Lumbar spinal canal stenosis (LSS) is the most common cause of lumbar spinal surgery in patients older than 65 years.1–4 Lumbar spinal canal stenosis is diagnosed based on physical examination and radiological documentation of lumbar spinal canal narrowing.5,6 The differential diagnosis of the level of lumbar radiculopathy is difficult in multilevel spinal stenosis. Therefore, the current authors focused on gait analysis as a classification method to improve diagnostic accuracy.
Several studies have reported gait analysis in patients with LSS.7–9 However, almost all studies used specialized equipment, such as a ground reaction force plate. This equipment is expensive and is only available at a few medical facilities. Furthermore, it is difficult to assess the walking motion of patients using a ground reaction force plate. The current authors aimed to use a simple examination method for motion analysis and to identify and quantify gait characteristics in patients with LSS. Using the gait characteristics identified with their method, the authors constructed a classification for differentiating between healthy individuals and patients with L4 and L5 radiculopathy using a support vector machine (SVM). This machine is a learning tool originating from modern statistical learning theory. Recently, SVM learning has been widely used for solving classification problems in the medical field.10–15
This study was performed in 2 phases. In phase 1, gait characteristics of L4 and L5 radiculopathy in LSS patients were identified. In phase 2, L4 and L5 radiculopathy was classified using an SVM.
Materials and Methods
This study was approved by the ethics committee of Kouseiren Takaoka Hospital. Written informed consent was obtained from all participants.
The study group comprised 13 healthy volunteers (control group) and 33 patients with neurological intermittent claudication due to LSS. The healthy volunteers (5 men and 8 women; mean age, 40.2 years [range, 22–55 years]) had no neurological or arthritic diseases causing gait disturbance. Patients with LSS were categorized into 2 groups: those with L4 radiculopathy (L4 group) and those with L5 radiculopathy (L5 group) based on the findings of physical examination, magnetic resonance imaging (MRI), computed tomography (CT), myelography, and selective nerve root block. The L4 group comprised 7 men and 4 women (mean age, 70.2 years [range, 56–80 years]), and the L5 group comprised 9 men and 13 women (mean age, 73.8 years [range, 57–86 years]).
Handmade light-emitting diode markers were attached at 5 sites (acromion, anterior superior iliac spine, fibular head, lateral malleolus of the ankle joint, and fifth metatarsal head) on the affected side (Figure 1). Furthermore, all participants underwent examination of load walk on a treadmill in a dim room. The experiment was performed with the treadmill at 0° of ramp incline. Walking speed was free (ie, each participant's preferred walking speed). The authors stopped performing measurements if a participant could not walk due to lower limb pain. If a participant did not feel pain, he or she walked for 5 minutes. Commercially available digital cameras recorded the walking motion (Figure 2). The authors performed the motion analysis for 10 seconds just before the walking stopped using their development program. The accuracy of this system depends on the resolution of the camera. The distance between the treadmill and camera was from 0.007 to 0.04 rad for the authors' set-up.
Positioning of light-emitting diode markers on the participant. The markers were attached at the following sites: acromion, anterior superior iliac spine, fibular head, lateral malleolus of the ankle joint, and fifth metatarsal head.
Walking measurement system. Walking was assessed on a treadmill, and a digital camera recorded the motion.
Joint movement was visualized as a waveform. The waveforms were compared between the 3 groups. The authors mainly focused on lower limb motion and examined the joint angle. Unlike the clinical joint angle, the authors defined each joint angle as shown in Figure 3. In addition, the authors focused on the gastrocnemius and quadriceps muscles because these were considered to be the problem areas. Individual differences in body size made direct comparison of muscle lengths illogical; therefore, a reference model based on the bone lengths of an actual human skeleton model were used. The authors based the sites of attachment of the muscles to the bones in the reference model on anatomical data.16 Using this model and these angle data, the authors calculated normalized maximum expanded muscle length (normalized maximum length), normalized maximum contracted muscle length (normalized minimum length), and normalized motion range.
Sagittal plane view. The angle of each joint is defined in the schema.
Statistical evaluation was performed using one-way analysis of variance (ANOVA). Post-hoc tests performed were indicated by ANOVA results using Tukey's test for multiple comparisons. A P value less than .05 was considered statistically significant.
Eighteen participants were able to walk for 5 minutes (13, 3, and 2 participants in the control, L4, and L5 groups, respectively).
Waveform of the Knee Joint in Normal Gait. The waveform of the knee joint looks like the letter M (ie, there are 2 waves). The first wave peaks at the time of heel contact on the treadmill (Figure 4). Then, small knee flexion occurs at the transition between the loading response and mid-stance, contributing to controlled shock absorption at the knee; the local minimum knee flexion was recorded as a notch (Figure 4). After the limb advances over the stationary foot, the knee completes its extension during the terminal stance (Figure 4). The latter large wave of flexion occurs during the swing phase and assists with foot clearance (Figure 4).
The waveform of the knee joint in normal gait. Circles indicate positions at which light-emitting diode markers were attached.
