Study on differentiation factors for main disease identification of intermittent claudication.
ABSTRACT Intermittent Claudication  is a walking symptom. After a short time walking, patients suffer from pains at lower limbs. But if taking a rest, the pains can be relieved and they can walk again. Unfortunately, it arises from not one but mainly two kinds of diseases: LSS (lumber spinal canal stenosis) and PAD (peripheral arterial disease). Additionally, it is reported that symptom is similar and LSS groups is furthermore divided into two main groups: L4 and L5 groups. Therefore, it is clinically very important to differentiate which diseases the patients suffer from, PAD, L4 or L5. We aims at developing the system to differentiate them from short walking motion data. In our previous paper , we derived differentiation factors, but did not consider the difference between L4 and L5 and the results are limited. This paper focuses on biarticular muscles associated with the diseases, and derive new and effective differentiation factors. The results supports their effectiveness and validity.
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ABSTRACT: Intermittent claudication is a walking symptom. Patients with intermittent claudication experience lower limb pain after walking for a short time. However, rest relieves the pain and allows the patient to walk again. Unfortunately, this symptom predominantly arises from not 1 but 2 different diseases: LSS (lumber spinal canal stenosis) and PAD (peripheral arterial disease). Patients with LSS can be subdivided by the affected vertebra into 2 main groups: L4 and L5. It is clinically very important to determine whether patients with intermittent claudication suffer from PAD, L4, or L5. This paper presents a novel SVM- (support vector machine-) based methodology for such discrimination/differentiation using minimally required data, simple walking motion data in the sagittal plane. We constructed a simple walking measurement system that is easy to set up and calibrate and suitable for use by nonspecialists in small spaces. We analyzed the obtained gait patterns and derived input parameters for SVM that are also visually detectable and medically meaningful/consistent differentiation features. We present a differentiation methodology utilizing an SVM classifier. Leave-one-out cross-validation of differentiation/classification by this method yielded a total accuracy of 83%.01/2014; 2014:861529. DOI:10.1155/2014/861529
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ABSTRACT: There are multiple diseases that cause intermittent claudication, including lumber spinal canal stenosis (LSS) and peripheral arterial disease (PAD). LSS is categorized on the basis of the diseased part: L4 and L5. The medical treatment for these groups is totally different and the differentiation is important. With this in mind, we examined walking-motion data for patients and derived several features for the differentiation in previous studies. However, these features were not specialized for classification, and there is no guarantee that the features are effective for real differentiation. The present study investigates the possibility of differentiation by gait analysis, via use of an L1-regularized support vector machine (SVM). An L1-regularized SVM can execute both classification and feature selections simultaneously. On the basis of this method, our paper presents the methodology for classifying the underlying disease of the intermittent claudication with an accuracy of 79.7%. In addition, new effective features for the differentiation are extracted.Conference proceedings: ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Conference 07/2013; 2013:6409-6412. DOI:10.1109/EMBC.2013.6611021