ISO 9001 Certified Training Provider Offers Biomedical Engineering Projects

Elysium Technologies Private Limited
Approved by ISO 9001:2008 and AICTE for SKP Training
Singapore | Madurai | Trichy | Coimbatore | Cochin | Kollam | Chennai
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IEEE FINAL YEAR PROJECTS 2012 – 2013
BIO- MEDICINE
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IEEE Final Year Projects 2012 |Student Projects | Information Technology & Bio Medicine
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Email: kerala@elysiumtechnologies.com, Website: www.elysiumtechnologies.com

INFORMATION TECHNOLOGY AND BIO MEDICINE 2012 - 2013

EGC
3-D Liver Segmentation Method with Parallel Computing for Selective Internal Radiation
6201
Therapy

This study describes a new 3-D liver segmentation method in support of the selective internal radiation treatment as a
treatment for liver tumors. This 3-D segmentation is based on coupling a modified k-means segmentation method with a
special localized contouring algorithm. In the segmentation process, five separate regions are identified on the
computerized tomography image frames. The merit of the proposed method lays in its potential to provide fast and
accurate liver segmentation and 3-D rendering as well as in delineating tumor region(s), all with minimal user interaction.
Leveraging of multicore platforms is shown to speed up the processing of medical images considerably, making this
method more suitable in clinical settings. Experiments were performed to assess the effect of parallelization using up to
442 slices. Empirical results, using a single workstation, show a reduction in processing time from 4.5 h to almost 1 h for
a 78% gain. Most important is the accuracy achieved in estimating the volumes of the liver and tumor region(s), yielding
an average error of less than 2% in volume estimation over volumes generated on the basis of the current manually
guided segmentation processes. Results were assessed using the analysis of variance statistical analysis.

EGC A New Intelligence-Based Approach for Computer-Aided Diagnosis of Dengue Fever
6202

Identification of the influential clinical symptoms and laboratory features that help in the diagnosis of dengue fever (DF)
in early phase of the illness would aid in designing effective public health management and virological surveillance
strategies. Keeping this as our main objective, we develop in this paper a new computational intelligence-based
methodology that predicts the diagnosis in real time, minimizing the number of false positives and false negatives. Our
methodology consists of three major components: 1) a novel missing value imputation procedure that can be applied on
any dataset consisting of categorical (nominal) and/or numeric (real or integer); 2) a wrapper-based feature selection
method with genetic search for extracting a subset of most influential symptoms that can diagnose the illness; and 3) an
alternating decision tree method that employs boosting for generating highly accurate decision rules. The predictive
models developed using our methodology are found to be more accurate than the state-of-the-art methodologies used in
the diagnosis of the DF.

EGC A Novel Semi-automated Atherosclerotic Plaque Characterization Method Using Grayscale
6203
Intravascular Ultrasound Images: Comparison With Virtual Histology

Intravascular ultrasound (IVUS) virtual histology (VH-IVUS) is a new technique, which provides automated plaque
characterization in IVUS frames, using the ultrasound backscattered RF-signals. However, its computation can only be
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performed once per cardiac cycle (ECG-gated technique), which significantly decreases the number of characterized
IVUS frames. Also atherosclerotic plaques in images that have been acquired by machines, which are not equipped with
the VH software, cannot be characterized. To address these limitations, we have developed a plaque characterization
technique that can be applied in grayscale IVUS images. Our semiautomated method is based on a three-step approach.
In the first step, the plaque area [region of interest (ROI)] is detected semiautomatically. In the second step, a set of
features is extracted for each pixel of the ROI and in the third step, a random forest classifier is used to classify these
pixels into four classes: dense calcium, necrotic core, fibrotic tissue, and fibro-fatty tissue. In order to train and validate
our method, we used 300 IVUS frames acquired from virtual histology examinations from ten patients. The overall
accuracy of the proposed method was 85.65% suggesting that our approach is reliable and may be further investigated
in the clinical and research arena.

EGC A Reliable Transmission Protocol for ZigBee-Based Wireless Patient Monitoring
6204

Patient monitoring systems are gaining their importance as the fast-growing global elderly population increases
demands for caretaking. These systems use wireless technologies to transmit vital signs for medical evaluation. In a
multihop ZigBee network, the existing systems usually use broadcast or multicast schemes to increase the reliability of
signals transmission; however, both the schemes lead to significantly higher network traffic and end-to-end
transmission delay. In this paper, we present a reliable transmission protocol based on anycast routing for wireless
patient monitoring. Our scheme automatically selects the closest data receiver in an anycast group as a destination to
reduce the transmission latency as well as the control overhead. The new protocol also shortens the latency of path
recovery by initiating route recovery from the intermediate routers of the original path. On the basis of a reliable
transmission scheme, we implement a ZigBee device for fall monitoring, which integrates fall detection, indoor
positioning, and ECG monitoring. When the triaxial accelerometer of the device detects a fall, the current position of the
patient is transmitted to an emergency center through a ZigBee network. In order to clarify the situation of the fallen
patient, 4-s ECG signals are also transmitted. Our transmission scheme ensures the successful transmission of these
critical messages. The experimental results show that our scheme is fast and reliable. We also demonstrate that our
devices can seamlessly integrate with the next generation technology of wireless wide area network, worldwide
interoperability for microwave access, to achieve real-time patient monitoring.

EGC
6205 A RESTful Image Gateway for Multiple Medical Image Repositories

Mobile technologies are increasingly important components in telemedicine systems and are becoming powerful
decision support tools. Universal access to data may already be achieved by resorting to the latest generation of tablet
devices and smartphones. However, the protocols employed for communicating with image repositories are not suited
to exchange data with mobile devices. In this paper, we present an extensible approach to solving the problem of

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querying and delivering data in a format that is suitable for the bandwidth and graphic capacities of mobile devices. We
describe a three-tiered component-based gateway that acts as an intermediary between medical applications and a
number of Picture Archiving and Communication Systems (PACS). The interface with the gateway is accomplished
using Hypertext Transfer Protocol (HTTP) requests following a Representational State Transfer (REST) methodology,
which relieves developers from dealing with complex medical imaging protocols and allows the processing of data on
the server side.