Waveform of the Ankle Joint in Normal Gait. The ankle joint takes on the position of maximum plantar flexion for strong kicking when the leg leaves the treadmill (Figure 5). Subsequently, the walking motion goes into the swing phase. Then, the heel makes contact with the treadmill with dorsiflexion of the ankle joint (Figure 5), and the wave of plantar flexion occurs when the foot is flat on the treadmill (Figure 5). Then, as the foot goes backward due to the treadmill motion, dorsiflexion of the ankle joint occurs (Figure 5). As a result, the waveform of the ankle joint has bimodal peaks.
The waveform of the ankle joint in normal gait. Circles indicate positions at which light-emitting diode markers were attached.
Gait Characteristics.Figure 6 shows the waveform of a representative case in each group. In the knee joint of the L4 group, the waveform was similar to that of participants with normal gait, but knee extension at initial contact was slightly greater than that of the other groups. The waveform of the ankle joint was also similar to the pattern of normal gait. In the ankle joint of the L5 group, the one-peak waveform pattern with disappearance of the second peak was observed in 10 (45.5%) of 22 cases. This was not observed in the control group and was observed in only 1 patient in the L4 group.
The waveform of representative cases in each group. Waveform of the knee joint: solid line=22-year-old man in the control group; dotted line=56-year-old man in the L4 group; perforated line=81-year-old man in the L5 group (A). Waveform of the ankle joint: solid line=48-year-old man in the control group; dotted line=56-year-old man in the L4 group; perforated line=82-year-old woman in the L5 group (B).
The authors identified the following 10 potential gait characteristics of each group to use as the parameters of the SVM: amplitude of the ankle angle during the swing phase, average angle of the ankle joint during the swing phase, angle of the knee joint at initial contact in the stance phase, amplitude of the hip angle, normalized maximum length of the gastrocnemius, normalized minimum length of the gastrocnemius, normalized motion range of the gastrocnemius, normalized maximum length of the quadriceps, normalized minimum length of the quadriceps and normalized motion range of the quadriceps. Table 1 shows the results of Tukey's multiple comparison tests. Although several factors showed no significant difference between the groups, all factors were used in the SVM for classification of L4 and L5 radiculopathy in phase 2.
Potential Factors of Gait Characteristics
Materials and Methods
An SVM classifier was used for data analysis.17 The SVM is originally a binary classifier; however, given the goal of the differentiation in this data set, the authors were able to appropriately judge which group (normal, L4, or L5 group) the data belong to. Therefore, the “one-versus-the-rest” method was used. In this method, 3 binary SVM classifiers were applied, to allow classification of the normal and the others, the L4 and the others, and the L5 and the others. Finally, the group with maximum possibility was chosen. Radial basis function was chosen as the kernel function. The previous 10 factors were normalized and used as SVM parameters. Appropriate parameters were selected for each binary classifier based on the significant differences detected in the phase 1 study.
Table 2 shows the classification results as a confusion matrix. The total classification accuracy was 80.4%, supporting the efficacy of the presented approach. The highest classification accuracy was obtained in the control group and the lowest in the L4 group. In the control group, 2 cases were mistaken for the L4 group. In the L4 group, 2 cases were mistaken for the L5 group and 1 case was mistaken for the control group. In the L5 group, 2 cases were mistaken for the control group and 2 cases were mistaken for the L4 group.
Results of the Classification
Many studies have performed gait analysis using large ground reaction force plates. Those studies determined the gait speed, stride, pitch, sway, and rhythm. However, the authors wanted to visualize and quantify the gait characteristics in patients with LSS based on the kinematic and morphologic standpoint, focusing on lower limb motion. The optimal classification method would consist of an examination using a simple instrument; this examination could be easily performed even by nonspecialists. Therefore, the authors aimed to develop a simple examination method for walking motion analysis.
The authors' method can be used to analyze walking motion without special equipment such as a force plate and 3-dimensional motion analysis. Compared with walking on the ground, walking on a treadmill is a nonphysiologic condition. However, treadmills offer many advantages in human locomotion analysis.18 Space requirements are constrained, environmental factors can be controlled, steady-state locomotion speeds are selectable, and successive repetitive strides can be documented expeditiously. Thus, use of a treadmill could circumvent some problems inherent in overground locomotion studies. The findings of normal gait in the current study were generally consistent with the results of other overground locomotion studies and indicated the validity of the authors' method.19–21
When gait characteristics of neurogenic disease are evaluated from the kinematic and morphologic aspects, 2 mechanisms should be considered. The first mechanism is that spastic and flaccid paralysis affects the joint motion through muscles; this is an essential finding that strongly reflects the nature of the disease. Another mechanism is the secondary finding of unconsciously performed gait strategy, such as avoidance reaction for pain and compensatory reaction for paralysis. Abnormalities in gait style have been noted soon after patients began to walk,7 suggesting that patients acquire a walking style that precludes the appearance of symptoms.
