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(1 - 9 of 9)
- Title
- Classifying and Predicting Walking Speed From Electroencephalography Data.
- Creator
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Rahrooh, Allen, Huang, Helen, Huang, Hsin-Hsiung, Samsam, Mohtashem, University of Central Florida
- Abstract / Description
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Electroencephalography (EEG) non-invasively records electrocortical activity and can be used to understand how the brain functions to control movements and walking. Studies have shown that electrocortical dynamics are coupled with the gait cycle and change when walking at different speeds. Thus, EEG signals likely contain information regarding walking speed that could potentially be used to predict walking speed using just EEG signals recorded during walking. The purpose of this study was to...
Show moreElectroencephalography (EEG) non-invasively records electrocortical activity and can be used to understand how the brain functions to control movements and walking. Studies have shown that electrocortical dynamics are coupled with the gait cycle and change when walking at different speeds. Thus, EEG signals likely contain information regarding walking speed that could potentially be used to predict walking speed using just EEG signals recorded during walking. The purpose of this study was to determine whether walking speed could be predicted from EEG recorded as subjects walked on a treadmill with a range of speeds (0.5 m/s, 0.75 m/s, 1.0 m/s, 1.25 m/s, and self-paced). We first applied spatial Independent Component Analysis (sICA) to reduce temporal dimensionality and then used current popular classification methods: Bagging, Boosting, Random Forest, Na(&)#239;ve Bayes, Logistic Regression, and Support Vector Machines with a linear and radial basis function kernel. We evaluated the precision, sensitivity, and specificity of each classifier. Logistic regression had the highest overall performance (76.6 +/- 13.9%), and had the highest precision (86.3 +/- 11.7%) and sensitivity (88.7 +/- 8.7%). The Support Vector Machine with a radial basis function kernel had the highest specificity (60.7 +/- 39.1%). These overall performance values are relatively good since the EEG data had only been high-pass filtered with a 1 Hz cutoff frequency and no extensive cleaning methods were performed. All of the classifiers had an overall performance of at least 68% except for the Support Vector Machine with a linear kernel, which had an overall performance of 55.4%. These results suggest that applying spatial Independent Component Analysis to reduce temporal dimensionality of EEG signals does not significantly impair the classification of walking speed using EEG and that walking speeds can be predicted from EEG data.
Show less - Date Issued
- 2019
- Identifier
- CFE0007517, ucf:52642
- Format
- Document (PDF)
- PURL
- http://purl.flvc.org/ucf/fd/CFE0007517
- Title
- "BACK" TO THE FUTURE: AN EVALUATION OF MORPHOLOGICAL INTEGRATION IN KYPHOSIS.
- Creator
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Ceuninck, Kristyna L, Starbuck, John, University of Central Florida
- Abstract / Description
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Morphological integration refers to the interdependence of two or more phenotypic structures. The morphological integration concept is based on the fact that parts of complex organisms do not vary randomly and instead display degrees of non-independence that are thought to occur from shared genetic or developmental origins, and/or functional demands. Integrated traits may develop, evolve, and be inherited together. One instance of morphological integration can be found between the vertebral...
