A novel wearable ERP-based BCI approach to explicate hunger necessity.

This study aimed to design a Brain-Computer Interface system to detect people's hunger status. EEG signals were recorded in various scenarios to create a database. We extracted the time-domain and frequency-domain features from these signals and applied them to the inputs of various Machine Learning algorithms. We compared the classification performances and reached the best-performing algorithm. The highest success score of 97.62% was achieved using the Multilayer Perceptron Neural Network algorithm in Event-Related Potential analysis.

Mathematical model of patella T-reflex and clinical evaluation with Ashworth scales.

Spasticity is one of the major problems that arise in different neurological diseases and seriously affect the quality of human life. Research on the understanding of mechanism of spasticity remains as important as the studies on the spasticity therapy and rehabilitation. In this study, the spasticity mechanism which develops concerning the upper motor neuron lesions is investigated by modelling "Patella tendon reflex triggered patella pendulum". The mathematical model based on the pendulum phenomenon is developed by solving the curve-fitting problem as finding the curve that best fits a set of data points. Electrophysiological and dynamic measurement data were taken from 76 spastic subjects and 20 healthy participants. The mathematical model is determined by the morphological properties of the goniometric variations. The results denote that the mathematical model containing two clinically relevant parameters -frequency component of the damped oscillatory motion defined as "f 0 " with the maximum angle of the reflex defined as "a 0 " ensures to distinguish spasticity from healthy subjects.

Physiological parameters as a tool in the diagnosis of fibromyalgia syndrome in females: A preliminary study.

AIMS: Although fibromyalgia (FM) syndrome is associated with many symptoms, there is as yet no specific finding or laboratory test diagnostic of this syndrome. The physical examination and laboratory tests may be helpful in figuring out this syndrome. MATERIALS AND METHODS: The heart rate, respiration rate, body temperature (TEMP), height, body weight, hemoglobin level, erythrocyte sedimentation rate, white blood cell count, platelet count (PLT), rheumatoid factor and C-reactive protein levels and electrocardiograms (ECG) of FM patients were compared with those of control individuals. In addition, the predictive value of these tests was evaluated via receiver operating characteristic (ROC) analysis. KEY FINDINGS: The results showed that the TEMP and the PLT were higher in the FM group compared with the control group. Also, ST heights in ECGs which corresponds to a period of ventricle systolic depolarization, showed evidence of a difference between the FM and the control groups. There was no difference observed in terms of the other parameters. According to the ROC analysis, PLT, TEMP and ST height have predictive capacities in FM. SIGNIFICANCE: Changes in hormonal factors, peripheral blood circulation, autonomous system activity disorders, inflammatory incidents, etc., may explain the increased TEMP in the FM patients. The high PLT level may signify a thromboproliferation or a possible compensation caused by a PLT functional disorder. ST depression in FM patients may interrelate with coronary pathology. Elucidating the pathophysiology underlying the increases in TEMP and PLT and the decreases in ST height may help to explain the etiology of FM.

A Study on the Effects of Sympathetic Skin Response Parameters in Diagnosis of Fibromyalgia Using Artificial Neural Networks.

Fibromyalgia syndrome (FMS), usually observed commonly in females over age 30, is a rheumatic disease accompanied by extensive chronic pain. In the diagnosis of the disease non-objective psychological tests and physiological tests and laboratory test results are evaluated and clinical experiences stand out. However, these tests are insufficient in differentiating FMS with similar diseases that demonstrate symptoms of extensive pain. Thus, objective tests that would help the diagnosis are needed. This study analyzes the effect of sympathetic skin response (SSR) parameters on the auxiliary tests used in FMS diagnosis, the laboratory tests and physiological tests. The study was conducted in Suleyman Demirel University, Faculty of Medicine, Physical Medicine and Rehabilitation Clinic in Turkey with 60 patients diagnosed with FMS for the first time and a control group of 30 healthy individuals. In the study all participants underwent laboratory tests (blood tests), certain physiological tests (pulsation, skin temperature, respiration) and SSR measurements. The test data and SSR parameters obtained were classified using artificial neural network (ANN). Finally, in the ANN framework, where only laboratory and physiological test results were used as input, a simulation result of 96.51 % was obtained, which demonstrated diagnostic accuracy. This data, with the addition of SSR parameter values obtained increased to 97.67 %. This result including SSR parameters - meaning a higher diagnostic accuracy - demonstrated that SSR could be a new auxillary diagnostic method that could be used in the diagnosis of FMS.

Investigation of the relationship between anxiety and heart rate variability in fibromyalgia: A new quantitative approach to evaluate anxiety level in fibromyalgia syndrome.

