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Gait Deviation Index for the assessment of normal pressure hydrocephalus
S8
ESMAC Abstracts 2015 / Gait & Posture 42S (2015) S1–S101
Session OS02 Rehab Adults
Session OS03 Muscle Function and Imaging
Gait Deviation Index for the assessment of normal pressure hydrocephalus
Wavelet spectra of surface EMG during gait in children with di- and tetraplegic spastic cerebral palsy
P. Lucareli 1,∗ , M. Bernal 2 , S. Lacerda 2 , I. Hideyo 2 , S. Garbelotti 3 , D. Speciali 2
K. Bracht-Schweizer 1,∗ , J. Romkes 1 , B. Göpfert 1 , R. Brunner 2 , E. Rutz 2
1
Universidade Nove de Julho, Sao Paulo, Brazil Hospital Israelita Albert Einstein, Sao Paulo, Brazil 3 Centro Universitário São Camilo, Sao Paulo, Brazil 2
Research question: Gait Deviation Index (GDI) is sensitive to detect changes in gait function after tapping of cerebrospinal fluid on gait function? Introduction: Idiopathic Normal Pressure Hydrocephalus (iNPH) is a syndrome consisting of chronic ventricular dilation, normal cerebrospinal fluid (CSF) pressure and the symptomatic triad of dementia, gait dysfunction and urinary incontinence. Gait disturbance are usually the initial sign and most important symptom, but its objective evaluation has not been established. The tap test (TT) is commonly used to prognosticate shunt responsiveness. Clinical improvement following TT is one of the few established prognostic indicators of a positive response to shunting in patients with iNPH. Materials and methods: Eighty-six consecutive patients (male: 57; female: 29) of mean age 77 (0.9) years with clinical diagnosis of iNPH were referred to gait analysis laboratory in Hospital Israelita Albert Einstein (HIAE) to participated in this study. The gait assessment was conducted using three-dimensional gait analysis pre and post TT. The reconstruction, markers label and the Plugin Gait biomechanical model processing to obtain kinematic data were performed using Vicon Nexus’ software. The kinematic data were imported into a spreadsheet, where a mathematical routine was used to calculate the GDI. The statistical analysis used paired t-test with the level of statistical significance set at p < 0.05 and Cohen’s d was used to measure the effect size and for power analysis purposes. Cohen’s d effect size measurements were calculated and defined as follows <0.2 trivial, 0.2–0.5 small, 0.5–0.8 medium and >0.8 large. Results: Differences were found between pre and post tests for GDI for both sides and with large effect size (Table 1). Discussion: The literature examining gait parameters after TT in iNPH is limited and often based on clinicians’ subjective ratings as opposed to objective measures of change. The results of this study demonstrate that GDI is likely to change following TT. The results suggest that this index is sensitive and provide a quantitative measure of the changes in gait pattern of pre and post TT.
1 Laboratory of Movement Analysis, University of Basel Children’s Hospital, Basel, Switzerland 2 Neuro-Orthopaedic Departement, University of Basel Children’s Hospital, Basel, Switzerland
Research question: How do the wavelet spectra of surface electromyography (SEMG) of leg muscles during gait differ between children with diplegic spastic cerebral palsy (DI-CP), children with tetraplegic spastic cerebral palsy (TE-CP), and typically developing children (TD). Introduction: Muscle function in CP patients can be measured by SEMG. Lauer [1] reported that the instantaneous mean frequency (IMNF) extracted from a wavelet analysis of SEMG recordings has the potential of scaling motor involvement in CP. Wavelet analysis could help to understand the cause of muscle control impairment in CP. Materials and methods: SEMG and three dimensional kinematic gait data (VICON, PiG-Model) of 20 TE-CP, 22 DI-CP, and 20 age-matched TD children were analysed retrospectively. SEMGs of the gastrocnemius medialis (GM), tibialis anterior (TA), rectus femoris (RF), and medial hamstrings (HM) muscles were collected (SENIAM guidelines, Biovision amplifiers, 2400 or 2520 Hz). The SEMG signals were wavelet transformed [2] and wavelets 3–13 of 6 gait cycles were averaged per subject. The peak normalised totalintensity (Itot ) was computed. The IMNF and the active muscle time based on the threshold (mean of Itot ) between the three groups were compared. Results: The patients walked with severe kinematic deviations based on the Gait Profile Score [3] DI-CP: 20.8 ± 8.0◦ , TE-CP: 21.5 ± 6.4◦ , TD: 6.4 ± 1.9◦ . Both CP groups presented increased muscle activity of 6.2–18.3% in GM (Fig. 1), RF, and HM over the gait cycle. The frequency spectra for both CP groups were similar in shape but were shifted toward higher frequencies (Fig. 2). IMNF did not differ between the DI-CP and TE-CP groups but were significantly lower in TD (GM: 189 ± 24 Hz, 183 ± 24 Hz, 153 ± 16 Hz; TA: 182 ± 17 Hz, 169 ± 16 Hz, 150 ± 14 Hz; RF: 151 ± 15 Hz, 145 ± 10 Hz, 128 ± 8 Hz; HM: 168 ± 16 Hz, 161 ± 27 Hz, 140 ± 12 Hz).
Table 1
GDI
Left Right
Pre
Post
Cohen’s d
68.1 (7.1) 66.9 (7.1)
78.1 (3.1) 76.1 (2.1)
1.2 1.7
http://dx.doi.org/10.1016/j.gaitpost.2015.06.022
Fig. 1. Wavelet analysis of the m. gastrocnemius medialis for the three study groups.
