical use: An ALS study. J Neural Eng 2015; 12(1)016013
[http://dx.doi.org/10.1088/1741-2560/12/1/016013] [PMID: 25588137]
[21]
Fazel-Rezai R. Human error in P300 speller paradigm for brain-computer interface. In: 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society;. 2007 Aug 22- 26;; Lyon, France: . 2516-519.
[http://dx.doi.org/10.1109/IEMBS.2007.4352840]
[22]
Jin J, Allison BZ, Zhang Y, Wang X, Cichocki A. An ERP-based BCI using an oddball paradigm with different faces and reduced errors in critical functions. Int J Neural Syst 2014; 24(8)1450027
[http://dx.doi.org/10.1142/S0129065714500270] [PMID: 25182191]
[23]
Box GE, Jenkins GM, Reinsel GC, et al. Time series analysis: forcasting and control. 4th ed. NJ, USA: John wiley 2008.
[24]
Gersch W. Spectral analysis of EEG’s by autoregressive decomposition of time series. Math Biosci 1970; 7(1-2): 205-22.
[http://dx.doi.org/10.1016/0025-5564(70)90049-0]
[25]
Chatfield C. The analysis of time series: an introduction. CRC press 2016.
[26]
Padmasai Y. SubbaRao K, Malini V. Linear prediction modelling for the analysis of the epileptic EEG. In: 2010 International Conference on Advances in Computer Engineering (ACE). 2010; Bangalore, India. 6-9.
[27]
Wong CS, Li WK. On a mixture autoregressive model. J R Stat Soc Series B Stat Methodol 2000; 62(1): 95-115.
[http://dx.doi.org/10.1111/1467-9868.00222]
[28]
Nai-Jen H, Palaniappan R. Classification of mental tasks using fixed and adaptive autoregressive models of EEG signals. In: The 26th Annual International Conference of the IEEE Engineering in Medicine and Biology Society. 2004 Sept 1-5; San Francisco, CA, USA: IEEE 507-10.
[http://dx.doi.org/10.1109/IEMBS.2004.1403205]
[29]
Chen W, Anderson B, Deistler M, Filler A. Solutions of Yule‐Walker equations for singular AR processes. J Time Ser Anal 2011; 32(5): 531-8.
[http://dx.doi.org/10.1111/j.1467-9892.2010.00711.x]
[30]
Fazel-Rezai R, Allison BZ, Guger C, Sellers EW, Kleih SC, Kübler A. P300 brain computer interface: current challenges and emerging trends. Front Neuroeng 2012; 5: 14.
[http://dx.doi.org/10.3389/fneng.2012.00014] [PMID: 22822397]
[31]
Hoffmann U, Vesin JM, Ebrahimi T. Recent advances in brain-computer interfaces. In: IEEE International Workshop on Multimedia Signal Processing (MMSP07). 2007; (No LTS-CONF-2007-063).
[32]
Ravindran N, Samraj A, Mastorakis N. P300 detection in visual evoked potentials by clustering the hybrid features of brain signals to classify alcoholics and controls. Rec Res Elect Engineer 2014; 2014: 231-8.
[33]
Deistler M, Dunsmuir W, Hannan EJ. Vector linear time series models: corrections and extensions. Adv Appl Probab 1978; 10(2): 360-72.
[http://dx.doi.org/10.2307/1426940]
[34]
Amini Z, Abootalebi V, Sadeghi MT. Comparison of performance of different feature extraction methods in detection of P300. Biocybern Biomed Eng 2013; 33(1): 3-20.
[http://dx.doi.org/10.1016/S0208-5216(13)70052-4]
[35]
Al-Fahoum AS, Al-Fraihat AA. Methods of EEG signal features extraction using linear analysis in frequency and time-frequency domains. ISRN Neurosci 2014; 2014730218
[http://dx.doi.org/10.1155/2014/730218] [PMID: 24967316]
[36]
Akaike H. Fitting autoregressive models for prediction. Ann Inst Stat Math 1969; 21(1): 243-7.
[http://dx.doi.org/10.1007/BF02532251]
[37]
Jurcak V, Tsuzuki D, Dan I. 10/20, 10/10, and 10/5 systems revisited: Their validity as relative head-surface-based positioning systems. Neuroimage 2007; 34(4): 1600-11.
[http://dx.doi.org/10.1016/j.neuroimage.2006.09.024] [PMID: 17207640]
[38]
Riccio A, Simione L, Schettini F, et al. Attention and P300-based BCI performance in people with amyotrophic lateral sclerosis. Front Hum Neurosci 2013; 7: 732.
[http://dx.doi.org/10.3389/fnhum.2013.00732] [PMID: 24282396]
[39]
Cipresso P, Carelli L, Solca F, et al. The use of P300-based BCIs in amyotrophic lateral sclerosis: from augmentative and alternative communication to cognitive assessment. Brain Behav 2012; 2(4): 479-98.
[http://dx.doi.org/10.1002/brb3.57] [PMID: 22950051]
[40]
Sahu M, Nagwani NK, Verma S. Applying auto regression techniques on amyotrophic lateral sclerosis patients EEG dataset with P300 Speller. Indian J Sci Technol 2016; 9(48)
[http://dx.doi.org/10.17485/ijst/2016/v9i48/109165]
[41]
Brunner C, Birbaumer N, Blankertz B, et al. BNCI Horizon 2020: towards a roadmap for the BCI community. Brain Comp Interf 2020; 2(1): 1-10.

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