Show moreMorphological integration refers to the interdependence of two or more phenotypic structures. The morphological integration concept is based on the fact that parts of complex organisms do not vary randomly and instead display degrees of non-independence that are thought to occur from shared genetic or developmental origins, and/or functional demands. Integrated traits may develop, evolve, and be inherited together. One instance of morphological integration can be found between the vertebral column and the skull. Due to the position of the skull resting atop of the vertebral column, posture may influence skull development and overall craniofacial morphology. Morphological integration within or between structures is typically statistically assessed by exploring correlation and covariation patterns among biological structures of interest. In this study, an analysis of morphological integration was carried out by studying covariation of morphometric measures from the vertebral column and craniofacial complex. Age- and sex-matched, de-identified computed tomography images of individuals with kyphosis spinal malformation (n = 15) and controls (n = 19) were acquired from Florida Hospital. It is hypothesized that the sample of individuals with kyphosis will exhibit statistically significant covariance differences relative to the control group for T6 vertebral and midfacial linear distance measurements. Anatomical landmarks were identified on the T6 thoracic vertebrae (n = 6) and the midfacial skeleton (n = 6), and XYZ coordinates were recorded for analysis. A subset of 10 individuals (5 kyphosis, 5 controls) individuals were measured on two occasions to assess reliability and measurement error. An Euclidean Distance Matrix Analysis (EDMA) of morphological integration was carried out on the entire sample by calculating correlation values for paired linear distance measurements (one vertebral and one midfacial) separately for the kyphosis and control samples (n = 225 for each sample). Next, EDMA calculated correlation differences and statistically assessed significance using a non-parametric bootstrap (1,000 resamples) and confidence interval testing (? ? 0.10). Only 35 of the 225 (15.56%) correlation differences were statistically significant. Patterns of variation among these significant correlation differences were explored by examining sample directionality of differences, sign patterns, and strengths. The relevance of these results to clinical and anthropological pursuits are discussed. Several recommendations for future investigations are made.
Show less - Date Issued
- 2018
- Identifier
- CFH2000286, ucf:45812
- Format
- Document (PDF)
- PURL
- http://purl.flvc.org/ucf/fd/CFH2000286
- Title
- ADDING CEREBRAL AUTOREGULATION TO A LUMPED PARAMETER MODEL OF BLOOD FLOW.
- Creator
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Gentile, Rusty, Kassab, Alain, University of Central Florida
- Abstract / Description
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A mathematical model of blood flow in infants with hypoplastic left heart syndrome (HLHS) was improved by adding cerebral autoregulation. This is the process by which blood vessels constrict or dilate to keep blood flow steady in certain organs during pressure changes. The original lumped parameter model transformed the fluid flow into an electrical circuit. Its behavior is described using a system of thirty-three coupled differential equations that are solved numerically using a fourth-order...
Show moreA mathematical model of blood flow in infants with hypoplastic left heart syndrome (HLHS) was improved by adding cerebral autoregulation. This is the process by which blood vessels constrict or dilate to keep blood flow steady in certain organs during pressure changes. The original lumped parameter model transformed the fluid flow into an electrical circuit. Its behavior is described using a system of thirty-three coupled differential equations that are solved numerically using a fourth-order Runge-Kutta method implemented in MATLAB. A literature review that includes a discussion of autoregulation mechanisms and approaches to modeling them is followed by a description of the model created for this paper. The model is based on the baroreceptor or neurogenic theory of autoregulation. According to this theory, nerves in certain places within the cardiovascular system detect changes in blood pressure. The brain then compensates by sending a signal to blood vessels to constrict or dilate. The model of the control system responded fairly well to a pressure drop with a steady state error of about two percent. Running the model with or without the control system activated had little effect on other parameters, notably cardiac output. A more complete model of blood flow control would include autonomic regulation. This would vary more parameters than local autoregulation, including heart rate and contractility. This is suggested as a topic of further research.
Show less - Date Issued
- 2012
- Identifier
- CFH0004214, ucf:44933
- Format
- Document (PDF)
- PURL
- http://purl.flvc.org/ucf/fd/CFH0004214
- Title
- MEDIUM ACCESS CONTROL PROTOCOLS AND ROUTING ALGORITHMS FOR WIRELESS SENSOR NETWORKS.
- Creator
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Bag, Anirban, Bassiouni, Mostafa, University of Central Florida
- Abstract / Description
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In recent years, the development of a large variety of mobile computing devices has led to wide scale deployment and use of wireless ad hoc and sensor networks. Wireless Sensor Networks consist of battery powered, tiny and cheap "motes", having sensing and wireless communication capabilities. Although wireless motes have limited battery power, communication and computation capabilities, the range of their application is vast. In the first part of the dissertation, we have addressed the...