EGC
A Review on Digital ECG Formats and the Relationships Between Them
6206

A plethora of digital ECG formats have been proposed and implemented. This heterogeneity hinders the design and
development of interoperable systems and entails critical integration issues for the healthcare information systems. This
paper aims at performing a comprehensive overview on the current state of affairs of the interoperable exchange of
digital ECG signals. This includes 1) a review on existing digital ECG formats, 2) a collection of applications and
cardiology settings using such formats, 3) a compilation of the relationships between such formats, and 4) a reflection
on the current situation and foreseeable future of the interoperable exchange of digital ECG signals. The objectives have
been approached by completing and updating previous reviews on the topic through appropriate database mining. 39
digital ECG formats, 56 applications, tools or implantation experiences, 47 mappings/converters, and 6 relationships
between such formats have been found in the literature. The creation and generalization of a single standardized ECG
format is a desirable goal. However, this unification requires political commitment and international cooperation among
different standardization bodies. Ongoing ontology-based approaches covering ECG domain have recently emerged as
a promising alternative for reaching fully fledged ECG interoperability in the near future.

EGC A Smart Health Monitoring Chair for Nonintrusive Measurement of Biological Signals
6207

We developed nonintrusive methods for simultaneous electrocardiogram, photoplethysmogram, and ballistocardiogram
measurements that do not require direct contact between instruments and bare skin. These methods were applied to the
design of a diagnostic chair for unconstrained heart rate and blood pressure monitoring purposes. Our methods were
operationalized through capacitively coupled electrodes installed in the chair back that include high-input impedance
amplifiers, and conductive textiles installed in the seat for capacitive driven-right-leg circuit configuration that is capable
of recording electrocardiogram information through clothing. Photoplethysmograms were measured through clothing
using seat mounted sensors with specially designed amplifier circuits that vary in light intensity according to clothing
type. Ballistocardiograms were recorded using a film type transducer material, polyvinylidenefluoride (PVDF), which was
installed beneath the seat cover. By simultaneously measuring signals, beat-to-beat heart rates could be monitored even
when electrocardiograms were not recorded due to movement artifacts. Beat-to-beat blood pressure was also monitored
using unconstrained measurements of pulse arrival time and other physiological parameters, and our experimental
results indicated that the estimated blood pressure tended to coincide with actual blood pressure measurements. This

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study demonstrates the feasibility of our method and device for biological signal monitoring through clothing for
unconstrained long-term daily health monitoring that does not require user awareness and is not limited by physical
activity.

EGC Abnormality Segmentation in Brain Images Via Distributed Estimation
6208

The aim of this paper is to introduce a novel semisupervised scheme for abnormality detection and segmentation in
medical images. Semisupervised learning does not require pathology modeling and, thus, allows high degree of
automation. In abnormality detection, a vector is characterized as anomalous if it does not comply with the probability
distribution obtained from normal data. The estimation of the probability density function, however, is usually not
feasible due to large data dimensionality. In order to overcome this challenge, we treat every image as a network of
locally coherent image partitions (overlapping blocks). We formulate and maximize a strictly concave likelihood function
estimating abnormality for each partition and fuse the local estimates into a globally optimal estimate that satisfies the
consistency constraints, based on a distributed estimation algorithm. The likelihood function consists of a model and a
data term and is formulated as a quadratic programming problem. The method is applied for automatically segmenting
brain pathologies, such as simulated brain infarction and dysplasia, as well as real lesions in diabetes patients. The
assessment of the method using receiver operating characteristic analysis demonstrates improvement in image
segmentation over two-group analysis performed with Statistical Parametric Mapping (SPM).

EGC
6209 An Info button For Web 2.0 Clinical Discussions:The Knowledge Linkage Framework

This paper aims to develop an infobutton to automatically retrieve published papers corresponding to a topic-specific
online clinical discussion. The knowledge linkages infobutton is designed to supplement online clinical conversations
with pertinent medical literature from Pubmed. The project involves three distinct steps: 1) Clinical messages around a
specific problem are grouped together into a thread. 2) These threads are processed using Metamap to link the
conversations to keywords from the MeSH lexicon. 3) These keywords are used in a novel search strategy to retrieve a
set of papers from Pubmed, which are then returned to the user. A pilot study using the messages from 2007 and 2008,
was conducted to compare the knowledge linkage search strategy to a vector space model and extended Boolean
model. The knowledge linkage model proved to be significantly better in terms of precision (p = 0.013 and 0.003,
respectively) and recall (p = 0.351 and 0.013). Pertinent papers were returned to over 55% of the threads. This approach
has demonstrated how clinicians can supplement their peer communications with evidence based research. Future work
should focus on how to improve the threading and keyword-mapping strategies.

EGC Analysis of Using Inter pulse Intervals to Generate128-Bit Biometric Random Binary
6210
Sequences for Securing Wireless Body Sensor Networks

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Wireless body sensor network (WBSN), a key building block for m-Health, demands extremely stringent resource
constraints and thus lightweight security methods are preferred. To minimize resource consumption, utilizing
information already available to a WBSN, particularly common to different sensor nodes of a WBSN, for security
purposes becomes an attractive solution. In this paper, we tested the randomness and distinctiveness of the 128-bit
biometric binary sequences (BSs) generated from interpulse intervals (IPIs) of 20 healthy subjects as well as 30 patients
suffered from myocardial infarction and 34 subjects with other cardiovascular diseases. The encoding time of a
biometric BS on a WBSN node is on average 23 ms and memory occupation is 204 bytes for any given IPI sequence. The
results from five U.S. National Institute of Standards and Technology statistical tests suggest that random biometric BSs
can be generated from both healthy subjects and cardiovascular patients and can potentially be used as authentication
identifiers for securing WBSNs. Ultimately, it is preferred that these biometric BSs can be used as encryption keys such
that key distribution over the WBSN can be avoided.

EGC Anonymization of Longitudinal Electronic Medical Records
6211

Electronic medical record (EMR) systems have enabled healthcare providers to collect detailed patient information from
the primary care domain. At the same time, longitudinal data from EMRs are increasingly combined with biorepositories
to generate personalized clinical decision support protocols. Emerging policies encourage investigators to disseminate
such data in a deidentified form for reuse and collaboration, but organizations are hesitant to do so because they fear
such actions will jeopardize patient privacy. In particular, there are concerns that residual demographic and clinical
features could be exploited for reidentification purposes. Various approaches have been developed to anonymize
clinical data, but they neglect temporal information and are, thus, insufficient for emerging biomedical research
paradigms. This paper proposes a novel approach to share patient-specific longitudinal data that offers robust privacy
guarantees, while preserving data utility for many biomedical investigations. Our approach aggregates temporal and
diagnostic information using heuristics inspired from sequence alignment and clustering methods. We demonstrate that
the proposed approach can generate anonymized data that permit effective biomedical analysis using several patient
cohorts derived from the EMR system of the Vanderbilt University Medical Center.

