Total en bloc Spondylectomyの脊髄循環に及ぼす影響について

取得学位 : 博士（医学）, 学位授与番号 : 医博乙第1303号, 学位授与年月日：平成6年6月1日,学位授与年：199

Main disease classification of intermittent claudication via L1-regularized SVM

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

Main disease classification of intermittent claudication via L1-regularized SVM

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. © 2013 IEEE

Identification of the Causative Disease of Intermittent Claudication through Walking Motion Analysis: Feature Analysis and Differentiation

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%

Study on differentiation factors for main disease identification of intermittent claudication

Intermittent Claudication [1] 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 [2], 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. © 2012 IEEE

Walking motion analysis of intermittent claudication and its application to medical diagnosis

There are mainly two kinds of diseases in intermittent claudication. One is lumbar spinal canal stenosis (LSS) and the other is peripheral arterial disease (PAD). Differentiating LSS and PAD is a critical issue. Wrong differentiation might cause amputation of lower extremities. At small clinics and hospitals, simple and cheap differentiation system is required. Concerning this, this paper investigated walking motions of the patients. The subject with LED markers walked on the treadmill until she or he felt pain. We recorded the walking motion by camera and tracked the LED markers. Treadmill enables to measure walking motion for a long time in a small space, and LED marker provides position of every joint in the walking. Then, we can get the information such as joint angle trajectory, hemi-foot step, stance and swing phases without any other sensors like foot switch or force plate. We compared walking motions of healthy persons, LSS patients and PAD patients, found their features and 3 factors for disease differentiation; average bending angle of knee joint at the start of stance phase, average dorsiflexion angle of ankle joint, and average hemi-foot step length. The results indicate that 2 dimensional images of walking motion for several seconds are enough for deriving the factors. Then, we can construct the simple examination system for the disease differentiation. © 2010 IEEE