Show moreIn recent years, the development of a large variety of mobile computing devices has led to wide scale deployment and use of wireless ad hoc and sensor networks. Wireless Sensor Networks consist of battery powered, tiny and cheap "motes", having sensing and wireless communication capabilities. Although wireless motes have limited battery power, communication and computation capabilities, the range of their application is vast. In the first part of the dissertation, we have addressed the specific application of Biomedical Sensor Networks. To solve the problem of data routing in these networks, we have proposed the Adaptive Least Temperature Routing (ALTR) algorithm that reduces the average temperature rise of the nodes in the in-vivo network while routing data efficiently. For delay sensitive biomedical applications, we proposed the Hotspot Preventing Routing (HPR) algorithm which avoids the formation of hotspots (regions having very high temperature) in the network. HPR forwards the packets using the shortest path, bypassing the regions of high temperature and thus significantly reduces the average packet delivery delay, making it suitable for real-time applications of in-vivo networks. We also proposed another routing algorithm suitable for being used in a network of id-less biomedical sensor nodes, namely Routing Algorithm for networks of homogeneous and Id-less biomedical sensor Nodes (RAIN). Finally we developed Biocomm, a cross-layer MAC and Routing protocol co-design for Biomedical Sensor Networks, which optimizes the overall performance of an in-vivo network through cross-layer interactions. We performed extensive simulations to show that the proposed Biocomm protocol performs much better than the other existing MAC and Routing protocols in terms of preventing the formation of hotspots, reducing energy consumption of nodes and preventing network congestion when used in an in-vivo network. In the second part of the dissertation, we have addressed the problems of habitat-monitoring sensor networks, broadcast algorithms for sensor networks and the congestion problem in sensor networks as well as one non-sensor network application, namely, on-chip communication networks. Specifically, we have proposed a variation of HPR algorithm, called Hotspot Preventing Adaptive Routing (HPAR) algorithm, for efficient data routing in Networks On-Chip catering to their specific hotspot prevention issues. A protocol similar to ALTR has been shown to perform well in a sensor network deployed for habitat monitoring. We developed a reliable, low overhead broadcast algorithm for sensor networks namely Topology Adaptive Gossip (TAG) algorithm. To reduce the congestion problem in Wireless Sensor Networks, we proposed a tunable cross-layer Congestion Reducing Medium Access Control (CRMAC) protocol that utilizes buffer status information from the Network layer to give prioritized medium access to congested nodes in the MAC layer and thus preventing congestion and packet drops. CRMAC can also be easily tuned to satisfy different application-specific performance requirements. With the help of extensive simulation results we have shown how CRMAC can be adapted to perform well in different applications of Sensor Network like Emergency Situation that requires a high network throughput and low packet delivery latency or Long-term Monitoring application requiring energy conservation.
Show less - Date Issued
- 2007
- Identifier
- CFE0001915, ucf:47480
- Format
- Document (PDF)
- PURL
- http://purl.flvc.org/ucf/fd/CFE0001915
- Title
- SYSTEM DESIGN AND OPTIMIZATION OF OPTICAL COHERENCE TOMOGRAPHY.
- Creator
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Akcay, Avni, Rolland, Jannick, University of Central Florida
- Abstract / Description
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Optical coherence imaging, including tomography (OCT) and microscopy (OCM), has been a growing research field in biomedical optical imaging in the last decade. In this imaging modality, a broadband light source, thus of short temporal coherence length, is used to perform imaging via interferometry. A challenge in optical coherence imaging, as in any imaging system towards biomedical diagnosis, is the quantification of image quality and optimization of the system components, both a primary...
