Paper Menu >>
Journal Menu >>
Engineering, 2012, 5, 11-15 doi:10.4236/eng.2012.410B004 Published Online October 2012 (http://www.SciRP.org/journal/eng) Copyright © 2012 SciRes. ENG Case Study on Assessment of Mild Traumatic Brain Injury Using G ranular Computi ng Melaku A. Bogale1, Huiying Yu1, Thompson Sarkodie-Gyan1, Murad Alaqtash1, James Moody2, Richard Brower3 1Department of Electri cal an d Compu ter Engineering, Univ er sity of Texas at El Paso, El Paso, Texas, USA 2Men t is NeuroRehabili t atio n, El Paso , Texas, US A 3Department of Medi cal Educa tion, Texas Tech U niv ersity Health Sciences Cen ter, El Paso , Texas, US A Email: tsarkodi@utep.edu Received 2012 ABSTRACT Patients with mild traumatic brain injury complain about having balance and stability problems despite normal clinical examination. The objective of this study is to investigate the stride-to-stride gait variability of mTBI subjects while walking on treadmill under dual-task gait protocols. Fuzzy-granular computing algorithm is used to objectively quantify the stride-to-stride variability of tem- poral gait parameter s. The degrees o f similarity (DS) o f temporal gait p arameters in the dual tasks were det ermined from th e corres- ponding granulated time-series. The mTBI grou p sho wed rel ativel y smaller d egree o f similari t y for all wind ow sizes un der th e cogni- tive (dual) task walking, showing pronounced stride-to-stride variability. Different levels of DS among the mTBI subjects were ob- served. Individually, both healthy and mTBI group showed different DS under the two dual-tasks, reflecting t he chall engin g level o f the co gnit ive tasks whil e walkin g. The mean val ues of th e te mporal par ameters for t he mTBI gro u p wer e di fferen t fro m th e averaged normal refer ence. On the o ther hand , the individual variance analysis sho ws no sign ificant d ifferen ces between the no rmal and dual task values for some mTBI subjects. The granular approach however is able to reveal very fine differences and exhibited similar trends for all mTBI subjects. Different DS values among mTBI group could be indicative for the different severity level or the un- derg one re ha bilita t ion proc e s s. Keywords: Fuzzy Granular Algorithms; Fuzzy-similarity; Stride-to-stride Variability ; Temporal Gait Variables; Dual-task Gait Protocol; Mild Traumatic Brain Injury 1. Introduction Mild traumatic brain injury (mTBI) is one of the most common neurological disorders [12]. According to the Center for Dis- ease Control (CDC) [9], 75 % of head injuries are mild trau- matic brain injuries. Th e C D C acknowledged mTBI as a seri ous public health problem in the United States in its 2003 report [9] to the US congress. This report pointed out, mTBI is underes- timated by the cu rrent “surveil lance metho ds” and some people with mTBI show no sign of abnormalities under the clinical diagnosis techniques and made recommendation for further research and studies [9]. Furthermore, research findings in two studies [10,11] indicated that mTBI could be misdiagnosed and altered cognitive and behavioral functions may still exist even years after mTBI. Many peop le with mTBI su ffer from balan ce and stability problems even though the clinical neuropsycho- logical examinations show no sign of abnormality [10]. Failure of the clinical evaluation of mTBI in showing any clear mor- phological brain effects was reported in [21] despite patients’ complaints about cognitive and emotional difficulties after they were discharged from the hospital. Gaetz et al. reported the insensitivity of the standard clinical EEG technique to most brain functions change after mTBI [22]. Research studies [2,3,5,8,13,14,17,27,28] investigated into possible gait alterations of people after mTBI. Body sway measurements, under different visual inputs, while the subject is standing, gathered from force plate were used to quantifying balance and stability changes [2,14]. Motion capture systems [3,8,13,14,27,28] were used to study gait dynamics among the general TBI population. Li-Shan et al [15] studied dynamic instability using obstacle crossin g as a secondary task among the general traumatic brain injury patients. Gait stability after concussion was investigated using divided attention [27,28] among college athletes who sustained Grade 2 concussion. In [27] 10 college-age men and women who suffered a concussion and 10 uninjured matched control group performed dual task walking that consisted of two trials of walking: Normal walking (undivided attention) and walking while performing “mental-task”. These “mental- tasks” were randomly selected from a set of three dual-tasks comprising the spelling of a 5-letter word in reverse, subtrac- tion by seven and reciting the month of the year in reverse or- ders. The result