In the current study, the gait characteristic of the L4 group was knee extension at initial contact. In cases with quadriceps weakness, knee hyperextension occurs to reduce the demand on a weak quadriceps at initial contact. In the current study, no patients showed changes in the manual muscle test before and after the walking test. However, in the L4 group, the authors considered that even if patients had no obvious muscle weakness, they acquired walking motion to avoid a broken knee as gait strategy. The gait characteristic of the L5 group was the one-peak waveform pattern in the ankle joint. Because the toe and heel make contact with the ground simultaneously by foot drop, the second peak caused by the time lag between the toe and heel making contact with the ground disappears, and the waveform shows a one-peak pattern. Therefore, this finding can be explained by the weakness of the tibialis anterior muscle innervated by the L5 nerve root. The authors focused on the lower limb motion and identified the 10 factors that had the potential to be gait characteristics of each group. However, there may have been more effective classification factors that were not identified.
Several studies have reported gait analysis in patients with LSS.7–9 Suda et al7 evaluated gait improvement after surgery in patients with neurogenic intermittent claudication. Papadakis et al8 compared the gait patterns of healthy people with those of patients with LSS; they also evaluated the postoperative progression of the gait pattern of patients with LSS and showed that gait variability decreased relative to the preoperative gait pattern.9 In contrast, the current study examined walking motion with regard to the gait characteristics of each level of lumbar radiculopathy and classified it among the control, L4, and L5 groups using the SVM. The SVM was first proposed by Vapnik22 and has since attracted a high degree of interest in the machine learning research community. Support vector machine methods have the feasibility and superior ability to extract higher-order statistics and have become extremely popular for classification and prediction. Children with cerebral palsy–related gait have been identified and classified via an SVM-based method.23 The total classification accuracy was greater than 80% using the SVM. The authors consider that identification of other factors for differentiating between the L4 group and the other groups may further improve the classification performance of the SVM.
The current study has some limitations, including small sample size and different participant backgrounds in each group. There are several differences in walking motion associated with sex and age, and it is necessary to match patient backgrounds in future studies.
The authors developed a new and simple examination method for walking motion analysis and identified several factors useful for differentiating between normal healthy people and patients with L4 and L5 radiculopathy. In addition, the derived factors were used to construct an SVM-based differentiation method, with a high accuracy rate. The authors' future work will focus on improving the classification accuracy of this diagnostic method.
- Andreisek G, Imhof M, Wertli M, et al. A systematic review of semiquantitative and qualitative radiologic criteria for the diagnosis of lumbar spinal stenosis. AJR Am J Roentgenol. 2013; 201(5):W735–W746. doi:10.2214/AJR.12.10163 [CrossRef]
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- Kim YS, Park SJ, Oh IS, Kwan JY. The clinical effect of gait load test in two level lumbar spinal stenosis. Asian Spine J. 2009; 3(2):96–100. doi:10.4184/asj.2009.3.2.96 [CrossRef]
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- Papadakis NC, Christakis DG, Tzagarakis GN, et al. Gait variability measurements in lumbar spinal stenosis patients: Part B. Preoperative versus postoperative gait variability. Physiol Meas. 2009; 30(11):1187–1195. doi:10.1088/0967-3334/30/11/004 [CrossRef]
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Potential Factors of Gait Characteristics
|Amplitude of ankle angle during swing phase|
| Control group||14.86||5.42|
| L4 group||12.94||4.88||.520||.002b||.066|
| L5 group||9.31||3.02|
|Average angle of ankle joint during swing phase|
| Control group||126.33||8.59|
| L4 group||121.05||8.02||.337||.001b||.129|
| L5 group||114.42||9.75|
|Angle of knee joint at stance phase start time|
| Control group||164.45||9.42|
| L4 group||169.55||7.23||.344||.938||.154|
| L5 group||163.40||9.13|
|Amplitude of hip angle|
| Control group||35.37||6.27|
| L4 group||28.07||6.30||.009b||<.001b||.053|
| L5 group||22.96||5.16|
|Normalized maximum length of gastrocnemius|
| Control group||1.056||0.020|
| L4 group||1.063||0.023||.744||.052||.317|
| L5 group||1.075||0.023|
|Normalized minimum length of gastrocnemius|
| Control group||0.966||0.019|
| L4 group||0.994||0.022||.023b||<.001b||.012b|
| L5 group||1.021||0.029|
|Normalized motion range of gastrocnemius|
| Control group||0.091||0.016|
| L4 group||0.069||0.026||.035b||<.001b||.105|
| L5 group||0.054||0.018|
|Normalized maximum length of quadriceps|
| Control group||1.314||0.004|
| L4 group||1.304||0.007||.025b||<.001||.219|
| L5 group||1.299||0.011|
|Normalized minimum length of quadriceps|
| Control group||1.264||0.007|
| L4 group||1.255||0.010||.092||.855||.157|
| L5 group||1.262||0.011|
|Normalized motion range of quadriceps|
| Control group||0.050||0.006|
| L4 group||0.049||0.006||.949||<.001b||<.001b|
| L5 group||0.037||0.007|
Results of the Classification
|Actual Class/Predicted Class||Group, No.||Accuracy|
|Control group (n=13)||11||2||0||84.6%|
|L4 group (n=11)||1||8||2||72.7%|
|L5 group (n=22)||2||2||18||81.8%|