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Stroke I

Ahmet YARDIMCI Department of Biomedical Equipment Technology, TBMYO Akdeniz University, Kampus, 07059 Antalya, Turkiye e-mail:yardimci@akdeniz.edu.tr web:www.ahmetyardimci.com. Stroke I.

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Stroke I

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  1. Ahmet YARDIMCIDepartment of Biomedical Equipment Technology, TBMYOAkdeniz University, Kampus, 07059 Antalya, Turkiyee-mail:yardimci@akdeniz.edu.trweb:www.ahmetyardimci.com

  2. Stroke I • Stroke is the most common neurologic disease that leads to death and disability in the elderly population. Every year, a significant number of stroke patients survive and are left with significant disabilities. Hemiparesis is the most common cause of disability after stroke, affecting 70 –85% of all patients, and it has been estimated that 60% of all surviving stroke patients may require rehabilitation treatment. It is important to identify effective stroke rehabilitation strategies as the number of stroke survivors and medical costs increase.

  3. Stroke II • Different treatment strategies for the rehabilitation of hemiplegic patients are available today, such as conventional exercise programs, proprioceptive neuromuscular facilitation techniques, muscle strengthening and physical conditioning programs, neurophysiologic approaches, and functional electrical stimulation. • Rehabilitation techniques have been more successful in restoring function in the lower limbs than in the upper limbs. The assesment of rehabilitation period is very important to find a correct method for treatment process. The aim of this study find a new way to assesment of hemiplegic patients gate.

  4. Study Aim of the study Classification of Hemiplegic Patients • Methods • Fuzzy Logic ? • Neuro Fuzzy (ANFIS) ? • NN ? Stage 1. Discrimination of subject situation Healthy?Patient? Stage 2. Classification of patients’ Brunnstrom stages III, IV, V, VI

  5. Software • Matlab V6.5, Mathworks • FuzzyTECH V5.54d Professional edition, Inform

  6. Data gathering Information about Measurements Subject: 7 healthy elderly subjects and 26 hemiplegic patients Parameter: Waist acceleration Condition: Walking on corridor Instruction: With orthosis and/or cane (hemiplegic patients) Sampling: 1024Hz

  7. Statistics about subjects

  8. What we do to reach our aims? • Find significant amplitude features of signals, • Find symmetry features of signals, • Decide to inputs of classification system, • Decide to rule blocks, • Find suitable rules for all conditions ( consult a specialist), • Test the system with your own data, • Test the system with blind approach (find test data which is not included the your own data), • Turn back if the system response does not satisfy you, • Check all steps again from 3 to 8.

  9. Three orthogonal acceleration signals from a normal healthy subject walking at a normal speed* *Evans AL, Duncan G, Gilchrist W. Recording accelerations in body movements. Med.& Biol. Eng. & Comput., 1991, 29, 102-104

  10. Description of accelerometer signal* *Bussmann JBJ, Damen L, Stam HJ. Analysis and decomposition of signals obtained by thigh-fixed uni-axial accelerometry during normal walking.Med.Biol.Eng.Comput.,2000, 38, 632-638

  11. Temporal events in stroke hemiparesis* Features • Walking speed • Stride period • Cadence • Stride length • Stance period • Swing Period • Stance/swing ratio • Double support • Stance symmetry • Swing symmetry * Sandra JO, Richards C.Hemiparetic gait following stroke. Part I : Characteristics. Gait & Posture 4 (1996) 136-148

  12. Temporal events in stroke hemiparesis* Features • Walking speed • Stride period • Cadence • Stride length • Stance period • Swing Period • Stance/swing ratio • Double support • Stance symmetry • Swing symmetry All of them temporal gait variables!.. Measurement and analysis of those variables did not further characterize the pathologic nature of locomotion in hemiplegic patients. Because most of the relevant temporal information in hemiplegic gaits is included in the measurement of walking speed.

  13. Some measurable features of gait • Walking speed m/sn • Cadence step/min • Step length m • Double step length m • Step time difference • (Mean step time= Mean step length/ walking speed STD= MSTL - MSTR ) • Double step time difference • (Double mean step time= double mean step length/ walking speed DSTD= DMSTL - DMSTR )

  14. Some important notes from literature Prior studies revealed that temporal variables of hemiplegic gait, (walking speed and symmetry of the swing phases) are significantly related to motor recovery as classified according to defined stages. Hemiplegic patients, even those with good motor recovery, by comparison all walked much more slowly. Walking speed was related to the clinical status of the patient, being progressively slower as the motor deficit became more severe. Walking speed is an important temporal variable of hemiplegics gait, as reported by many investigators. There are several algorithms to compute step times and quantifying symmetry. (Aminian et al., Sadeghi et al., Robinson et al., Ganguli et al., Vagenas et al.)

  15. Symmetry and Laterality Quantification* Gait symmetry has been defined as a perfect agreement between the actions of the lower limbs. A way of categorizing different means of determining whether or not symmetry and laterality exist between the lower limbs using indices and statistical analysis. *Sadeghi H, Allard P, Prince F, Labelle H, Symmetry and Limb dominance in able-bodied gait:a review. Gait and Posture 12 (2000) 34-45

  16. Symmetry and Laterality Quantification* In pathological gait, marked differences have been noted between the affected and unaffected limbs. Asymmetrical properties were reported for 34 gait variables in a group of 31 hemiplegic subjects*. The gait of hemiparetic patients was characterized by slower velocity and more asymmetry as they swayed more laterally on the unaffected leg compared to healthy persons. *Sadeghi H, Allard P, Prince F, Labelle H, Symmetry and Limb dominance in able-bodied gait:a review. Gait and Posture 12 (2000) 34-45