of this study with respect to the spatial-temp o - ral gait p arameters showed that a significant slower gait veloci- ty, shorter stride-length, and longer stride-time during the dual-task walking trials in both healthy and the concussed group. However, the variation in stride-length and gait velocity did not show significant difference between the concussed and matched control group [27]. In an effort to study the effect of cognitive task on gait stability after concussion, Catena et al. [4] performed single task level walking and walking while per- forming cognitive tasks. They used the same cognitive tasks M. A. BOGALE ET AL. Copyright © 2012 SciRes. ENG 12 (recitin g th e mont hs o f th e year in r everse, su b tracti n g b y se ven and spelling a five letter word in reverse) as Parker et al., [28] in the first dual-task walking. The second dual-task walking was reaction-time (RT) test, where subjects responded by pressing a button when they heard an audible cue [4]. A differ- ence in spatial-temporal variables was reported in both healthy and concussed groups. Both groups exhibited slower speed in both dual tasks compared with the normal level walking. Long- er stride-time was observed among the concussed group. A significantly shorter stride-length and increased step width were observed during the cognitive task walking compared to the reaction-time test walking. Differen t d ual -task gai t protocols were shown to discriminate between able-bodied and mTBI groups [4,15,24,27,28]. How- ever, the current research of mTBI in dual-task paradigm is mostly focused on comparing the mean values of the spatial- temporal parameters of normal group with mTB I gr oup [27,28 ]. For people with neurological disorders, gait analysis is used to provide diagnosis, evaluation and treatment planning infor- mation. The benefit of gait analysis is so well established that it has now become a part of routine process in many rehabili ta- tion centers [16,25]. Recognition and understanding of “nor- mal” gait patterns and behavior are very important in the clini- cal gait analysis process for the purposes of identification of pathological gait. The observed or measured “normal” gait patterns or parameters serve as a reference or standard against which a p atholo gi cal gait can be compared. Studying gait parameters over a gait cycle, particularly, comparison of established reference patterns with that of the neurological impaired subject’s data over a cycle [19,23] is a common way of assessment and evaluation. However, wave- form anal ysis and comparis on of averaged gait par ameters over a gait cycle may not be sensitive enough to detect any subtle variation or irregularity in mTBI subjects’ gait parameters. We present a case s tudy for a possible application of granul ar computing to accomplish the required local comparisons and analysis. The purpose of this study is therefore to investigate the effect o f a secondar y cognit ive task on stab ility of temporal gait parameters using fuzzy information granulation. We spe- cifically aim to compute the similarity of temporal gait para- meters in the dual-task walking with that of the undivided at- tention walking, individually by calculating degree of similarity of each variable. A statistical variance analysis will also be presented for comparison purposes. We hypothesize that mTBI subjects would show more pronounced stride-to-stride vari abil- ity under the dual task walking conditions, and hence smaller degree of similarity and significantly different values from normal walki ng parameters. 2. Fuzzy Granulation Algorithm 2.1. Information Granulation Information granulation is an essential activity of human cogni- tion, information processing and communication [18 - 20]. The goal of information granulation is to better understand the problem and transform it into more tractable smaller parts, so that we have smaller sub problems with smaller computational complexity [6]. Information granules are established using set theory, rough sets, fuzzy sets, and shadow sets, etc., [6]. There are essential two steps in the granulation process, namely, seg- mentation and granular representation [19]. In the first phase we divide the original data into segments that retain the expe- rimental nature of the data. In the second phase we create a granular representation of each segment [6]. These two phases has competi ng goals, since we are t rying to acco mmodate more informatio n fo r experi ment al rel evance an d at the same ti me we de ma nd to be more specific in each information granule. Algo- rithmic optimization [6] approach aims to compromise these two conflicting goals. 