EGC
6212
Application of Evolutionary Fuzzy Cognitive Mapsfor Prediction of Pulmonary
Infections
In this paper, a new evolutionary-based fuzzy cognitive map (FCM) methodology is proposed to cope with the
forecasting of the patient states in the case of pulmonary infections. The goal of the research was to improve the
efficiency of the prediction. This was succeeded with a new data fuzzification procedure for observables and
optimization of gain of transformation function using the evolutionary learning for the construction of FCM model. The
approach proposed in this paper was validated using real patient data from internal care unit. The results emerged had
less prediction errors for the examined data records than those produced by the conventional genetic-based algorithmic
approaches.
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EGC Artifact Removal in Physiological Signals—Practices and Possibilities
6213

The combination of reducing birth rate and increasing life expectancy continues to drive the demographic shift toward
an aging population. This, in turn, places an ever-increasing burden on healthcare due to the increasing prevalence of
patients with chronic illnesses and the reducing income-generating population base needed to sustain them. The need
to urgently address this healthcare “time bomb” has accelerated the growth in ubiquitous, pervasive, distributed
healthcare technologies. The current move from hospital-centric healthcare toward in-home health assessment is aimed
at alleviating the burden on healthcare professionals, the health care system and caregivers. This shift will also further
increase the comfort for the patient. Advances in signal acquisition, data storage and communication provide for the
collection of reliable and useful in-home physiological data. Artifacts, arising from environmental, experimental and
physiological factors, degrade signal quality and render the affected part of the signal useless. The magnitude and
frequency of these artifacts significantly increases when data collection is moved from the clinic into the home. Signal
processing advances have brought about significant improvement in artifact removal over the past few years. This paper
reviews the physiological signals most likely to be recorded in the home, documenting the artifacts which occur most
frequently and which have the largest degrading effect. A detailed analysis of current artifact removal techniques will
then be presented. An evaluation of the advantages and disadvantages of each of the proposed artifact detection and
removal techniques, with particular application to the personal healthcare domain, is provided.

EGC ARTreat Project: Three-Dimensional Numerical Simulation of Plaque Formation and
6214
Developmentin the Arteries

Despite Atherosclerosis is a progressive disease characterized by the accumulation of lipids and fibrous elements in
arteries. It is characterized by dysfunction of endothelium and vasculitis, and accumulation of lipid, cholesterol, and cell
elements inside blood vessel wall. In this study, a continuum-based approach for plaque formation and development in
3-D is presented. The blood flow is simulated by the 3-D Navier-Stokes equations, together with the continuity equation
while low-density lipoprotein (LDL) transport in lumen of the vessel is coupled with Kedem-Katchalsky equations. The
inflammatory process was solved using three additional reaction-diffusion partial differential equations. Transport of
labeled LDL was fitted with our experiment on the rabbit animal model. Matching with histological data for LDL
localization was achieved. Also, 3-D model of the straight artery with initial mild constriction of 30% plaque for formation
and development is presented.

EGC Assessment of Sensing Fire Fighters Uniforms for Physiological Parameter Measurement
6215
in Harsh Environment
Despite years of research, the name ambiguity problem remains largely unresolved. Outstanding issues include how to

Mining In the last few years, much effort has been devoted to the development of wearable sensing systems able to

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monitor physiological, behavioral, and environmental parameters. Less has been done on the accurate testing and
assessment of this instrumentation, especially when considering devices thought to be used in harsh environments by
subjects or operators performing intense physical activities. This paper presents methodology and results of the
evaluation of wearable physiological sensors under these conditions. The methodology has been applied to a specific
textile-based prototype, aimed at the real-time monitoring of rescuers in emergency contexts, which has been developed
within a European funded project called ProeTEX. Wearable sensor measurements have been compared with the ones of
suitable gold standards through Bland-Altman statistical analysis; tests were realized in controlled environments
simulating typical intervention conditions, with temperatures ranging from 20°C to 45°C and subjects performing mild to
very intense activities. This evaluation methodology demonstrated to be effective for the definition of the limits of use of
wearable sensors. Furthermore, the ProeTEX prototype demonstrated to be reliable, since it produced negligible errors
when used for up to 1 h in normal environmental temperature (20°C and 35°C) and up to 30 min in harsher environment
(45°C).

EGC Assessment of Tremor Activity in the Parkinson’sDisease Using a Set of Wearable
6216
Sensors

Tremor is the most common motor disorder of Parkinson's disease (PD) and consequently its detection plays a crucial
role in the management and treatment of PD patients. The current diagnosis procedure is based on subject-dependent
clinical assessment, which has a difficulty in capturing subtle tremor features. In this paper, an automated method for
both resting and action/postural tremor assessment is proposed using a set of accelerometers mounted on different
patient's body segments. The estimation of tremor type (resting/action postural) and severity is based on features
extracted from the acquired signals and hidden Markov models. The method is evaluated using data collected from 23
subjects (18 PD patients and 5 control subjects). The obtained results verified that the proposed method successfully: 1)
quantifies tremor severity with 87 % accuracy, 2) discriminates resting from postural tremor, and 3) discriminates tremor
from other Parkinsonian motor symptoms during daily activities.

EGC
Automated Recognition of Obstructive Sleep ApneaSyndrome Using Support Vector
6217
Machine Classifier

Obstructive sleep apnea (OSA) is a common sleep disorder that causes pauses of breathing due to repetitive
obstruction of the upper airways of the respiratory system. The effect of this phenomenon can be observed in other
physiological signals like the heart rate variability, oxygen saturation, and the respiratory effort signals. In this study,
features from these signals were extracted from 50 control and 50 OSA patients from the Sleep Heart Health Study
database and implemented for minute and subject classifications. A support vector machine (SVM) classifier was used
with linear and second-order polynomial kernels. For the minute classification, the respiratory features had the highest
sensitivity while the oxygen saturation gave the highest specificity. The polynomial kernel always had better
performance and the highest accuracy of 82.4% (Sen: 69.9%, Spec: 91.4%) was achieved using the combined-feature
classifier. For subject classification, the polynomial kernel had a clear improvement in the oxygen saturation accuracy

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as the highest accuracy of 95% was achieved by both the oxygen saturation (Sen: 100%, Spec: 90.2%) and the
combined-feature (Sen: 91.8%, Spec: 98.0%). Further analysis of the SVM with other kernel types might be useful for
optimizing the classifier with the appropriate features for an OSA automated detection algorithm.

