Cagdas Topcu
Mayo Clinic, Neurology, Graduate Student
- Medical University of Graz, Physiology, Graduate Studentadd
- Electromyography, Neurorehabilitation, Motor Control, Modeling and Simulation, Motor Unit Recordings, Digital Signal Processing, and 14 moreCognitive Rehabilitation, Nonlinear Analysis, Nonlinear dynamics, Nonlinear Dynamics and Stochasticity, Machine Learning, Biomedical Engineering, Anthropology of emotions, ECG Signal processing, Synchronization, Nonlinear Dynamical Systems, Oscillators, Brain Plasticity, Neuroscience, and Neural Engineeringedit
Research Interests:
In this study, a non-contact respiration signal recording system used to assess respiratory system during emotional stimulations. Respiration signal was subtracted due to the depth of the person's chest wall with a RGB-D camera.... more
In this study, a non-contact respiration signal recording system used to assess respiratory system during emotional stimulations. Respiration signal was subtracted due to the depth of the person's chest wall with a RGB-D camera. Respiratory data were obtained from 10 individuals, 5 healthy women and 5 healthy men. Four different records were collected from 10 healthy individuals that were sitting in front of the RGB-D camera. In the first experiment, a person was recorded while sitting in daily life. In the second experiment, volunteers were shown horror videos with virtual reality stimuli. In the third experiment, volunteers were shown video that would stir excitement with virtual stimuli. In the last experiment, a video was shown that would make the volunteers feel laughing. The power spectral density attribute of respiratory signals was estimated for each stimulus. Finally, the mean values of the four different frequency bands were calculated as feature vectors to investigate patterns in respiration signals. Different patterns in respiratory signals for each stimulus were observed, thus this contactless respiration monitoring system can be used for investigating respiratory responses to emotional virtual reality stimuli.
Research Interests: Emotion, Respiratory Medicine, Biomedical Engineering, Sensors and Sensing, Virtual Reality (Computer Graphics), and 9 moreSpectral Methods, Embedded Systems, Digital Signal Processing, Biomedical signal and image processing, Respiratory Physiology, Respiration, Health monitoring, Depth Cameras, and Chest
In this study, it is aimed to determine the degree of the development in emotional expression of full face transplant patients from photographs. Hence, a rehabilitation process can be planned according to the determination of degrees as a... more
In this study, it is aimed to determine the degree of the development in emotional expression of full face transplant patients from photographs. Hence, a rehabilitation process can be planned according to the determination of degrees as a later work. As envisaged, in full face transplant cases, the determination of expressions can be confused or cannot be achieved as the healthy control group. In order to perform image-based analysis, a control group consist of 9 healthy males and 2 full-face transplant patients participated in the study. Appearance-based Gabor Wavelet Transform (GWT) and Local Binary Pattern (LBP) methods are adopted for recognizing neutral and 6 emotional expressions which consist of angry, scared, happy, hate, confused and sad. Feature extraction was carried out by using both methods and combination of these methods serially. In the performed expressions, the extracted features of the most distinct zones in the facial area where the eye and mouth region, have been used to classify the emotions. Also, the combination of these region features has been used to improve classifier performance. Control subjects and transplant patients’ ability to perform emotional expressions have been determined with K-nearest neighbor (KNN) classifier with region-specific and method-specific decision stages. The results have been compared with healthy group. It has been observed that transplant patients don’t reflect some emotional expressions. Also, there were confusions among expressions.
Research Interests:
Objective: Several different measures of heart rate variability, and particularly of respiratory sinus arrhythmia, are widely used in research and clinical applications. For many purposes it is important to know which features of heart... more
Objective: Several different measures of heart rate variability, and particularly of respiratory sinus arrhythmia, are widely used in research and clinical applications. For many purposes it is important to know which features of heart rate variability are directly related to respiration and which are caused by other aspects of cardiac dynamics.
Approach: Inspired by ideas from the theory of coupled oscillators, we use simultaneous measurements of respiratory and cardiac activity to perform a nonlinear disentanglement of the heart rate variability into the respiratory-related component and the rest.
Main results: The theoretical consideration is illustrated by the analysis of 25 data sets from healthy subjects. In all cases we show how the disentanglement is manifested in the different measures of heart rate variability.
Significance: The suggested technique can be exploited as a universal preprocessing tool, both for the analysis of respiratory influence on the heart rate and in cases when effects of other factors on the heart rate variability are in focus.
Approach: Inspired by ideas from the theory of coupled oscillators, we use simultaneous measurements of respiratory and cardiac activity to perform a nonlinear disentanglement of the heart rate variability into the respiratory-related component and the rest.