BACKGROUND: Fibromyalgia syndrome (FMS) is identified by widespread musculoskeletal pain, sleep disturbance, nonrestorative sleep, fatigue, morning stiffness and anxiety. Anxiety is very common in Fibromyalgia and generally leads to a misdiagnosis. Self-rated Beck Anxiety Inventory (BAI) and doctor-rated Hamilton Anxiety Inventory (HAM-A) are frequently used by specialists to determine anxiety that accompanies fibromyalgia. However, these semi-quantitative anxiety tests are still subjective as the tests are scored using doctor-rated or self-rated scales. METHOD: In this study, we investigated the relationship between heart rate variability (HRV) frequency subbands and anxiety tests. The study was conducted with 56 FMS patients and 34 healthy controls. BAI and HAM-A test scores were determined for each participant. ECG signals were then recruited and 71 HRV subbands were obtained from these ECG signals using Wavelet Packet Transform (WPT). The subbands and anxiety tests scores were analyzed and compared using multilayer perceptron neural networks (MLPNN). RESULTS: The results show that a HRV high frequency (HF) subband in the range of 0.15235Hz to 0.40235Hz, is correlated with BAI scores and another HRV HF subband, frequency range of 0.15235Hz to 0.28907Hz is correlated with HAM-A scores. The overall accuracy is 91.11% for HAM-A and 90% for BAI with MLPNN analysis. CONCLUSION: Doctor-rated or self-rated anxiety tests should be supported with quantitative and more objective methods. Our results show that the HRV parameters will be able to support the anxiety tests in the clinical evaluation of fibromyalgia. In other words, HRV parameters can potentially be used as an auxiliary diagnostic method in conjunction with anxiety tests.

Biceps femoris late latency responses and the "notching sign" in spasticity.

BACKGROUND: Spasticity is a motor impairment due to lesions in the brain and spinal cord. Despite being a well-known problem, difficulties remain in the assessment of the condition. The electrophysiological and kinesiological characteristics of the patellar pendulum changes during the movement triggered by the patellar T reflex could be used to assess spasticity. METHODS: Features of the patellar pendulum during the patellar T reflex were considered using a goniometric approach in spastic patients evaluated with the Ashworth scale. Medium and late latency responses in the rectus and biceps femoris muscles were examined electrophysiologically. For each pendulum, the maximum angle extension during an oscillation of the knee joint, maximal extension time, angular velocities of extensions of the knee joint and frequency of motion due to the patellar reflex were calculated. The damping of the amplitude in the pendulum was calculated. RESULTS: The spasticity group consisted of 65 patients (38 males and 27 females) with a mean age of 47.6 ± 14.0 years. The normal control group consisted of 25 individuals (19 males and six females) with a mean age of 32.1 ± 10 years. The biceps and rectus femoris long latency late responses were not observed in the normal cases. The biceps femoris medium latency response was observed only in 24 % of healthy individuals; conversely, late responses were observed in 84 % of patients. Activation of the antagonist muscles at a certain level of spasticity created a notching phenomenon. Amplitude of the reflex response and mean angular velocity of the first oscillation present in a dichotomic nature in the spasticity groups. Frequency of the first pendular oscillation increased with the increase of the Ashworth scale, while the damping ratio decreased with increasing scale. The Ashworth scale showed a correlation with the damping ratio. The damping ratio strongly distinguished the spastic subgroups and showed a strong negative correlation with the Ashworth scale. CONCLUSIONS: The Ashworth scale presents a good correlation with kinesiological parameters, but it is only possible to differentiate normal and spastic cases with electrophysiologic parameters. Furthermore, the notching phenomenon could be evaluated as a determinant of spasticity.

Temperature and burn injury prediction of human skin exposed to microwaves: a model analysis.

A one-dimensional multi-layer model is presented to characterize skin temperature rises and burn processes resulting from skin exposure to microwaves. Temperature variations and damage function analyses in the skin tissue exposed to microwaves were predicted depending on blood perfusion rate, thermal conductivity, power density, and exposure time. Thermal wave model was applied and the bio-heat transfer equation was solved using the finite difference time domain method. The thermal wave model of bio-heat transfer predicts a lower temperature rise than a model that uses Pennes' equation. When approaching steady state, the solutions overlaps with that obtained using the Pennes' equation. The results obtained may help to analyze the consequences of short-time high-power MW exposures in biological tissues.

Determination of a new VLF band in HRV for ventricular tachyarrhythmia patients.

This study presents a new very low frequency (VLF) band range in ventricular tachyarrhythmia patients and involves an approach for estimation of effect of VLF band on ventricular tachyarrhythmia patients. A model based on wavelet packets (WP) and multilayer perceptron neural network (MLPNN) is used for determination of effective VLF band in heart rate variability (HRV) signals. HRV is decomposed into sub-bands including very low frequency parts and variations of energy are analyzed. Domination test is done using MLPNN and dominant band is determined. As a result, a new VLF band was described in 0.0039063-0.03125 Hz frequency range. This method can be used for other bands or other arrhythmia patients. Especially, estimation of dominant band energy using this method can be helped to diagnose for applications where have important effect of characteristic band.

Measuring the sympathetic skin response on body and using as diagnosis-purposed for lung cancer patients by artificial neural networks.

In this study, the points of Sympathetic skin response that can be measured from different zones on body of healthy and patient subjects are determined. The Sympathetic skin responses on these points are obtained using a measurement device that is called Grass Model 7 Polygraph 1. The database is formed in Cerrahpasa University, Faculty of Medicine and data is taken from healthy and patient subjects who are volunteer. Some parameters of the subjects which are more effective on SSR such as height, weight, age must be chosen between the specific limits to obtain results more clearly. The symmetric points on human body are chosen for the measurement. After that, the Sympathetic skin response values which are measured from a human body are simulated and tested by using artificial neural network toolbox on Matlab software. The structure of the chosen neural network is a multilayer feedforward neural network. According to simulation results, the application method as diagnosis-purposed of the lung cancer patients is explained by using the differences related to these values on the skin.