EGC Cardiovascular Modeling of Congenital Heart Disease Based on Neonatal Echo
6218
cardiographic Images

This paper proposes a 3-D cardiovascular modeling system based on neonatal echocardiographic images. With the
system, medical doctors can interactively construct patient-specific cardiovascular models, and share the complex
topology and the shape information. For the construction of cardiovascular models with a variety of congenital heart
diseases, we propose a set of algorithms and interface that enable editing of the topology and shape of the 3-D models.
In order to facilitate interactivity, the centerline and radius of the vessels are used to edit the surface of the heart
vessels. This forms a skeleton where the centerlines of blood vessel serve as the nodes and edges, while the radius of
the blood vessel is given as an attribute value to each node. Moreover, parent-child relationships are given to each
skeleton. They are expressed as the directed acyclic graph, where the skeletons are viewed as graph nodes and the
connecting points are graph edges. The cardiovascular models generated from some patient data confirmed that the
developed technique is capable of constructing cardiovascular disease models in a tolerable timeframe. It is successful
in representing the important structures of the patient-specific heart vessels for better understanding in preoperative
planning and electric medical recording of the congenital heart disease

EGC Compensation of Sound Speed Deviations in3-D B-Mode Ultrasound for Intraoperative
6219
Determination of the Anterior Pelvic Plane

An accurate determination of the pelvic orientation is inevitable for the correct cup prosthesis placement of navigated
total hip arthroplasties. Conventionally, this step is accomplished by percutaneous palpation of anatomic landmarks.
Sterility issues and an increased landmark localization error for obese patients lead to the application of B-mode
ultrasound imaging in the field of computer-assisted orthopedic surgery. Many approaches have been proposed in the
literature to replace the percutaneous digitization by 3-D B-mode ultrasound imaging. However, the correct depth
localization of the pelvic landmarks could be significantly affected by the acoustic properties of the penetrated tissues.
Imprecise depth estimation could lead to a miscalculation of the pelvic orientation and subsequently to a misalignment
of the acetabular cup implant. But so far, no solution has been presented, which compensates for acoustic property
differences for correct depth estimation. In this paper, we present a novel approach to determine pelvic orientation from
ultrasound images by applying a hierarchical registration scheme based on patch statistical shape models to
compensate for differences in speed of sound. The method was validated based on plastic bones and a cadaveric
specimen.

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EGC
Context-Based Electronic Health Record: TowardPatient Specific Healthcare
6220

Due to the increasingly data-intensive clinical environment, physicians now have unprecedented access to detailed
clinical information from a multitude of sources. However, applying this information to guide medical decisions for a
specific patient case remains challenging. One issue is related to presenting information to the practitioner: displaying a
large (irrelevant) amount of information often leads to information overload. Next-generation interfaces for the electronic
health record (EHR) should not only make patient data easily searchable and accessible, but also synthesize fragments
of evidence documented in the entire record to understand the etiology of a disease and its clinical manifestation in
individual patients. In this paper, we describe our efforts toward creating a context-based EHR, which employs
biomedical ontologies and (graphical) disease models as sources of domain knowledge to identify relevant parts of the
record to display. We hypothesize that knowledge (e.g., variables, relationships) from these sources can be used to
standardize, annotate, and contextualize information from the patient record, improving access to relevant parts of the
record and informing medical decision making. To achieve this goal, we describe a framework that aggregates and
extracts findings and attributes from free-text clinical reports, maps findings to concepts in available knowledge
sources, and generates a tailored presentation of the record based on the information needs of the user. We have
implemented this framework in a system called Adaptive EHR, demonstrating its capabilities to present and synthesize
information from neurooncology patients. This paper highlights the challenges and potential applications of leveraging
disease models to improve the access, integration, and interpretation of clinical patient data.

EGC Contextualized Access to Electronical HealthRecords in Cardiology
6220

In this paper, we propose a new approach for accessing the electronical health records (EHR), and we apply it to the
cardiology medical specialty. Though the use of EHR improves the storage and access to the information in it regarding
the previous health records in papers, it entails the risk of having the same problems of huge size and of becoming
inoperative and really difficult to handle, especially if the user is looking for a specific data item. Our proposal is based
on the contextualization of the access, providing the user with the most important information for the assistance act in
which he/she is involved. To do this, we define the set of possible contexts and consider different aspects of the
pertinence of the documents to each context. We do it by using fuzzy logic and pay special attention to the efficiency,
due to the huge size of the involved databases. Our proposal does not limit the access to the EHR, but establishes a
prioritization based on the access needs, which provides the system with an additional advantage, easily enabling the
use of new terminals and devices like tablet PCs and PDAs, which have great limitations in the interfaces.

EGC
Cross-Layer Ultrasound Video Streaming OverMobile WiMAX and HSUPA Networks
6221

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It is well known that the evolution of 4G-based mobile multimedia network systems will contribute significantly to future
mobile healthcare (m-health) applications that require high bandwidth and fast data rates. Central to the success of such
emerging applications is the compatibility of broadband networks, such as mobile Worldwide Interoperability For
Microwave Access (WiMAX) and High-Speed Uplink Packet Access (HSUPA), and especially their rate adaption issues
combined with the acceptable real-time medical quality of service requirements. In this paper, we address the relevant
challenges of cross-layer design requirements for real-time rate adaptation of ultrasound video streaming in mobile
WiMAX and HSUPA networks. A comparative performance analysis of such approach is validated in two experimental m-
health test bed systems for both mobile WiMAX and HSUPA networks. The experimental results have shown an
improved performance of mobile WiMAX compared to the HSUPA using the same cross-layer optimization approach.

EGC Design and Evaluation of a Telemonitoring ConceptBased on NFC-Enabled Mobile
6222
Phonesand Sensor Devices

Utilization of information and communication technologies such as mobile phones and wireless sensor networks
becomes more and more common in the field of telemonitoring for chronic diseases. Providing elderly people with a
mobile-phone-based patient terminal requires a barrier-free design of the overall user interface including the setup of
wireless communication links to sensor devices. To easily manage the connection between a mobile phone and wireless
sensor devices, a concept based on the combination of Bluetooth and near-field communication technology has been
developed. It allows us initiating communication between two devices just by bringing them close together for a few
seconds without manually configuring the communication link. This concept has been piloted with a sensor device and
evaluated in terms of usability and feasibility. Results indicate that this solution has the potential to simplify the
handling of wireless sensor networks for people with limited technical skills.

