Complementary Phenomena: Phantom Hand and Phantom Face.

After tissue or limb loss, the development of sensation and perception of the lost or deafferent tissue is defined as a phantom phenomenon. We investigated the presence of phantom phenomena in individuals who underwent a full face transplant as well as those who had a hand transplant. Specifically, we investigated sensory perception of the face on the fingers and sensory perception of the fingers on the face in three full face and four hand transplant patients. In all seven individuals, we used a brush to separately stimulate the right and left sides of the face or the palmar and dorsal faces of the hand. We then asked the individuals if they felt a sensation of touch on any other part of their body and, if so, to describe their perceptions. Changes in the regions of the primary sensory cortex representing the hand and face were defined using fMRI obtained via tactile sensory stimulation of the clinical examination areas. Two of the full face transplant patients reported sensory perceptions such as a prominent sensation of touch on their faces during sensory stimulation of their fingers. Three of the hand transplant patients reported sensory perceptions, which we referred to as finger patches, during sensory stimulation of the face area. In fMRI, overlaps were observed in the cortical hand and face representation areas. We consider the phantom hand and phantom face phenomena we observed to be complementary due to the neighborhood of the representations of the hand and face in the somatosensory cortex.

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.

Determination of Fatigue Following Maximal Loaded Treadmill Exercise by Using Wavelet Packet Transform Analysis and MLPNN from MMG-EMG Data Combinations.

The muscle fatigue can be expressed as decrease in maximal voluntary force generating capacity of the neuromuscular system as a result of peripheral changes at the level of the muscle, and also failure of the central nervous system to drive the motoneurons adequately. In this study, a muscle fatigue detection method based on frequency spectrum of electromyogram (EMG) and mechanomyogram (MMG) has been presented. The EMG and MMG data were obtained from 31 healthy, recreationally active men at the onset, and following exercise. All participants were performed a maximally exercise session in a motor-driven treadmill by using standard Bruce protocol which is the most widely used test to predict functional capacity. The method used in the present study consists of pre-processing, determination of the energy value based on wavelet packet transform, and classification phases. The results of the study demonstrated that changes in the MMG 176-234 Hz and EMG 254-313 Hz bands are critical to determine for muscle fatigue occurred following maximally exercise session. In conclusion, our study revealed that an algorithm with EMG and MMG combination based on frequency spectrum is more effective for the detection of muscle fatigue than EMG or MMG alone.

An effective solution for capturing the single twitch of muscle: application to monitor muscle relaxation.

In this study, a fast algorithm was developed to capture of train of four and to filter extra contraction and noises. A low pass filter created to filter extra contraction and high frequency noises. Then, a TCA algorithm designed to capturing of the single twitch of muscle. The algorithm updated to remove embedded extra contraction and to derive boundary values in this location from cubic spline interpolation. Efficiency of TCA and effect of extra contraction tested in time and frequency domain.

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.