2.2. Fuzzy-granulation Applied to Temporal Gait Parameters Given the original 100-point stride-time, stance-time and swi ng -ti me, ti me series dat a, th e goal of granu lat io n i s to divide the given original data points into smaller segments and represent each segment with a fuzzy membership function. Before doing any granulation, normalization was performed to min imize the effect of speed of walking [7] and individual dif- ferences in temporal gait variables .The data were normalized as, 00 00 min( ) max( )min( ) TT TTT − =− (1) where T0 is the original time-series d ata, max is maxi mu m, and min is mi nimum. We then divided the 100 cycles’ ti me series data in to several equal parts of different window sizes. Window sizes (w = 2, 4, 5) were used so that the original time-series is divided into segmen ts (gran ule) o f equal data po ints. Fi nally, a fuzzy tri angular membership function was designed based on the methods outlined [6,7] t o repr esent each gran ule. For each segment in the inter val [a, b], the triangular membership function is established as ,, () abm xm xma µ − =− , for axm≤≤ (2a) ,, () abm bx xbm µ − =− , for mxb≤≤ (2b) where m is the modal or core of the respective fuzzy set. The median of each segment is taken as the modal value [6]. To obtain the parameters, a and b of each fuzzy set, the optimiza- tion equation (3) was solved for each segment [1,6]. ,, 1 () ( ,)max k abm i i x Qab ba µ = =− ∑ (3) 2.3. Granular Matrix and Calculation of Degree of Similarity Next, we form the granular matrix, 3 () ij xp Gg= from each information granule represented by (, ,)amb where p is the number of segments [7]. The degree of similarity (DS) [7] be- tween two granulated time series 3 () ij xp Gg= and 3 () ij xp Hh= was calculated by M. A. BOGALE ET AL. Copyright © 2012 SciRes. E NG 13 3 11 3 11 () (, )() p ij ij ji p ij ij ji gh DS G Hgh = = = = ∧ = ∨ ∑∑ ∑∑ (4) whe re min(, ) ijijij ij gh isgh∧ and max(, ) ijijij ij gh isgh∨. The DS is within a range between 0 and 1. DS value of zero signi- fies no similarity at all and 1 represents 100 % similarity. A DS value closer to 1 indicates higher degree of similarity and DS values close to zero show little or no similarity. 3. Experimental Design and Methods 3.1. Participants The institutional review board (IRB) of The University of Tex- as at El Paso approved this study. Subjects obtained explana- tions about the study and are asked to sign informed consent prior to participation. Fifteen healthy male control subjects with no history of gait abnormalities are recruited from the El Paso community. Four male mTBI s ubjects are recr uited from a l ocal NeuroRehabilitation center in El Paso. Reported loss of con- sciousness for less than 30 minutes, post-traumatic amnesia les s than 24 hours and post-concussive symptoms (dizziness, mem- ory loss, headache, confusion) were used to diagnosis subjects with mild traumatic brain injury. 3.2. Experimental Protocol Both normal control and mTBI subjects performed treadmill walking at their comfortable speed for three minutes under three different conditions: 1) Undivided attention (refer as Normal walking), 2) Walking while reciting the months of the year in reverse order start ing from December (ref er as Dual task 1), and 3) Walking while subtracting by two starting from 299 (refer as Dual task 2.). These protocols are the standard in mental status examinations [26,27]. 3.3. Data Proc e ssing and Feature Extraction A dual-belt instrumented treadmill (Bertec®, USA) was used to measure th e groun d react ion forces (GR Fs) in three-d i me nsions. The speed of the treadmill is controllable and can be set at the subject’s comfortable speed. The force plates measure the ground reaction forces in 3D at 100Hz sampling frequency. Vertical GRF was filtered using a second order Butterworth low pass filter with cut-off frequency of 20 Hz. The vertical ground reaction force (vGRF) was used to define the gait cycles. A gait cycle begins at the instant one-foot strikes or contacts the ground and the instant when the same foot strikes the ground again marks the end of the gait cycle. The stance phase covers the duration from initial contact to toe-off and swing phase is Figure 1. Sample granulated time-series. defined from toe off to the next initial contact. The stride-time, stance-time and swing-time for 100 gait cycles were extracted for the three walking trial. The three walking trials temporal variable were segmented into different window sizes. A trian- gular fuzzy membership function was used to represent each segment as described ab ove in equations 2a and 2b . The granu- lar matrix for each walking set was then established from the respective values o f a, m, and b d etermined from the optimiza- tion equation. To study the effect of the cognitive task on stride-to-stride variability of the temporal gait parameters we calculat ed the degree of similari ty between th e granular matric- es built from the data in the normal walking, with that of the two dual tasks walking using equation (4). The reference degree of similarity was built from the average of the 15 able-bodied subj ects’ degree of similarities. 