EGC Detection and Analysis of Transitional Activity in Manifold Space
6223

Activity monitoring is important for assessing daily living conditions for elderly patients and those with chronic
diseases. Transitions between activities can present characteristic patterns that may be indicative of quality of
movement. To detect and analyze transitional activities, a manifold-based approach is proposed in this paper. The
proposed method uses a recursive spectral graph-partitioning algorithm to segment transitions in activity. These
segments are subsequently mapped to a reference manifold space. Categorization of transitions is performed with the
corresponding features in the manifold space. The practical value of the work is demonstrated through data collected
under laboratory conditions, as well as patients recovering from total knee replacement operations, demonstrating
specific transitions and motion impairment compared to normal subjects.

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EGC
6224
Development and Evaluation of an Ambulatory Stress Monitor Based on Wearable
Sensors

Chronic stress is endemic to modern society. However, as it is unfeasible for physicians to continuously monitor stress
levels, its diagnosis is nontrivial. Wireless body sensor networks offer opportunities to ubiquitously detect and monitor
mental stress levels, enabling improved diagnosis, and early treatment. This article describes the development of a
wearable sensor platform to monitor a number of physiological correlates of mental stress. We discuss tradeoffs in both
system design and sensor selection to balance information content and wearability. Using experimental signals
collected from the wearable sensor, we describe a selected number of physiological features that show good correlation
with mental stress. In particular, we propose a new spectral feature that estimates the balance of the autonomic nervous
system by combining information from the power spectral density of respiration and heart rate variability. We validate
the effectiveness of our approach on a binary discrimination problem when subjects are placed under two
psychophysiological conditions: mental stress and relaxation. When used in a logistic regression model, our feature set
is able to discriminate between these two mental states with a success rate of 81% across subjects.

EGC
6225
Emerging Technologies for Patient-Specific Healthcare

In this paper, a series of emerging technologies aim to emphasize the provision of personalized healthcare services to
patients were studied

EGC Experimental Study of a Hybrid Microwave Radiometry—Hyperthermia Apparatus With
6226
the Use of an Anatomical Head Phantom

This paper presents the latest progress made concerning a hybrid diagnostic and therapeutic system able to provide
focused microwave radiometric temperature and/or conductivity variation measurements and hyperthermia treatment.
Previous experimental studies of our group have demonstrated the system performance and focusing properties in
phantom as well as human experiments. The system is able to detect temperature and conductivity variations with
frequency-dependent detection depth and spatial sensitivity. Numerous studies have also demonstrated the
improvement of the system focusing properties attributed to the use of dielectric and left handed matching layers. In this
study, similar experimental procedures are performed but this time using an anatomical head model as phantom aiming
to achieve a more accurate modeling of the system's future real function. This way, another step is made toward the
deeper understanding of the system's capabilities, with the view to further use it in experimental procedures with
laboratory animals and human volunteers.

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EGC
Fuzzy Local Gaussian Mixture Model for Brain MR Image Segmentation
6227

Accurate brain tissue segmentation from magnetic resonance (MR) images is an essential step in quantitative brain
image analysis. However, due to the existence of noise and intensity inhomogeneity in brain MR images, many
segmentation algorithms suffer from limited accuracy. In this paper, we assume that the local image data within each
voxel's neighborhood satisfy the Gaussian mixture model (GMM), and thus propose the fuzzy local GMM (FLGMM)
algorithm for automated brain MR image segmentation. This algorithm estimates the segmentation result that maximizes
the posterior probability by minimizing an objective energy function, in which a truncated Gaussian kernel function is
used to impose the spatial constraint and fuzzy memberships are employed to balance the contribution of each GMM.
We compared our algorithm to state-of-the-art segmentation approaches in both synthetic and clinical data. Our results
show that the proposed algorithm can largely overcome the difficulties raised by noise, low contrast, and bias field, and
substantially improve the accuracy of brain MR image segmentation.

EGC
HBS: A Novel Biometric Feature Based on Heartbeat Morphology
6228

In this paper, a new feature named heartbeat shape (HBS) is proposed for ECG-based biometrics. HBS is computed from
the morphology of segmented heartbeats. Computation of the feature involves three basic steps: 1) resampling and
normalization of a heartbeat; 2) reduction of matching error; and 3) shift invariant transformation. In order to construct
both gallery and probe templates, a few consecutive heartbeats which could be captured in a reasonably short period of
time are required. Thus, the identification and verification methods become efficient. We have tested the proposed
feature independently on two publicly available databases with 76 and 26 subjects, respectively, for identification and
verification. The second database contains several subjects having clinically proven cardiac irregularities (atrial
premature contraction arrhythmia). Experiments on these two databases yielded high identification accuracy (98% and
99.85%, respectively) and low verification equal error rate (1.88% and 0.38%, respectively). These results were obtained
by using templates constructed from five consecutive heartbeats only. This feature compresses the original ECG signal
significantly to be useful for efficient communication and access of information in telecardiology scenarios.

EGC High-Grade Glioma Diffusive Modeling Using Statistical Tissue Information and
6229
Diffusion Tensors Extracted from Atlases

Glioma, especially glioblastoma, is a leading cause of brain cancer fatality involving highly invasive and neoplastic
growth. Diffusive models of glioma growth use variations of the diffusion-reaction equation in order to simulate the
invasive patterns of glioma cells by approximating the spatiotemporal change of glioma cell concentration. The most
advanced diffusive models take into consideration the heterogeneous velocity of glioma in gray and white matter, by
using two different discrete diffusion coefficients in these areas. Moreover, by using diffusion tensor imaging (DTI), they

Projects


simulate the anisotropic migration of glioma cells, which is facilitated along white fibers, assuming diffusion tensors
with different diffusion coefficients along each candidate direction of growth. Our study extends this concept by fully
exploiting the proportions of white and gray matter extracted by normal brain atlases, rather than discretizing diffusion
coefficients. Moreover, the proportions of white and gray matter, as well as the diffusion tensors, are extracted by the
respective atlases; thus, no DTI processing is needed. Finally, we applied this novel glioma growth model on real data
and the results indicate that prognostication rates can be improved.

EGC
In-Depth Analysis and Evaluation of DiffusiveGlioma Models
6230

In-Depth Analysis and Evaluation of Diffusive Glioma Models and inclusion dependency discovery is important to
knowledge discovery, database semantics analysis, database design, and data quality assessment. Motivated by the
importance of dependency discovery, this paper reviews the methods for functional dependency, conditional functional
dependency, approximate functional dependency, and inclusion dependency discovery in relational databases and a
method for discovering XML functional dependencies.