Show moreOptical coherence imaging, including tomography (OCT) and microscopy (OCM), has been a growing research field in biomedical optical imaging in the last decade. In this imaging modality, a broadband light source, thus of short temporal coherence length, is used to perform imaging via interferometry. A challenge in optical coherence imaging, as in any imaging system towards biomedical diagnosis, is the quantification of image quality and optimization of the system components, both a primary focus of this research. We concentrated our efforts on the optimization of the imaging system from two main standpoints: axial point spread function (PSF) and practical steps towards compact low-cost solutions. Up to recently, the criteria for the quality of a system was based on speed of imaging, sensitivity, and particularly axial resolution estimated solely from the full-width at half-maximum (FWHM) of the axial PSF with the common practice of assuming a Gaussian source power spectrum. As part of our work to quantify axial resolution we first brought forth two more metrics unlike FWHM, which accounted for side lobes in the axial PSF caused by irregularities in the shape of the source power spectrum, such as spectral dips. Subsequently, we presented a method where the axial PSF was significantly optimized by suppressing the side lobes occurring because of the irregular shape of the source power spectrum. The optimization was performed through optically shaping the source power spectrum via a programmable spectral shaper, which consequentially led to suppression of spurious structures in the images of a layered specimen. The superiority of the demonstrated approach was in performing reshaping before imaging, thus eliminating the need for post-data acquisition digital signal processing. Importantly, towards the optimization and objective image quality assessment in optical coherence imaging, the impact of source spectral shaping was further analyzed in a task-based assessment method based on statistical decision theory. Two classification tasks, a signal-detection task and a resolution task, were investigated. Results showed that reshaping the source power spectrum was a benefit essentially to the resolution task, as opposed to both the detection and resolution tasks, and the importance of the specimen local variations in index of refraction on the resolution task was demonstrated. Finally, towards the optimization of OCT and OCM for use in clinical settings, we analyzed the detection electronics stage, which is a crucial component of the system that is designed to capture extremely weak interferometric signals in biomedical and biological imaging applications. We designed and tested detection electronics to achieve a compact and low-cost solution for portable imaging units and demonstrated that the design provided an equivalent performance to the commercial lock-in amplifier considering the system sensitivity obtained with both detection schemes.
Show less - Date Issued
- 2005
- Identifier
- CFE0000651, ucf:46527
- Format
- Document (PDF)
- PURL
- http://purl.flvc.org/ucf/fd/CFE0000651
- Title
- Visionary Ophthalmics: Confluence of Computer Vision and Deep Learning for Ophthalmology.
- Creator
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Morley, Dustin, Foroosh, Hassan, Bagci, Ulas, Gong, Boqing, Mohapatra, Ram, University of Central Florida
- Abstract / Description
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Ophthalmology is a medical field ripe with opportunities for meaningful application of computer vision algorithms. The field utilizes data from multiple disparate imaging techniques, ranging from conventional cameras to tomography, comprising a diverse set of computer vision challenges. Computer vision has a rich history of techniques that can adequately meet many of these challenges. However, the field has undergone something of a revolution in recent times as deep learning techniques have...
Show moreOphthalmology is a medical field ripe with opportunities for meaningful application of computer vision algorithms. The field utilizes data from multiple disparate imaging techniques, ranging from conventional cameras to tomography, comprising a diverse set of computer vision challenges. Computer vision has a rich history of techniques that can adequately meet many of these challenges. However, the field has undergone something of a revolution in recent times as deep learning techniques have sprung into the forefront following advances in GPU hardware. This development raises important questions regarding how to best leverage insights from both modern deep learning approaches and more classical computer vision approaches for a given problem. In this dissertation, we tackle challenging computer vision problems in ophthalmology using methods all across this spectrum. Perhaps our most significant work is a highly successful iris registration algorithm for use in laser eye surgery. This algorithm relies on matching features extracted from the structure tensor and a Gabor wavelet (-) a classically driven approach that does not utilize modern machine learning. However, drawing on insight from the deep learning revolution, we demonstrate successful application of backpropagation to optimize the registration significantly faster than the alternative of relying on finite differences. Towards the other end of the spectrum, we also present a novel framework for improving RANSAC segmentation algorithms by utilizing a convolutional neural network (CNN) trained on a RANSAC-based loss function. Finally, we apply state-of-the-art deep learning methods to solve the problem of pathological fluid detection in optical coherence tomography images of the human retina, using a novel retina-specific data augmentation technique to greatly expand the data set. Altogether, our work demonstrates benefits of applying a holistic view of computer vision, which leverages deep learning and associated insights without neglecting techniques and insights from the previous era.
