Inverse kinematics of a 6 DoF human upper limb using ANFIS and ANN for anticipatory actuation in ADL-based physical Neurorehabilitation

Objective: This research is focused in the creation and validation of a solution to the inverse kinematics problem for a 6 degrees of freedom human upper limb. This system is intended to work within a realtime dysfunctional motion prediction system that allows anticipatory actuation in physical Neurorehabilitation under the assisted-as-needed paradigm. For this purpose, a multilayer perceptron-based and an ANFIS-based solution to the inverse kinematics problem are evaluated. Materials and methods: Both the multilayer perceptron-based and the ANFIS-based inverse kinematics methods have been trained with three-dimensional Cartesian positions corresponding to the end-effector of healthy human upper limbs that execute two different activities of the daily life: "serving water from a jar" and "picking up a bottle". Validation of the proposed methodologies has been performed by a 10 fold cross-validation procedure. Results: Once trained, the systems are able to map 3D positions of the end-effector to the corresponding healthy biomechanical configurations. A high mean correlation coefficient and a low root mean squared error have been found for both the multilayer perceptron and ANFIS-based methods. Conclusions: The obtained results indicate that both systems effectively solve the inverse kinematics problem, but, due to its low computational load, crucial in real-time applications, along with its high performance, a multilayer perceptron-based solution, consisting in 3 input neurons, 1 hidden layer with 3 neurons and 6 output neurons has been considered the most appropriated for the target application

An automatic diabetes diagnosis system based on LDA-Wavelet Support Vector Machine Classifier

In this paper, an automatic diagnosis system for diabetes on Linear Discriminant Analysis (LDA) and Morlet Wavelet Support Vector Machine Classifier: LDA-MWSVM is introduced. The structure of this automatic system based on LDA-MWSVM for the diagnosis of diabetes is composed of three stages: The feature extraction and feature reduction stage by using the Linear Discriminant Analysis (LDA) method and the classification stage by using Morlet Wavelet Support Vector Machine (MWSVM) classifier stage. The Linear Discriminant Analysis (LDA) is used to separate features variables between healthy and patient (diabetes) data in the first stage. The healthy and patient (diabetes) features obtained in the first stage are given to inputs of the MWSVM classifier in the second stage. Finally, in the third stage, the correct diagnosis performance of this automatic system based on LDA-MWSVM for the diagnosis of diabetes is calculated by using sensitivity and specificity analysis, classification accuracy, and confusion matrix, respectively. The classification accuracy of this system was obtained at about 89.74%. (C) 2011 Elsevier Ltd. All rights reserved

An Expert Diagnosis System for Parkinson Disease Based on Genetic Algorithm-Wavelet Kernel-Extreme Learning Machine

Parkinson disease is a major public health problem all around the world. This paper proposes an expert disease diagnosis system for Parkinson disease based on genetic algorithm- (GA-) wavelet kernel- (WK-) Extreme Learning Machines (ELM). The classifier used in this paper is single layer neural network (SLNN) and it is trained by the ELM learning method. The Parkinson disease datasets are obtained from the UCI machine learning database. In wavelet kernel-Extreme Learning Machine (WK-ELM) structure, there are three adjustable parameters of wavelet kernel. These parameters and the numbers of hidden neurons play a major role in the performance of ELM. In this study, the optimum values of these parameters and the numbers of hidden neurons of ELM were obtained by using a genetic algorithm (GA). The performance of the proposed GA-WK-ELM method is evaluated using statical methods such as classification accuracy, sensitivity and specificity analysis, and ROC curves. The calculated highest classification accuracy of the proposed GA-WK-ELM method is found as 96.81%

Klinik Metisiline Dirençli Staphylococcus Aureus İzolatlarında Daptomisin Etkinliğinin İncelenmesi

Amaç: Tüm dünyada ve Türkiye’de metisiline dirençli stafilokoklara bağlı infeksiyonlar tedaviye yanıtsızlık ve maliyet artışı gibi önemli sorunlara neden olmaktadır. Daptomisin Gram pozitif hücre zarına bağlanarak dirençli suşlara karşı hızlı bakterisidal etki gösteren bir antibiyotiktir.Gereç ve Yöntem: Çalışmamıza laboratuvarımıza gönderilen klinik örneklerden izole edilen 26 metisiline dirençli Staphylococcus aureus (MRSA) suşu dahil edilmiştir. Daptomisin için minimum inhibitör konsantrasyon (MİK) değerleri E-test (AB Biodisk, Solna, Sweden) yöntemiyle belirlenmiş ve Clinical and Laboratory Standards Institute (CLSI) önerileri doğrultusunda değerlendirilmiştir. MİK değeri ≤ 1 μg/ml olarak tespit edilen izolatlar daptomisine duyarlı kabul edilmiştir. İzolatların diğer antibiyotiklere duyarlılıkları ise CLSI önerileri doğrultusunda Kirby-Bauer disk difüzyon yöntemiyle saptanmıştır.Bulgular: Çalışılan tüm MRSA suşları daptomisine duyarlı olarak bulunmuştur. MİK50 ve MİK90 değerleri sırasıyla 0.125 μg/ml, 0.25 μg/ml olarak saptanmıştır.Sonuç: Çalışmamızda, daptomisinin MRSA suşlarında in vitro etkinliğe sahip olduğu tespit edilmiştir. Daptomisin, MRSA suşları ile gelişen enfeksiyonların tedavisinde alternatif bir tedavi seçeneği olarak düşünülebilir.Anahtar kelimeler: Daptomisin, E test, MİK, MRSA, Staphylococcus aureu