EGC Integrated e-Health Approach Based on Vascula Ultrasound and Pulse Wave Analysis
6231 for Asymptomatic Atherosclerosis Detection and Cardiovascular Risk Stratification in
the Community
New strategies are urgently needed to identify subjects at increased risk of atherosclerotic cardiovascular disease
(ACVD) development or complications. A National Public University Center (CUiiDARTE) was created in Uruguay, based
on six main pillars: 1) integration of experts in different disciplines and creation of multidisciplinary teams, 2) incidence
in public and professional education programs to give training in the use of new technologies and to shift the focus from
ACVD treatment to disease prevention, 3) implementation of free vascular studies in the community (distributed rather
than centralized healthcare), 4) innovation and application of e-Health and noninvasive technology and approaches, 5)
design and development of a biomedical approach to determine the target population and patient workflow, and 6)
improvement in individual risk estimation and differentiation between aging and ACVD-related arterial changes using
population-based epidemiological and statistical patient-specific models. This work describes main features of
CUiiDARTE project implementation, the scientific and technological steps and innovations done for individual risk
stratification, and sub-clinical ACVD diagnosis.

EGC
Low-Complexity Image Processing for Real-Time Detection of Neonatal Clonic
6232
Seizures
In this paper, we consider a novel low-complexity real-time image-processing-based approach to the detection of
neonatal clonic seizures. Our approach is based on the extraction, from a video of a newborn, of an average luminance
signal representative of the body movements. Since clonic seizures are characterized by periodic movements of parts of
the body (e.g., the limbs), by evaluating the periodicity of the extracted average luminance signal it is possible to detect
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the presence of a clonic seizure. The periodicity is investigated, through a hybrid autocorrelation-Yin estimation
technique, on a per-window basis, where a time window is defined as a sequence of consecutive video frames. While
processing is first carried out on a single window basis, we extend our approach to interlaced windows. The
performance of the proposed detection algorithm is investigated, in terms of sensitivity and specificity, through receiver
operating characteristic curves, considering video recordings of newborns affected by neonatal seizures.

EGC Mining Data From Hemodynamic Simulations for Generating Prediction and Explanation
6233 Models

One of the most common causes of human death is stroke, which can be caused by carotid bifurcation stenosis. In our
work, we aim at proposing a prototype of a medical expert system that could significantly aid medical experts to detect
hemodynamic abnormalities (increased artery wall shear stress). Based on the acquired simulated data, we apply
several methodologies for1) predicting magnitudes and locations of maximum wall shear stress in the artery, 2)
estimating reliability of computed predictions, and 3) providing user-friendly explanation of the model's decision. The
obtained results indicate that the evaluated methodologies can provide a useful tool for the given problem domain.

EGC
Mobile Medical Visual Information Retrieval
6234

In this paper, we propose mobile access to peer-reviewed medical information based on textual search and content-
based visual image retrieval. Web-based interfaces designed for limited screen space were developed to query via web
services a medical information retrieval engine optimizing the amount of data to be transferred in wireless form. Visual
and textual retrieval engines with state-of-the-art performance were integrated. Results obtained show a good usability
of the software. Future use in clinical environments has the potential of increasing quality of patient care through
bedside access to the medical literature in context.

EGC
Multisensor Data Fusion in an Integrated Tracking System for Endoscopic Surgery
6235

Surgical planning and navigation systems are vital for minimally invasive endoscopic surgeries but it is challenging to
track the position and orientation of intrabody surgical instruments in these procedures. In order to address this
problem, we propose a tracking system including multiple-sensor integration and data fusion. The proposed tracking
approach is free of the constraints of line-of-sight, less subject to environmental distortion, and with higher update rate.
By incorporating electromagnetic and inertial sensors, the system yields continuous 6-DOF information. Based on a
system dynamic model and estimation theories, a new multisensor fusion algorithm, cascade orientation and position-
estimation algorithm, is proposed for the integrated tracking device. The experimental results show that the proposed
algorithms achieve accurate orientation and position tracking with robustness.

Projects


EGC NeuroGlasses: A Neural Sensing Healthcare System for 3-D Vision Technology
6236

3-D vision technologies are extensively used in a wide variety of applications. Particularly glasses-based 3-D technology
facilities are increasingly becoming affordable to our daily lives. Considering health issues raised by 3-D video
technologies, to the best of our knowledge, most of the pilot studies are practiced in a highly-controlled laboratory
environment only. In this paper, we present NeuroGlasses, a nonintrusive wearable physiological signal monitoring
system to facilitate health analysis and diagnosis of 3-D video watchers. The NeuroGlasses system acquires health-
related signals by physiological sensors and provides feedbacks of health-related features. Moreover, the NeuroGlasses
system employs signal-specific reconstruction and feature extraction to compensate the distortion of signals caused by
variation of the placement of the sensors. We also propose a server-based NeuroGlasses infrastructure where
physiological features can be extracted for real-time response or collected on the server side for long term analysis and
diagnosis. Through an on-campus pilot study, the experimental results show that NeuroGlasses system can effectively
provide physiological information for healthcare purpose. Furthermore, it approves that 3-D vision technology has a
significant impact on the physiological signals, such as EEG, which potentially leads to neural diseases.

EGC Noise-Assisted Correlation Algorithm for Suppressing Noise-Induced Artifacts in
6237
Ultrasonic Nakagami Images

Ultrasonic Nakagami images can complement conventional B-mode images for scatterer characterization. White noise in
anechoic areas leads to artifacts that affect the Nakagami image to characterize tissues. Artifact removal requires
rejection of the white noise without deforming the backscattered waveform. This study proposes a noise-assisted
correlation algorithm (NCA) and carries out simulations, phantom experiments, and clinical measurements to validate its
feasibility and practicality. The simulation results show that the NCA can reject white noise in an anechoic area without
any deformation of the backscattered waveforms. The results obtained from phantoms and tissues further demonstrate
that the proposed NCA can suppress a Nakagami image artifact without changing the texture of the Nakagami image of
the scattering background. The NCA is an essential algorithm to construct artifact-free Nakagami image for correctly
reflecting scatterer properties of biological tissues

EGC
6238 Numerical Characterization and Modeling of Subject-Specific Ultra wide band Body-
Centric Radio Channels and Systems for Healthcare Applications

The paper presents a subject-specific radio propagation study and system modeling in wireless body area networks
using a simulation tool based on the parallel finite-difference time-domain technique. This technique is well suited to
model the radio propagation around complex, inhomogeneous objects such as the human body. The impact of different
digital phantoms in on-body radio channel and system performance was studied. Simulations were performed at the
frequency of 3-10 GHz considering a typical hospital environment, and were validated by on-site measurements with

Projects


reasonably good agreement. The analysis demonstrated that the characteristics of the on-body radio channel and
system performance are subject-specific and are associated with human genders, height, and body mass index.
Maximum variations of almost 18.51% are observed in path loss exponent due to change of subject, which gives
variations of above 50% in system bit error rate performance. Therefore, careful consideration of subject-specific
parameters are necessary for achieving energy efficient and reliable radio links and system performance for body-
centric wireless network.

