Esta investigación sistematiza el diagnóstico clínico especializado para cuantifica la condición del sujeto en términos patológicos de la Hemiplejia Espástica. English Translation, Synonyms, Definitions and Usage Examples of Spanish Word ‘hemiplejía espástica’. espástica spastic colitis – colitis (Ё) espástica spastic diplegia – diplejía spastic hemiparesis – hemiparesia (Ё) espástica spastic hemiplegia – hemiplejía .
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Spastic hemiplegia gait characterization using support vector machines: Spastic Hemiplegia SH is a brain motor dysfunction with neuromuscular implications on one side of the body which leads to gait disorders. The gait of such dysfunction has been described and classified in terms of its affected side lower limb known as ipsilateral limb measurements using manual analytical methods. It has been assumed that the unaffected side limb known as contralateral limb compensates gait deviations due to the abnormal pattern of the ipsilateral limb.
But in gait, the behavior of both limbs is highly correlated so analysis of the contralateral side should prove useful, although there are a lack of studies regarding contralateral limbs. This study is part of an ongoing effort to analyze the SH gait pathology in terms of limbs of both sides and it begins with the relationship between ipsilateral SH gait pattern classification versus contralateral limb compensating pattern.
In this work, the focus has been on the kinematics of the unaffected contralateral limb of the disorder taking advantage of high profile statistical learning computational methods, such as Support Vector Machines SVM models. Results showed that consistent types of SH kinematics patterns can be found, described and also characterized using a SVM model. Further improvements in the accuracy of SH classification and characterization are under way. Clinical gait analysis record interpretation is a valuable instrument in the study and comprehension of the effects of neuro-muscular-skeletal pathologies in human gait.
Kinematics angles and rotationskinetics joint moment and power and physiological records, such as electromyograms EMG and energy consumption, can be used to give a complete parametric description of the gait process of a patient; for further insight in normal and pathological gait refer to Gage and Perry Normal gait can be verified when certain prerequisites are accomplished, such as performance within normal gait patterns, high energetic consumption efficiency, etc.
The compensation that occurs as a response to gait pathology has the goal of achieving a viable gait, and minimizing then deviation with respect to the normal pattern and the consumption of metabolic energy, which increases dramatically in pathological gait Gage SH consists of a motor compromise of the upper and lower limbs of one side of the body, due to the lack of neurological control, accompanied by muscular stiffness due to abnormal muscle tone increase.
The affected limb is referred to as the ipsilateral limb, and the non-affected limb is called contralateral limb. Since one of the lower limbs is affected, SH therefore affects the gait pattern of the patient.
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In order to apply either surgical or espatica treatments for the improvement of the gait pattern of a subject, specialists interpret the information extracted from a clinical gait analysis kinematics, kinetics, electromyography and energetic consumption from which they establish correlations based in the coherence of the results. Data from the lower limbs can be contrasted with pre-established normal patterns, serving as a base for diagnosis and treatment recommendation.
This method is widely applied to children with neuromuscular diseases, mainly with CP, specifically in the case of SH, mielomeningocele, etc. GagePerryDavis Through the use of gait analysis and kinematics espasticx of the ipsilateral limb, six distinct types of Eespastica can be recognized Gage referred to in this article by roman numerals, Types I to VI.
The first four types I, II, III, and IV are determined exclusively based only in kinematics data, while the remaining two types V and VI need complementary parameters obtained from kinetics plots studies.
Each of these types has a particular movement pattern and a specific group of related treatments; surgical and non-surgical Gage Although the mentioned classification scheme has contributed, up to now, in the specification of hemipllejia for the different types of SH, a continuous search for improving its effectives is in place.
However, gait dynamics and followups of patients with SH, that underwent treatment; suggest that there might exist factors which have not been taken into consideration in the current classification, therefore affecting the post-operatory outcome.
Such factors could stem from the functional compensation of the espastuca limb. The use of SVM has extended to pattern recognition application, such as face recognition in an image, with data with similar variability as the one present in gait patterns.
The main focus of this research is the hemi;lejia of the kinematics patterns of the contralateral limb in patients with SH, in order to verify the presence or not espawtica differentiating patterns from the normal one due to compensating responses.
This article is the first in a series regarding the impact of the study of the contralateral limb behaviour and the contributions hemipeljia it could bring to gait analysis. The results and techniques obtained from these studies will server as a basis for comparison with future methods and procedures. Model for Statistical Learning and Data Mining. In the last ten years, SVM has become a practical pattern recognition model for learning from examples, such as the case of gait patterns.
SVM hemipleja become a very useful tool for feature extraction, capable of finding subtle or diffuse patterns in complex problems with a minimum set of ad hoc parameters. Based on the Structural Risk Minimization Principle and Vapnik-Chervonenkis Theory better known as the VC Theory Vapnikthe model focuses on finding the optimal decision surface in terms of the linear function 1 or its modified version 2. These coefficients are fine-tuned at the SVM espwstica phase and reach their natural stable value during optimization when the data is separable.
For example, K hemiplejoa convert f x into a polynomial classifier using. A qualitative example of a SVM transformation of a function in a feature space where it becomes linear. Each of these kernel functions are used under restricted conditions. The more intuitive condition is. The non-separable case is solved by including error penalty variables which translate in espasrica formulation by creating an upper bound on the quadratic optimization variables.