ESMAC Abstracts 2015 / Gait & Posture 42S (2015) S1–S101
Session OS02 Rehab Adults
Session OS03 Muscle Function and Imaging
Gait Deviation Index for the assessment of normal pressure hydrocephalus
Wavelet spectra of surface EMG during gait in children with di- and tetraplegic spastic cerebral palsy
P. Lucareli 1,∗ , M. Bernal 2 , S. Lacerda 2 , I. Hideyo 2 , S. Garbelotti 3 , D. Speciali 2
K. Bracht-Schweizer 1,∗ , J. Romkes 1 , B. Göpfert 1 , R. Brunner 2 , E. Rutz 2
1
Universidade Nove de Julho, Sao Paulo, Brazil Hospital Israelita Albert Einstein, Sao Paulo, Brazil 3 Centro Universitário São Camilo, Sao Paulo, Brazil 2
Research question: Gait Deviation Index (GDI) is sensitive to detect changes in gait function after tapping of cerebrospinal fluid on gait function? Introduction: Idiopathic Normal Pressure Hydrocephalus (iNPH) is a syndrome consisting of chronic ventricular dilation, normal cerebrospinal fluid (CSF) pressure and the symptomatic triad of dementia, gait dysfunction and urinary incontinence. Gait disturbance are usually the initial sign and most important symptom, but its objective evaluation has not been established. The tap test (TT) is commonly used to prognosticate shunt responsiveness. Clinical improvement following TT is one of the few established prognostic indicators of a positive response to shunting in patients with iNPH. Materials and methods: Eighty-six consecutive patients (male: 57; female: 29) of mean age 77 (0.9) years with clinical diagnosis of iNPH were referred to gait analysis laboratory in Hospital Israelita Albert Einstein (HIAE) to participated in this study. The gait assessment was conducted using three-dimensional gait analysis pre and post TT. The reconstruction, markers label and the Plugin Gait biomechanical model processing to obtain kinematic data were performed using Vicon Nexus’ software. The kinematic data were imported into a spreadsheet, where a mathematical routine was used to calculate the GDI. The statistical analysis used paired t-test with the level of statistical significance set at p < 0.05 and Cohen’s d was used to measure the effect size and for power analysis purposes. Cohen’s d effect size measurements were calculated and defined as follows <0.2 trivial, 0.2–0.5 small, 0.5–0.8 medium and >0.8 large. Results: Differences were found between pre and post tests for GDI for both sides and with large effect size (Table 1). Discussion: The literature examining gait parameters after TT in iNPH is limited and often based on clinicians’ subjective ratings as opposed to objective measures of change. The results of this study demonstrate that GDI is likely to change following TT. The results suggest that this index is sensitive and provide a quantitative measure of the changes in gait pattern of pre and post TT.
1 Laboratory of Movement Analysis, University of Basel Children’s Hospital, Basel, Switzerland 2 Neuro-Orthopaedic Departement, University of Basel Children’s Hospital, Basel, Switzerland
Research question: How do the wavelet spectra of surface electromyography (SEMG) of leg muscles during gait differ between children with diplegic spastic cerebral palsy (DI-CP), children with tetraplegic spastic cerebral palsy (TE-CP), and typically developing children (TD). Introduction: Muscle function in CP patients can be measured by SEMG. Lauer [1] reported that the instantaneous mean frequency (IMNF) extracted from a wavelet analysis of SEMG recordings has the potential of scaling motor involvement in CP. Wavelet analysis could help to understand the cause of muscle control impairment in CP. Materials and methods: SEMG and three dimensional kinematic gait data (VICON, PiG-Model) of 20 TE-CP, 22 DI-CP, and 20 age-matched TD children were analysed retrospectively. SEMGs of the gastrocnemius medialis (GM), tibialis anterior (TA), rectus femoris (RF), and medial hamstrings (HM) muscles were collected (SENIAM guidelines, Biovision amplifiers, 2400 or 2520 Hz). The SEMG signals were wavelet transformed [2] and wavelets 3–13 of 6 gait cycles were averaged per subject. The peak normalised totalintensity (Itot ) was computed. The IMNF and the active muscle time based on the threshold (mean of Itot ) between the three groups were compared. Results: The patients walked with severe kinematic deviations based on the Gait Profile Score [3] DI-CP: 20.8 ± 8.0◦ , TE-CP: 21.5 ± 6.4◦ , TD: 6.4 ± 1.9◦ . Both CP groups presented increased muscle activity of 6.2–18.3% in GM (Fig. 1), RF, and HM over the gait cycle. The frequency spectra for both CP groups were similar in shape but were shifted toward higher frequencies (Fig. 2). IMNF did not differ between the DI-CP and TE-CP groups but were significantly lower in TD (GM: 189 ± 24 Hz, 183 ± 24 Hz, 153 ± 16 Hz; TA: 182 ± 17 Hz, 169 ± 16 Hz, 150 ± 14 Hz; RF: 151 ± 15 Hz, 145 ± 10 Hz, 128 ± 8 Hz; HM: 168 ± 16 Hz, 161 ± 27 Hz, 140 ± 12 Hz).
Table 1
GDI
Left Right
Pre
Post
Cohen’s d
68.1 (7.1) 66.9 (7.1)
78.1 (3.1) 76.1 (2.1)
1.2 1.7
http://dx.doi.org/10.1016/j.gaitpost.2015.06.022
Fig. 1. Wavelet analysis of the m. gastrocnemius medialis for the three study groups.