EGC Personalization and Adaptation to the Medium and Context in a Fall Detection System
6239

The main objective of this paper is to present a distributed processing architecture that explicitly integrates capabilities
for its continuous adaptation to the medium, the context, and the user. This architecture is applied to a falling detection
system through: (1) an optimization module that finds the optimal operation parameters for the detection algorithms of
the system devices; (2) a distributed processing architecture that provides capabilities for remote firmware update of the
smart sensors. The smart sensor also provides an estimation of activities of daily living (ADL), which results very useful
in monitoring of the elderly and patients with chronic diseases. The developed experiments have demonstrated the
feasibility of the system and specifically, the accuracy of the proposed algorithms and procedures (100% success for
impact detection, 100% sensitivity and 95.68% specificity rates for fall detection, and 100% success for ADL level
classification). Although the experiments have been developed with a cohort of young volunteers, the personalization
and adaption mechanisms of the proposed architecture related to the concepts of "design for all" and "design space"
will significantly ease the adaptation of the system for its application to the elderly.
EGC Prediction of Interstitial Glucose Level
6240

Glucose is an important source of energy for cells. In clinical practice, we measure glucose level in blood and interstitial
fluid. Each method has its pros and cons, and both levels correlate with each other. As the body tries to maintain the
glucose level within a particular range to avoid adverse effects, it is desirable to predict future glucose levels in order to
aid provided health care. We can see this desire in research, e.g., research on glucose transporters of cells. As yet
another example, we can see it with diabetic patients, patients in a metabolic intensive care unit, particularly. In this
paper, a glucose level prediction method is proposed.

EGC
6241
Prediction of the Timing and the Rhythm of the Parkinsonian Subthalamic Nucleus
Neural Spikes Using the Local Field Potentials

In this paper, we discuss the use of a nonlinear cascade model to predict the subthalamic nucleus spike activity from
the local field potentials recorded in the motor area of the nucleus of Parkinson's disease patients undergoing deep
brain stimulation. We use a segment of appropriately selected and processed data recorded from five nuclei to acquire
the information of the spike timing and rhythm of a single neuron and estimate the model parameters. We then use the
rest of each recording to assess the model's accuracy in predicting spike timing, rhythm, and interspike intervals. We

Projects


show that the cumulative distribution function (CDF) of the predicted spikes remains inside the 95% confidence interval
of the CDF of the recorded spikes. By training the model appropriately, we prove its ability to provide quite accurate
predictions for multiple-neuron recordings as well, and we establish its validity as a simple yet biologically plausible
model of the intranuclear spike activity recorded from Parkinson's disease patients.

.
EGC Probabilistic Learning From Incomplete Data for Recognition of Activities of Daily Living
6242
in Smart Homes

Learning behavioral patterns for activities of daily living in a smart home environment can be challenged by the limited
number of training data that may be available. This may be due to the infrequent repetition of routine activities (e.g.,
once daily), the expense of using observers to label activities, and the intrusion that would be caused by the presence of
observers over long time periods. It is important, therefore, to make as much use of any labeled data that are collected,
however, incomplete these data may be. In this paper, we propose an algorithm for learning behavioral patterns for
multi-inhabitants living in a single smart home environment, by making full use of all limited labeled activities, including
incomplete data resulting from unreliable low-level sensors in this environment. Through maximum-likelihood
estimation, using Expectation-Maximization, we build a model that captures both environmental uncertainties from
sensor readings and user uncertainties, including variations in how individuals carry out activities. Our algorithm
outperforms models that cannot handle data incompleteness, with increasing performance gains as incompleteness
increases. The approach also enables the impact of particular sensors to be assessed and can thus inform sensor
maintenance and deployment.

EGC Prognosis of Right Ventricular Failure in Patients With Left Ventricular Assist Device
6243 Based on Decision Tree With SMOTE

Right ventricular failure is a significant complication following implantation of a left ventricular assist device (LVAD),
which increases morbidity and mortality. Consequently, researchers have sought predictors that may identify patients at
risk. However, they have lacked sensitivity and/or specificity. This study investigated the use of a decision tree
technology to explore the preoperative data space for combinatorial relationships that may be more accurate and
precise. We retrospectively analyzed the records of 183 patients with initial LVAD implantation at the Artificial Heart
Program, University of Pittsburgh Medical Center, between May 1996 and October 2009. Among those patients, 27 later
required a right ventricular assist device (RVAD+) and 156 remained on LVAD (RVAD-) until the time of transplantation or
death. A synthetic minority oversampling technique (SMOTE) was applied to the RVAD+ group to compensate for the
disparity of sample size. Twenty-one resampling levels were evaluated, with decision tree model built for each. Among
these models, the top six predictors of the need for an RVAD were transpulmonary gradient (TPG), age, international
normalized ratio (INR), heart rate (HR), aspartate aminotransferase (AST), prothrombin time, and right ventricular
systolic pressure. TPG was identified to be the most predictive variable in 15 out of 21 models, and constituted the first
splitting node with 7 mmHg as the breakpoint. Oversampling was shown to improve the senstivity of the models

Projects


monotonically, although asymptotically, while the specificity was diminished to a lesser degree. The model built upon 5X
synthetic RVAD+ oversampling was found to provide the best compromise between sensitivity and specificity, included
TPG (layer 1), age (layer 2), right atrial pressure (layer 3), HR (layer 4,7), INR (layer 4, 9), alanine aminotransferase (layer
5), white blood cell count (layer 5,6, &7), the number of inotrope agents (layer 6), creatinine (layer 8), A- T (layer 9, 10),
and cardiac output (layer 9). It exhibited 85% sensitivity, 83% specificity, and 0.87 area under the receiver operating
characteristic curve (RoC), which was found to be greatly improved compared to previously published studies.

