**2.5. Statistical analysis**

*DD DD DD DD DD DD SO SO SO GA SO TA SO VA SO RF SO BF*

é ù ê ú

ë û

gastrocnemius (GA), soleus (SO), vastus medialis (VM), and biceps femoris (BF).

0

The outcome is averaged over the number of combinations (equation 7).

<sup>6</sup> <sup>1</sup> 2

è ø

(*SY N*6) while controlling for the number of combinations ((

) =15 for four muscles, (

5 2 1 , , 1 , <sup>5</sup>

( ) 6 6 <sup>1</sup> <sup>1</sup>

*SYN ij i j DET DET bi i ji*

6 5

*SYN i ij SYN j tonumber of gait speedconditions GS j*

0

three muscles (*SY Ntri*

6 4

three muscles, (

speed.

0

138 Electrodiagnosis in New Frontiers of Clinical Research

The delta function negates the elements of the matrix located on the diagonal (equation 6) for the EMG from the following muscles: rectus femoris (RF), tibialis anterior (TA), lateral

0

Finally, the technique calculates the sum of the square roots of the Equation 7 matrix elements.

=- = å å d<sup>¼</sup> æ ö = = ç ÷

Other components of the SYNERGOS requires the calculation of combinations of % DET of

Finally, for each subject, to obtain a single SYNERGOS index for the EMG signals during each gait speed, the root mean square of the five SYNERGOS indices obtained from the clustered EMG signals (five gait cycles per gait speed; see Recurrence Quantification Analysis) were calculated (equation 8). This single value represented the quantified MMA during each gait

ë û

*DD DD DD DD DD SO GA SO TA SO VA SO RF SO BF*

é ù ê ú

*DD DD DD DD GA TA GA VA GA RF GA BF*

*DD DD DD DD DD GA GA GA TA GA VA GA RF GA BF*

*DD DD DD DD TA TA TA VA TA RF TA BF*

0

), four muscles (*SY Nquad* ), five muscles (*SY N*5), and six muscles

<sup>å</sup> <sup>=</sup> = = (8)

)=6 for five muscles, (

6 2

> 6 6

*DD DD VA RF VA BF*

*DD DD DD TA VA TA RF TA BF*

*DD DD DD VA VA VA RF VA BF*

*DD DD RF RF RF BF*

0

) =15 for two muscles, (

)=1 for six muscles).

*D D RF BF*

*D D BF BF*

(5)

(6)

(7)

6 3

) =20 for

To analyze the efficiency of the proposed method, a restricted maximum likelihood linear mixed model was employed to identify changes in MMA measured by SYNERGOS associated with gait pattern (i.e. walk or run) and with changing gait speed. The model included three fixed effects, speed, pattern (walk or run), and speed-by-pattern interaction, and two random effects, subjects and measurement error (i.e., random within-subject variation). This analytical approach is similar to repeated measures analysis of variance in that it accounts for depend‐ ency resulting from multiple measures per subject but unlike analysis of variance does not require the same number of measures for each subject. The fixed effects were used to test the study hypotheses. The random effects were used to compute intraclass correlation coefficients (ICC) of type (2,1) (i.e., degree of consistency among measures) [43] and the corresponding standard errors of measurement (SEm) as relative and absolute reliability estimates, respec‐ tively (i.e. indicators of the consistency and precision of the SYNERGOS measure). The significance level was set at p≤ 0.05. The analysis was conducted by using SPSS 16.0.1 (SPSS Inc., Chicago, Illinois, USA).