Main results: The theoretical consideration is illustrated by the analysis of 25 data sets from healthy subjects. In all cases we show how the disentanglement is manifested in the different measures of heart rate variability.
Significance: The suggested technique can be exploited as a universal preprocessing tool, both for the analysis of respiratory influence on the heart rate and in cases when effects of other factors on the heart rate variability are in focus.
Research Interests: Chronobiology, Physiology, Physics, Respiratory Medicine, Biomedical Engineering, and 15 moreSpectral Methods, Networks, Digital Signal Processing, Synchronization, Entropy, Coupled Oscillator, Heart rate variability, Non-Linear Dynamics, Hilbert transform, Heart rate, ECG Signal processing, Oscillations, Respiration, Clinical Sciences, and Respiratory Sinus Arrhythmia
Background We assessed the recovery of 2 face transplantation patients with measures of complexity during neuromuscular rehabilitation. Cognitive rehabilitation methods and functional electrical stimulation were used to improve facial... more
Background
We assessed the recovery of 2 face transplantation patients with measures of complexity during neuromuscular rehabilitation. Cognitive rehabilitation methods and functional electrical stimulation were used to improve facial emotional expressions of full-face transplantation patients for 5 months. Rehabilitation and analyses were conducted at approximately 3 years after full facial transplantation in the patient group. We report complexity analysis of surface electromyography signals of these two patients in comparison to the results of 10 healthy individuals.
Methods
Facial surface electromyography data were collected during 6 basic emotional expressions and 4 primary facial movements from 2 full-face transplantation patients and 10 healthy individuals to determine a strategy of functional electrical stimulation and understand the mechanisms of rehabilitation. A new personalized rehabilitation technique was developed using the wavelet packet method. Rehabilitation sessions were applied twice a month for 5 months. Subsequently, motor and functional progress was assessed by comparing the fuzzy entropy of surface electromyography data against the results obtained from patients before rehabilitation and the mean results obtained from 10 healthy subjects.
Results
At the end of personalized rehabilitation, the patient group showed improvements in their facial symmetry and their ability to perform basic facial expressions and primary facial movements. Similarity in the pattern of fuzzy entropy for facial expressions between the patient group and healthy individuals increased. Synkinesis was detected during primary facial movements in the patient group, and one patient showed synkinesis during the happiness expression. Synkinesis in the lower face region of one of the patients was eliminated for the lid tightening movement.
Conclusions
The recovery of emotional expressions after personalized rehabilitation was satisfactory to the patients. The assessment with complexity analysis of sEMG data can be used for developing new neurorehabilitation techniques and detecting synkinesis after full-face transplantation.
We assessed the recovery of 2 face transplantation patients with measures of complexity during neuromuscular rehabilitation. Cognitive rehabilitation methods and functional electrical stimulation were used to improve facial emotional expressions of full-face transplantation patients for 5 months. Rehabilitation and analyses were conducted at approximately 3 years after full facial transplantation in the patient group. We report complexity analysis of surface electromyography signals of these two patients in comparison to the results of 10 healthy individuals.
Methods
Facial surface electromyography data were collected during 6 basic emotional expressions and 4 primary facial movements from 2 full-face transplantation patients and 10 healthy individuals to determine a strategy of functional electrical stimulation and understand the mechanisms of rehabilitation. A new personalized rehabilitation technique was developed using the wavelet packet method. Rehabilitation sessions were applied twice a month for 5 months. Subsequently, motor and functional progress was assessed by comparing the fuzzy entropy of surface electromyography data against the results obtained from patients before rehabilitation and the mean results obtained from 10 healthy subjects.
Results
At the end of personalized rehabilitation, the patient group showed improvements in their facial symmetry and their ability to perform basic facial expressions and primary facial movements. Similarity in the pattern of fuzzy entropy for facial expressions between the patient group and healthy individuals increased. Synkinesis was detected during primary facial movements in the patient group, and one patient showed synkinesis during the happiness expression. Synkinesis in the lower face region of one of the patients was eliminated for the lid tightening movement.
Conclusions
The recovery of emotional expressions after personalized rehabilitation was satisfactory to the patients. The assessment with complexity analysis of sEMG data can be used for developing new neurorehabilitation techniques and detecting synkinesis after full-face transplantation.