Show less - Date Issued
- 2018
- Identifier
- CFE0007058, ucf:52001
- Format
- Document (PDF)
- PURL
- http://purl.flvc.org/ucf/fd/CFE0007058
- Title
- BIOSIGNAL PROCESSING CHALLENGES IN EMOTION RECOGNITIONFOR ADAPTIVE LEARNING.
- Creator
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Vartak, Aniket, Mikhael, Wasfy, University of Central Florida
- Abstract / Description
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User-centered computer based learning is an emerging field of interdisciplinary research. Research in diverse areas such as psychology, computer science, neuroscience and signal processing is making contributions the promise to take this field to the next level. Learning systems built using contributions from these fields could be used in actual training and education instead of just laboratory proof-of-concept. One of the important advances in this research is the detection and assessment of...
Show moreUser-centered computer based learning is an emerging field of interdisciplinary research. Research in diverse areas such as psychology, computer science, neuroscience and signal processing is making contributions the promise to take this field to the next level. Learning systems built using contributions from these fields could be used in actual training and education instead of just laboratory proof-of-concept. One of the important advances in this research is the detection and assessment of the cognitive and emotional state of the learner using such systems. This capability moves development beyond the use of traditional user performance metrics to include system intelligence measures that are based on current neuroscience theories. These advances are of paramount importance in the success and wide spread use of learning systems that are automated and intelligent. Emotion is considered an important aspect of how learning occurs, and yet estimating it and making adaptive adjustments are not part of most learning systems. In this research we focus on one specific aspect of constructing an adaptive and intelligent learning system, that is, estimation of the emotion of the learner as he/she is using the automated training system. The challenge starts with the definition of the emotion and the utility of it in human life. The next challenge is to measure the co-varying factors of the emotions in a non-invasive way, and find consistent features from these measures that are valid across wide population. In this research we use four physiological sensors that are non-invasive, and establish a methodology of utilizing the data from these sensors using different signal processing tools. A validated set of visual stimuli used worldwide in the research of emotion and attention, called International Affective Picture System (IAPS), is used. A dataset is collected from the sensors in an experiment designed to elicit emotions from these validated visual stimuli. We describe a novel wavelet method to calculate hemispheric asymmetry metric using electroencephalography data. This method is tested against typically used power spectral density method. We show overall improvement in accuracy in classifying specific emotions using the novel method. We also show distinctions between different discrete emotions from the autonomic nervous system activity using electrocardiography, electrodermal activity and pupil diameter changes. Findings from different features from these sensors are used to give guidelines to use each of the individual sensors in the adaptive learning environment.
Show less - Date Issued
- 2010
- Identifier
- CFE0003301, ucf:48503
- Format
- Document (PDF)
- PURL
- http://purl.flvc.org/ucf/fd/CFE0003301
- Title
- Brain stethoscope: A non-invasive method for monitoring intracranial pressure.
- Creator
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Azad, Md Khurshidul, Mansy, Hansen, Kassab, Alain, Bhattacharya, Samik, University of Central Florida
- Abstract / Description
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Monitoring intracranial pressure (ICP) is important for patients with increased intracranial pressure. Invasive methods of ICP monitoring include lumbar puncture manometry, which requires high precision, is costly, and can lead to complications. Non-invasive monitoring of ICP using tympanic membrane pulse (TMp) measurement can provide an alternative monitoring method that avoids such complications. In the current study, a piezo based sensor was designed, constructed and used to acquire TMp...