  17. To determines asymmetries: Symmetry Index (SI) 1 Robinson RO, Herzog W, Nigg BM. Use of force platform variables to quantify the effects of chiropractic manipulation on gait symmetry. J Manipulative Physiol Ther 1987;10:172-6 2 Ganguli S, Mizrahi J, Bose KS. Gait evaluation of unilateral below–knee amputees fitted with patellar-tendon-bearing prostheses. J Ind Med Assoc 1974;63(8):256-9 R= XR / XL 3 Vagenas G, Hoshizaki B. A multivariable analysis of lower extremity kinematic asymmetry in running. Int J Sports Biomech 1992; 8(1):11-29 4

  18. Lets see the differences between the SI’s in a sample problem A B 1 2 3 SI(A)= -%50 SI(B)= -%33 SI(A)= 0,6 SI(B)= 0,71 SI(A)= -%40 SI(B)= -%28 4 SI(A)= %25 SI(B)= %16 %0 %100 Symmetry Asymmetry

  19. Hemiplegic gate signals Healthy ST6 ST5 ST4 ST3 Anteroposterior Acceleration signals

  20. Feature of signal Amplitude of signal A Step1 time S1 Step2 time S2 Slope of signal? SL Two steps time T Absolute Step Difference ASD=S1-S2 Rate of Step Difference RSD=ASD / T

  21. Detection algorithm* After find the phl ptl , phr, ptr parameters; Duration of each gait cycle gc(i) = phr(i+1) – phr(i) 1 i N Left stance LS(i) = pt1(i) – phi(i) right stance RS(i) = ptr(i) – phr(i) Left double support LDS(i) = ptl(i) - phr(i) Right double support RDS = ptr(i) - phl(i) Aminian K, Rezahhanlou K, Andres E, Fritsch C, Leyvraz PF, Robert P. Temporal feature estimation during walking using miniature accelerometers: an analysis of gait improvement after hip arthroplasty. Medical&Biological Engineering& Computing, 1999 Vol.37,p.686-691

  22. Anteroposterior Step times Step time comparison

  23. Anteroposterior signal ranges and mean values indefinite

  24. Anteroposterior signal ranges and mean values

  25. Vertical acceleration signals

  26. Lateral acceleration signals

  27. Comparison of some features of vertical acceleration signals (Range, max, min)

  28. Comparison of some features of lateral acceleration signals (Range, max, min)

  29. Vertical and lateral acceleration signals mean ranges

  30. Fuzzy logic based classification A N A L Y S I S Temporal Features of Gait Signals Symmetri Features of signals Physiological Features of Subject Amplitudes of signals ?

  31. ZLR YVR S2 XAST XASI S1 XAR XAM Preferred features of acceleration signals

  32. System block diagram direct fuzzy classifier

  33. System Structure XAM XAR Signal Peak Features 81 rules YVR ZLR Main Decision Rule Block 25 rules XASI Signal Symmetry Features 9 rules XST

  34. Fuzzy logic system diagram

  35. Membership Functions of 1st Rule Block MBF of XAM MBF of XAR MBF of ZLR MBF of YVR

  36. Membership Functions of 2nd and 3rd Rule Blocks MBF of XASI MBF of XAST MBF of Classification

  37. Rules 1st RB 81 rules 2nd RB 3rd RB 25 rules

  38. Test (MoM)

  39. Test Results Healthy ST6 ST5 ST4 ST3 XAM,XAR,XASI,XAST,YVR,ZLR,Classification,__flags_ -0.2446,1.555,0.031,0.4183,1.211,1.0468,1,0 -0.157,0.7305,0.1575,0.6081,1.0172,0.9236,1,0 -0.1128,0.6394,0.101,0.6157,0.8952,0.6339,3,0 -0.047,0.5791,0.2849,1.06,1.2105,0.7844,3,0 0.025,5846,0.5633,2.39,0.8012,0.6479,5,0 Healthy Healthy ST3 ST3 ST5 + + + ST4 +

  40. Tests 1 2

  41. Classification Accuracy Assesment

  42. Classification Accuracy Assesment

  43. PMCC (Pearson product-moment correlation coefficient) results The Pearson coefficient is a statistic which estimates the correlation of the two given random variables. The linear equation that best describes the relationship between X and Y can be found by linear regression. This equation can be used to "predict" the value of one measurement from knowledge of the other. That is, for each value of X the equation calculates a value which is the best estimate of the values of Y corresponding the specific value. We denote this predicted variable by Y'. Correlation coefficient is 0.85

  44. Statistical Results 1 Successful discrimination rate of Patients Successful discrimination rate of Healthy Subjects 100% 100% 2 Successful classification rate of hemiplegic patients ST6→ 66% ST5→ 66% ST4→ 66% ST3→ 46% Good results for discrimination of subjects as healthy and patient! Low success for classification of ST3 patients. This study has shown that it is possible to discriminate subjects as healthy or patients with fuzzy logic approach. But successful classification of patients, due to the unstable behaviors of signals, is rather difficult than discrimination of subjects for fuzzy approach.

  45. Future works • Check the wrong results to find whether a failure in system. • Check all the rules. • If necessary do some fine arrangements on rules and membership functions. • Expand the system by adding new inputs.

  46. Future works XAM XAR Signal Peak Features 81 rules YVR ZLR Main Decision Rule Block ? XASI Signal Symmetry Features 9 rules XST Age Height Subjects Physiological Features ? Weight Gender

  47. Future works • Examine the literature on detection algorithms. • Develop an algorithm for detect precise moments and compute temporal parameters. • Make new measurements with using footswitch equipped shoe. • Try the neuro-fuzzy methods to produce membership functions and rule base from the data records. • Compare results with prior studies.

  48. Thank you for your attention!

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