SVM has become a very useful tool for feature extraction, finding hemmiplejia or diffuse patterns in complex problems with a minimum set of ad hoc parameters. In order to solve multiclass problems using SVM the usual approach is to generate several binary classifiers between the classes or between one class and the rest of them, in order to construct the final set of classifiers Platt In Figure 2, a shows a vector sample constructed with SH information and b illustrates a classifier surface for a SH classification task.
Figure 2 a illustrates how kinematics variables are organized as espaztica vector, there is a hemilejia of details in axis labels due to the scale; the vertical axes labels refers to pelvis attitude and joint angles in degrees, and horizontal axis represents gait cycle samples for those kinematics variables.
The data groups, SVM classification hemiolejia and pattern characterization procedure is explained as follows: KAD is a device used by biomechanical model reconstruction algorithms in order calculate certain critical angles, instead of manual input. In this way the hip center was estimated based on the distance between hejiplejia iliac spines.
Kinematics and kinetics data were espstica with VCM 1. A espastuca of records were divided into two groups: Several features of these samples are: Other patterns group records Labeled as Group 5, Table 1 included: For this study, only sagittal plane kinematics variables were used: A guide for this angle definition is shown in Figure 3. Evaluation of the existence of consistent patterns in each type category was performed using a Support Vector pattern recognition model, in order to generate a statistical classification rule between one SH type pattern versus the remaining classes.
The Support Vector Machine Model. The aforementioned kinematics variables: Such was achieved by connecting the data of the aforementioned variables in a singular string in the same manner data from an image is arrange for pattern recognitionusing the order generally utilize by medical experts research of the impact of variable order in the input vector effectiveness for revealing patterns is part of ongoing efforts ; in Figure 3 an example of an input vector can be observed.
The SVM scheme used was developed for gait kinematics studies Salazar SVM parameters C and N were tuned manually, in order to obtain their best classification performance. The determination of C by analytical methods does not apply when working with SVM in feature spaces high number of dimensions spaces and when the separability of data is too complicated to be analyzed; generally for these cases a trial and error approach is utilized. Sensibility, specificity, negative predictive and positive predictive values were calculated for each case.
Tables 2 and 3 summarize the results of the experiments using the statistical learning tool selected following the protocols described in Salazar Averaged kinematics pattern for unaffected contralateral limb Type I SH. Averaged kinematics patterns for unaffected contralateral limb type IV SH.
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It produces an increase in the hip flexion between terminal swing T-S and the end of mid stance M-S. The espatsica showed a pattern that is similar to the normal, but with a slight dorsiflexion, which is more evident in pre-swing P-S and initial swing I-S. This espastiica be due to a pelvic descends in order to compensates the other limb which is in stance at that time.
The proportion between stance phase and swing phases are practically normal. The upper and lower standard deviation boundaries around the average pattern look very consistent and uniform along the gait cycle for all levels. At the ankle, the initial H-C and LR are done in normal pattern. Also, hip flexion resembles that for Type I and is near the normal limits even though it is slightly increased from T-S to the end of M-S.
Knee extension is more premature half of M-S and the flexion espaatica swing is restricted in a few degrees. Espastixa is a great variability for all levels during the swing, but more specifically at pelvis, hip and knee.
At the ankle, the major variability in observed between P-S and initial swing. High consistency small standard deviation for the knee and ankle during stance is observed. A remarkable lordosis is observed, in the M-S and in terminal swing it reaches 15o respect to normal. In terminal stance it is reduced until almost 7o which indicates a strong antero-posterior swinging of the trunk. The hip flexion is very augmented, which is expected, reaching an excess of 12o with respect to normal for initial heel contact around 47o and more than 15o with respect to normal in terminal swing.
The raise in hip flexion is maintained almost to the end of the mid stance, then being hyper-extended slightly from the end of P-S to almost half of the M-S. After the half of M-S until P-S, a recurvatum can be observed, and it reaches a knee hyper-extension of 10o, then a delayed flexion between normal limits and finally, end the gait cycle with almost 10o in flexion. Ankle flexion is like normal until the middle of M-S, and between normal limits until P-S, however, the plantar flexion is restricted until the end of initial swing where it reaches only around 10o, and then returns to neutral or slightly dorsal in the rest of swing phase.
The pattern consistency is good for the ankle and for the pelvic tilt in swing and terminal stance. Another range of augmented variability is observed in hip flexion during the first two sub phases of stance, and in the knee in P-S and initial swing.
There is a consistent kinematics pattern for Types I to IV SH for the unaffected contralateral limb, and SVM models can be used successfully as a classifying tool in gait patterns. Support vectors form an important dataset to analyze, because they represent the kinematic pattern that makes the classification difficult and it could be related with the inconsistencies and critical points in SH characterization.
The widening of standard deviation band could be due to variability either in amplitude or in the stride, stance and swing durations.
It is recommended to increase the number of patients and records.
For further studies, in the inclusion of all kinematics variables instead of just the sagittal plane should be considered. All authors would like to give an acknowledgment to Danirida Urbano, P. For example, K can convert f x into a polynomial classifier using, Figure 1. Kinematics angles definition Analysis Method and Experiments. Clinical Gait Analysis Interpretation: An Approach and Proposed Enhancement.
Gait Analysis in Cerebral Palsy. Gait Analysis, Normal and Pathological Function. Lower extremity kinematics during level walking. Journal of Orthopaedic Research, Vol. Advances in Neural Information Processing Systems. Cambridge, USA, Volume 12, p.