4. Results Figure 2 represents a sample granulated plot of stride time shown for window size w =5, we have twenty segments of the stride data each being represented by the respective triangular fuzzy-m ember s hips f unc ti on pa r a m e t e rs a, m and b. Table 1 shows th e calculated degrees of similarities of able- bodied (reference) and the four-mTBI subjects (PM01, PM02, PM03, PM04) for the three temporal variables (stride-time, stance-time and swing-time). DS (N, D1) represents the DS of normal walking temporal variable with that of walking with dual task 1 (reciting month of the year backwards). Similarly, DS (N, D2) stands for DS of normal walking temporal variable with dual task 2 (counting backwards) walking. The DS values for the three temporal parameters are relatively smaller than unity in the two dual task walking for both able-bodied and mTBI subj ects. Figure 2. Original swing-time for mTBI subject PM03. Tabl e 1. Mean values of temporal parameters. M. A. BOGALE ET AL. Copyright © 2012 SciRes. ENG 14 *No significant difference (p > 0.05) from the corresponding averaged control group; †No significance difference (p > 0.05) from their respective individual nor mal w alking val ues ; ††No significance difference (p > 0.1) from their respective individual normal wa lking va lues. Table 2. DS values for reference and mTBI subjects. The calculated DS for stride-time, stance-time and swing- time of the four-mTBI subjects are smaller than the DS of the reference gro up for all window size consi dered. The t wo-mTBI subjects (PM01 & PM04) relatively have a higher DS among the mTBI group though smaller than the normal group for all the tree temporal parameters (stride-time, stance-time and swi ng -time). The one-way ANOVA comparison between nor- mal walking and the two dual tasks for PM01 have p > 0.05 (Table 1), showing no difference at all. Also, the dual task 2 values of PM02 for stride-time and stance-time display no sig- nificance d ifference fro m the cor responding normal values (p > 0.05), but the DS values are quite different form the reference values. In this regard, the granular approach is shown to reveal very small differences, that otherwise would have been im- possible to pick up. Both the reference and mTBI group have a higher degree o f similarity in dual task 2 walking. The DS of swing-time for PM02 and PM03 suffered a si gnificant d ecrease in th e two dual task walking. These notable deviations are expected because looking back at the original swing-time series data of PM03 in the three trials (Figure 2), we observer different values and hence little similarity. 5. Discussion In this research study dual-task gait (with cognitive tasks) pro- tocols are proved to be able to discriminate able-bodied and neurologically challenged mTBI group in agreement with pre- vious research findings [15,27,28]. The proposed granular computing approach was shown to provide a simple parameter (DS) th at is capable o f revealing very fine individual differenc- es that otherwise would have been very difficult to pick up using the usual statistical variance analysis. This approach has a greater advantage over the statistical averaging methods pre- sented [4,15,27,28] because it furnishes a single individual parameter that can be used to individually follow and evaluate recovery process and outcome of an intervention. Our approach can easily be integrated into a clinical setting with real-time data processing. Particularly, this can be applied in sports where ind ivid u al baselin e perfo rmances of ath let es on an y dual- task gait protocol before a game could be collected and com- pared with post-game performance. Likewise we can extend this application to army soldiers where individual evaluation can be do ne before and after dep loyment . 6. Acknowledgements The authors would like to acknowledge the Computational Science program of University of Texas at El Paso and the Stern foundation for their financial support. These funds were used to support a graduate student in this project. The authors also would like to thank all subjects who participated in this study. REFERENCES [1] Bargiela, A., and Pedrycz, W. (2003) Granular Computing: An Introduction, Kluwer Academics Publishers, Boston, Dordrecht, and London. [2] Geurts, A.C.H., Ribbers, G.M., and Knoop, J.A. (1996) Identifi- cation of static and dynamic postural instability following trau- matic brain injury, Archives of Physical Medicine and Rehabili- tation, 7, 639 - 644. [3] Catena, R.D., van Donkelaar, P., and Chou, L. (2009) Different gait ta sks distin guish immedia