A new intelligent hepatitis diagnosis system: PCA-LSSVM

In this study, a method based on Principle Component Analysis and Least Square Support Vector Machine Classifier for Expert Hepatitis Diagnosis System (PCA-LSSVM) is introduced. This intelligent diagnosis system deals with combination of the feature extraction and classification. This intelligent hepatitis diagnosis system is separated into two phases: (1) the feature extraction from hepatitis diseases database and feature reduction by PCA, (2) the classification by LSSVM classifier. The hepatitis diseases features were obtained from UCI Repository of Machine Learning Databases. The number of these feature attributes are 19. Then, number of these features was reduced to 10 from 19 by using PCA. In second phase, these reduced features are given to inputs LSSVM classifier. The correct diagnosis performance of the PCA-LSSVM intelligent diagnosis system for hepatitis disease is estimated by using classification accuracy, sensitivity and specifity analysis respectively. (C) 2011 Elsevier Ltd. All rights reserved

The Lipid Parameters and Lipoprotein(a) Excess in Hashimoto Thyroiditis

Objective. The risk of atherosclerotic heart disease is increased in autoimmune thyroiditis, although the reason is not clear. Lipoprotein(a) (Lp(a)) excess has been identified as a powerful predictor of premature atherosclerotic vascular diseases. The aim of this study is to investigate the relationship between Lp(a) levels and thyroid hormones in Hashimoto patients. Method. 154 premenopausal female Hashimoto patients (48 patients with overthypothyroid (OH), 50 patients with subclinical hypothyroid (SH), and 56 patients with euthyroid Hashimoto to (EH)) were enrolled in this study. The control group consists of 50 age matched volunteers. In every group, thyroid function tests and lipid parameters with Lp(a) were measured. Lp(a) excess was defined as Lp(a) > 30 mg/dL. Results. Total-C, LDL-C, TG, and Lp(a) levels were increased in Hashimoto group. Total-C, LDL-C, and TG levels were higher in SH group than in the control group. Total-C and LDL-C levels were also higher in EH group compared to controls. Lp(a) levels were similar in SH and EH groups with controls. However, excess Lp(a) was more common in subclinical hypothyroid and euthyroid Hashimoto group than in the control group. Conclusion. The Total-C and LDL-C levels and excess Lp(a) were higher even in euthyroid Hashimoto patients. Thyroid autoimmunity may have some effect on Lp(a) and lipid metabolism

Reduced Pain and Anxiety With Music and Noise-canceling Headphones During Shockwave Lithotripsy

OBJECTIVE: We assessed the effects of music and noise-canceling headphones (NCHs) on perceived patient pain and anxiety from extracorporeal shockwave lithotripsy (SWL). PATIENTS AND METHODS: Patients with renal calculi scheduled for SWL were prospectively enrolled. All 89 patients between the ages of 19 and 80 years were informed about this study and then randomized into three groups: Group 1 (controls), no headphones and music; Group 2, music with NCHs (patients listened to Turkish classical music with NCHs during SWL); and Group 3, music with non-NCHs (patients listened to Turkish classical music with non-NCHs during SWL). Hemodynamic and respiratory parameters were recorded before and just after the SWL session. All patient visual analog scale (VAS) and State-Trait Anxiety Inventory (STAI) scores were recorded just after the SWL procedure. RESULTS: There were significant differences in VAS scores among the groups (5.1, 3.6, and 4.5, respectively, p \u3c 0.001), including between Groups 2 and 3 (p = 0.018). There were also significant differences in STAI-State anxiety scores among the groups (43.1, 33.5, and 38.9, respectively, p = 0.001), including between Groups 2 and 3 (p = 0.04). CONCLUSIONS: Music therapy during SWL reduced pain and anxiety. Music therapy with NCHs was more effective for pain and anxiety reduction. To reduce pain and anxiety, nonpharmacologic therapies such as music therapy with NCHs during SWL should be investigated further and used routinely

Patterns, Risks and Outcomes of Urethral Recurrence After Radical Cystectomy for Urothelial Cancer; Over 20 Year Single Center Experience

Purpose: To evaluate the factors affecting urethral recurrence after radical cystectomy for bladder cancer and relationship between urinary diversion type and urethral recurrence rates. Patients and methods: In our 504 radical cystectomy series, 287 male patients whose final pathological were urothelial carcinoma were included in the study. The relationship between urethral recurrence and pathological stage, grade, lymph node involvement and diversion type was researched in addition to risk factors for urethral recurrence. Results: A Total of 287 patients. Orthotopic continent urinary diversion (OCD) and ileal conduit (IC) was performed after radical cystectomy in 141 (49.1%) and 146 (50.9%) patients respectively. Urethral recurrence was observed in 11 (3.8%) patients and urethral recurrence rates in OCD and IC groups were 1.4% and 6.2% (p = 0.034). Pathological stages of recurrent patients were 2 pT1, 1 pT2 and 8 pT4 respectively (p < 0.001). Urethral recurrence was significantly lower in OCD group when compared to IC group (p = 0.036). When all parameters were analyzed using Cox multivariate regression analysis, the most important factor that affects urethral recurrence was pathological T stage (p < 0.001). Risk factors for urethral recurrence were present in 92 patients. Urethral recurrence rates in patients with and without risk factors were 8.69% and 1.53% (p < 0.01). Conclusions: In this study, pathological stage was found to be the most important factor affecting urethral recurrence and prostatic stromal invasion was an important prognostic factor in these cases. Although risk factors for urethral recurrence were similar in both groups, urethral recurrence rates were significantly lower in OCD group when compared to IC group. (C) 2014 Surgical Associates Ltd. Published by Elsevier Ltd. All rights reserved.WoSScopu