Research Interests: Neuroscience, Neurology, Biomedical Engineering, Electrophysiology, Rehabilitation, and 15 moreNeurorehabilitation, Complexity, Digital Signal Processing, Microsurgery, Facial expression, Cognitive Neuroscience, Entropy, Wavelets, Facial Reconstruction, Nonlinear Analysis, Facial Expressions and Emotions, Electromyography, Clinical Neurophysiology and Electromyography, Fuzzy Entropy, and Face Transplant
We assessed clinical features as well as sensory and motor recoveries in 3 full-face transplantation patients. A frequency analysis was performed on facial surface electromyography data collected during 6 basic emotional expressions and 4... more
We assessed clinical features as well as sensory and motor recoveries in 3 full-face transplantation patients. A frequency analysis was performed on facial surface electromyography data collected during 6 basic emotional expressions and 4 primary facial movements. Motor progress was assessed using the wavelet packet method by comparison against the mean results obtained from 10 healthy subjects. Analyses were conducted on 1 patient at approximately 1 year after face transplantation and at 2 years after transplantation in the remaining 2 patients. Motor recovery was observed following sensory recovery in all 3 patients; however, the 3 cases had different backgrounds and exhibited different degrees and rates of sensory and motor improvements after transplant. Wavelet packet energy was detected in all patients during emotional expressions and primary movements; however, there were fewer active channels during expressions in transplant patients compared to healthy individuals, and patterns of wavelet packet energy were different for each patient. Finally, high-frequency components were typically detected in patients during emotional expressions, but fewer channels demonstrated these high-frequency components in patients compared to healthy individuals. Our data suggest that the posttransplantation recovery of emotional facial expression requires neural plasticity.
Research Interests:
Research Interests:
Measuring complexity of dynamical systems is a mighty tool for electrophysiological signal processing. There are plenty of entropies for estimating complexity measure. Approximate entropy (ApEn), sample entropy (SampEn), fuzzy entropy... more
Measuring complexity of dynamical systems is
a mighty tool for electrophysiological signal processing.
There are plenty of entropies for estimating complexity
measure. Approximate entropy (ApEn), sample entropy
(SampEn), fuzzy entropy (FuzzyEn), wavelet entropy (WE)
and wavelet packet entropy (WPE) was used for surface
EMG feature extraction for face movements classification.
Linear discriminant analysis (LDA) selected for
classification. Classification performance was determined by
mean square error (MSE) for different window sizes. Fuzzy
entropy is the most robust and succeeding method of them.
Principal component analysis used to improve classification
performance however just results of approximate entropy
feature were refined. MSE of wavelet entropy and wavelet
packet entropy are also decent methods for this classification
problem.
a mighty tool for electrophysiological signal processing.
There are plenty of entropies for estimating complexity
measure. Approximate entropy (ApEn), sample entropy
(SampEn), fuzzy entropy (FuzzyEn), wavelet entropy (WE)
and wavelet packet entropy (WPE) was used for surface
EMG feature extraction for face movements classification.
Linear discriminant analysis (LDA) selected for
classification. Classification performance was determined by
mean square error (MSE) for different window sizes. Fuzzy
entropy is the most robust and succeeding method of them.
Principal component analysis used to improve classification
performance however just results of approximate entropy
feature were refined. MSE of wavelet entropy and wavelet
packet entropy are also decent methods for this classification
problem.
Research Interests:
Complexity measure of dynamical systems is a popular feature for biological signal processing. In this study surface electromyography (sEMG) data is recorded 3 full face transplantation patients and 10 healthy subjects. Their muscle... more
Complexity measure of dynamical systems is a
popular feature for biological signal processing. In this study
surface electromyography (sEMG) data is recorded 3 full face
transplantation patients and 10 healthy subjects. Their muscle
activity regions are compared with a Fuzzy entropy based
method for 4 basic face movements. The fuzzy entropy based
method effectively detects active positions and determines
different patterns in fuzzy entropy domain for these basic
movements.
popular feature for biological signal processing. In this study
surface electromyography (sEMG) data is recorded 3 full face
transplantation patients and 10 healthy subjects. Their muscle
activity regions are compared with a Fuzzy entropy based
method for 4 basic face movements. The fuzzy entropy based
method effectively detects active positions and determines
different patterns in fuzzy entropy domain for these basic
movements.
Research Interests:
In this study, fractal dimension which used to analysis complexity of the biomedical signals used to determine active channels when performing 24 fingers and wrist movements. The results compared withroot mean square (RMS) and mean... more
In this study, fractal dimension which used to analysis complexity of the biomedical signals used to determine active channels when performing 24 fingers and wrist movements. The results compared withroot mean square (RMS) and mean absolute value (MAV) features. Higuchi fractal dimension (HFD) method was choosen that has high accuracy and linear with theoretical fractal dimension values nevertheless the method is noise sensitive. The noise sensitivity problem was overcomed with filtering the signal. Mean Higuchi fractal dimension feature determined using sliding window and active movements obtained that are above of thechoosen thresholds for each channels. Correlation of active chanels for each movements which was obtained with RMS and HFD features was discussed. Thus a new method was submitted which based on HFD instead of RMS adn MAV features.