te vs. long-ter m effects of concu s- sion on balance cont rol, Journal of NeuroEngi neering and Reha- bilitation, 6 :25. [4] Catena, R.D., and van Donkelaar, P. (2007) Cognitive task ef- fects on gait stability following concussion, Exp. Brain Res, 176, 23 - 31. [5] Niechwiej-Szwedo, E., Inness, E.L., Howe, J.A., Jagla, S., Mcllory, W.E., and Verrier, M.C. (2007) Changes in gait varia- bility during different challenges to mobility in patients with traumatic brain injury, Gait and posture, 1 170 -77. [6] Yu, F., and Pedrycz, W. (2009) The design of fuzzy information granules: Tradeoffs between specificity and experimental evi- dence, Applied Soft Computing, 9, 264 -273. [7] Yu, F., Chen, F., and Dong, K. (2005) A Granulation-ba sed method for finding similarity between time Series, Granular Computing, IEEE International Conference, 2, 700 - 703. [8] Williams, G., Morris, M.E., Schache, A., and McCrory, P.R. (2009) Incidence of gait abnormalities after traumatic brain in- jury, Archives of Physical Medicine. Rehabilitation, 4, 4587-4593. M. A. BOGALE ET AL. Copyright © 2012 SciRes. E NG 15 [9] Gerberding, J.L. and Binder, S. (2003) Report to congress on mild traumatic brain injury i n the United St ates. [10] Malece, J.F., (1999) Mild traumatic brain injury: scope of the problem. in: Varny NR, Roberts, J.R, editors, Evaluation and treatment of mild traumatic brain injury, Lawrence Erlbaum Publications 15 – 31. [11] Malec, J.F., and Cicerave, K.D. (2006) Cognitive rehabilitation, In: Evans, R., editor, Neurology and trauma, Oxford University Press 238 - 248. [12] Kurtzke, J.F. and Jurland, L.T. (1993) The epidemiology of neurological diseases, in: Joynt, R.J, editor, Clinical Neurology, Rev, Ph iladelphia JB Lippincott. [13] Basford, J.R., Chou, L., Kaufman, K.R., Brey, R.H., Walker, A., Malec, J.F. Moessner, A.M, and Brown, A.W. (2003) An as- sessment of gait and balance deficit after traumatic brain injury, Archives of Physical Medicine and Rehabilitation, 84, 343 - 349. [14] Lehmann, J.F., Boswell, S. R., Price, A., Burleigh, B.J., deLateur, K.M., and Jaff, D. H. (1990) Quantitative evaluation of sway as an indicator of functional balance in post-traumatic injury, Arc- hive s of Phys ical Me dici ne an d Reha bilit at i o n , 12 , 95 5 - 962. [15] Chou, L., Kaufman, K.R., Walker-Rabation, A.E., Brey, R.H., and Basford, J.R.(2004) Dynamic instability during obstacle crossing following traumatic brain injury, Gait and Posture, 20, 245 - 254. [16] Hausdroff, J. M. (2005) Gait Variability: methods, modeling and meaning, Journal of NeuroEngineering and Rehabilitation, 2 :19. [17] Wade, L.D ., Canning, C.G., Fowler, V., Felmingham, K.L., and Bagu ley, I.J. (1 997 ) Chan ge in postu ral sway and p erforman ce of functional tasks during rehabilitation after traumatic brain injury, Archives of Physical Medicine and Rehabilitation, 10, 107 - 1111. [18] Zadeh, L.A. (1997) Toward a theory of information granulation and it s centra lity in hu man reas oning and fuzzy logic, Fuzz y sets and System s , 90, 111 - 127. [19] Sarkodie-Gyan, T., Yu, H., Alaqtash, M., Abdelgawad, A., Spier, E., and Brower, R. (2011) Measurement of functional impair- ments in hu man locomoti on using patt ern ana lysis, Measurement, 44, 18 1 – 191. [20] Zadeh, L.A. (1979) Fuzzy sets and information granulity, in: M. Gupta, R. Ragade, R. Yager (Eds), Advances in fuzzy set theory and application , North-Holland , A ms te r dam, 3 - 18. [21] Ruijs, M.B., Keyser, A., and Gabreëls, F.J. (1990) Long-term sequelae of brain damage from closed head injury in children and adol escents, C l inic al Neurology and Neur o s ur g ery, 92, 323-328. [22] Gaetz, M., and Bernstein, D.M. (2001) The current status of electro physiologic procedure for the assessment of mild trau- matic brain injury, Journal of Head Trauma and Rehabilitation, 14,386 - 405. [23] Alaqtash, M., Yu, H., Brower, R., Abdelgawad, A., and Sarko- die-Gyan, T. (2011) Application of wearable sensors for human gait analysis using fuzzy computational algorithm, Engineering Application of Artificial Intelligence 6 1018 -1025. [24] Woollacott, M., and Shumway-Cook, A. (2002) Attention and the control of posture and gait: a review of an emerging area of research, Gait an d Po s t ur e, 16 , 1 -14. [25] Whittle, M.W. (2007) Gait Analysis an introduction, 4th edition, Elsevi er Ltd . [26] Bell, R., and Hall, R. C. (1997) The mental status examination, Amer ican F amil y Phy s icia n, 16, 1 45 – 152. [27] Osternig, L.R., Lee, H., and Chou, Li (2005) The effect of di- vided attention on gait stability following concussion, Clinical Biomechanics 20 389 - 395. [28] Parker, T. M., Osternig, L.R., Donkelaar, P.V., and Chou, Li. (2006) Gait stability following concussion, Medicine and Science in S ports a n d E x erci se, 38, 61032-61040. |