Show moreMonitoring intracranial pressure (ICP) is important for patients with increased intracranial pressure. Invasive methods of ICP monitoring include lumbar puncture manometry, which requires high precision, is costly, and can lead to complications. Non-invasive monitoring of ICP using tympanic membrane pulse (TMp) measurement can provide an alternative monitoring method that avoids such complications. In the current study, a piezo based sensor was designed, constructed and used to acquire TMp signals. The results showed that tympanic membrane waveform changed in morphology and amplitude with increased ICP, which was induced by changing subject position using a tilt table. In addition, the results suggest that TMp are affected by breathing, which has small effects on ICP. The newly developed piezo based brain stethoscope may be a way to monitor patients with increased intracranial pressure thus avoiding invasive ICP monitoring and reducing associated risk and cost.
Show less - Date Issued
- 2018
- Identifier
- CFE0006972, ucf:51643
- Format
- Document (PDF)
- PURL
- http://purl.flvc.org/ucf/fd/CFE0006972
- Title
- SIGNAL PROCESSING OF AN ECG SIGNALIN THE PRESENCE OF A STRONG STATIC MAGNETIC FIELD.
- Creator
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Gupta, Aditya, Weeks, Arthur, University of Central Florida
- Abstract / Description
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This dissertation addresses the problem of elevation of the T wave of an electrocardiogram (ECG) signal in the magnetic resonance imaging (MRI). In the MRI, due to the strong static magnetic field the interaction of the blood flow with this strong magnetic field induces a voltage in the body. This voltage appears as a superimposition at the locus of the T wave of the ECG signal. This looses important information required by the doctors to interpret the ST segment of the ECG and detect...
Show moreThis dissertation addresses the problem of elevation of the T wave of an electrocardiogram (ECG) signal in the magnetic resonance imaging (MRI). In the MRI, due to the strong static magnetic field the interaction of the blood flow with this strong magnetic field induces a voltage in the body. This voltage appears as a superimposition at the locus of the T wave of the ECG signal. This looses important information required by the doctors to interpret the ST segment of the ECG and detect diseases such as myocardial infarction. This dissertation aims at finding a solution to the problem of elevation of the T wave of an ECG signal in the MRI. The first step is to simulate the entire situation and obtain the magnetic field dependent T wave elevation. This is achieved by building a model of the aorta and simulating the blood flow in it. This model is then subjected to a static magnetic field and the surface potential on the thorax is measured to observe the T wave elevation. The various parameters on which the T wave elevation is dependent are then analyzed. Different approaches are used to reduce this T wave elevation problem. The direct approach aims at computing the magnitude of T wave elevation using magneto-hydro-dynamic equations. The indirect approach uses digital signal processing tools like the least mean square adaptive filter to remove the T wave elevation and obtain artifact free ECG signal in the MRI. Excellent results are obtained from the simulation model. The model perfectly simulates the ECG signal in the MRI at all the 12 leads of the ECG. These results are compared with ECG signals measured in the MRI. A simulation package is developed in MATLAB based on the simulation model. This package is a graphical user interface allowing the user to change the strength of magnetic field, the radius of the aorta and the orientation of the aorta with respect to the heart and observe the ECG signals with the elevation at the 12 leads of the ECG. Also the artifacts introduced due to the magnetic field can be removed by the least mean square adaptive filter. The filter adapts the ECG signal in the MRI to the ECG signal of the patient outside the MRI. Before the adaptation, the heart rate of the ECG outside the MRI is matched to the ECG in the MRI by interpolation or decimation. The adaptive filter works excellently to remove the T wave artifacts. When the cardiac output of the patient changes, the simulation model is used along with the adaptive filter to obtain the artifact free ECG signal.
Show less - Date Issued
- 2007
- Identifier
- CFE0001857, ucf:47389
- Format
- Document (PDF)
- PURL
- http://purl.flvc.org/ucf/fd/CFE0001857