EGC Real-Time Sleep Apnea Detection by Classifier Combination
6244

To find an efficient and valid alternative of polysomnography (PSG), this paper investigates real-time sleep apnea and
hypopnea syndrome (SAHS) detection based on electrocardiograph (ECG) and saturation of peripheral oxygen (SpO2)
signals, individually and in combination. We include ten machine-learning algorithms in our classification experiment. It
is shown that our proposed SpO2 features outperform the ECG features in terms of diagnostic ability. More importantly,
we propose classifier combination to further enhance the classification performance by harnessing the complementary
information provided by individual classifiers. With our selected SpO2 and ECG features, the classifier combination
using AdaBoost with Decision Stump, Bagging with REPTree, and either kNN or Decision Table achieves sensitivity,
specificity, and accuracy all around 82% for a minute-based real-time SAHS detection over 25 sleep-disordered-
breathing suspects' full overnight recordings.

EGC Rhythmogram-Based Analysis for Continuous Electrographic Data of the Human Brain
6245

Ecologically relevant stimuli are rarely used in scientific studies because they are difficult to control. Instead,
researchers employ simple stimuli with sharp boundaries (in space and time). Here, we explore how the rhythmogram
can be used to provide much needed rigorous control of natural continuous stimuli like music and speech. The analysis
correlates important features in the time course of stimuli with corresponding features in brain activations elicited by the
same stimuli. Correlating the identified regularities of the stimulus time course with the features extracted from the
activations of each voxel of a tomographic analysis of brain activity provides a powerful view of how different brain
regions are influenced by the stimulus at different times and over different (user-selected) timescales. The application of
the analysis to tomographic solutions extracted from magnetoencephalographic data recorded while subjects listen to
music reveals a surprising and aesthetically pleasing aspect of brain function: an area believed to be specialized for
visual processing is recruited to analyze the music after the acoustic signal is transformed to a feature map. The
methodology is ideal for exploring processing of complex stimuli, e.g., linguistic structure and meaning and how it fails,
for example, in developmental dyslexia.

Projects


EGC Robust Real-Time-Constrained Estimation of Respiratory Motion for Interventional MRI on
6246
Mobile Organs

Real-time magnetic resonance imaging is a promising tool for image-guided interventions. For applications such as
thermotherapy on moving organs, a precise image-based compensation of motion is required in real time to allow
quantitative analysis, retrocontrol of the interventional device, or determination of the therapy endpoint. Reduced field-
of-view imaging represents a promising way to improve spatial and / or temporal resolution. However, it introduces new
challenges for target motion estimation, since structures near the target may appear transiently due to the respiratory
motion and the limited spatial coverage. In this paper, a new image-based motion estimation method is proposed
combining a global motion estimation with a novel optical flow approach extending the initial Horn and Schunck (H&S)
method by an additional regularization term. This term integrates the displacement of physiological landmarks into the
variational formulation of the optical flow problem. This allowed for a better control of the optical flow in presence of
transient structures. The method was compared to the same registration pipeline employing the H&S approach on a
synthetic dataset and in vivo image sequences. Compared to the H&S approach, a significant improvement (p <; & 0.05)
of the Dice's similarity criterion computed between the reference and the registered organ positions was achieved.

EGC Secure Management of Biomedical Data With Cryptographic Hardware
6247

The biomedical community is increasingly migrating toward research endeavors that are dependent on large quantities
of genomic and clinical data. At the same time, various regulations require that such data be shared beyond the initial
collecting organization (e.g., an academic medical center). It is of critical importance to ensure that when such data are
shared, as well as managed, it is done so in a manner that upholds the privacy of the corresponding individuals and the
overall security of the system. In general, organizations have attempted to achieve these goals through deidentification
methods that remove explicitly, and potentially, identifying features (e.g., names, dates, and geocodes). However, a
growing number of studies demonstrate that deidentified data can be reidentified to named individuals using simple
automated methods. As an alternative, it was shown that biomedical data could be shared, managed, and analyzed
through practical cryptographic protocols without revealing the contents of any particular record. Yet, such protocols
required the inclusion of multiple third parties, which may not always be feasible in the context of trust or bandwidth
constraints. Thus, in this paper, we introduce a framework that removes the need for multiple third parties by collocating
services to store and to process sensitive biomedical data through the integration of cryptographic hardware. Within
this framework, we define a secure protocol to process genomic data and perform a series of experiments to
demonstrate that such an approach can be run in an efficient manner for typical biomedical investigations.

Projects


EGC Semantic Image Retrieval in Magnetic Resonance Brain Volumes
6248

Practitioners in the area of neurology often need to retrieve multimodal magnetic resonance (MR) images of the brain to
study disease progression and to correlate observations across multiple subjects. In this paper, a novel technique for
retrieving 2-D MR images (slices) in 3-D brain volumes is proposed. Given a 2-D MR query slice, the technique identifies
the 3-D volume among multiple subjects in the database, associates the query slice with a specific region of the brain,
and retrieves the matching slice within this region in the identified volumes. The proposed technique is capable of
retrieving an image in multimodal and noisy scenarios. In this study, support vector machines (SVM) are used for
identifying 3-D MR volume and for performing semantic classification of the human brain into various semantic regions.
In order to achieve reliable image retrieval performance in the presence of misalignments, an image registration-based
retrieval framework is developed. The proposed retrieval technique is tested on various modalities. The test results
reveal superior robustness performance with respect to accuracy, speed, and multimodality

EGC
6249
SEMPATH Ontology: Modeling Multidisciplinary Treatment Schemes Utilizing Semantics

A dramatic increase of demand for provided treatment quality has occurred during last decades. The main challenge to
be confronted, so as to increase treatment quality, is the personalization of treatment, since each patient constitutes a
unique case. Healthcare provision encloses a complex environment since healthcare provision organizations are highly
multidisciplinary. In this paper, we present the conceptualization of the domain of clinical pathways (CP). The SEMPATH
(SEMantic PATHways) Oontology comprises three main parts: 1) the CP part; 2) the business and finance part; and 3)
the quality assurance part. Our implementation achieves the conceptualization of the multidisciplinary domain of
healthcare provision, in order to be further utilized for the implementation of a Semantic Web Rules (SWRL rules)
repository. Finally, SEMPATH Ontology is utilized for the definition of a set of SWRL rules for the human papillomavirus)
disease and its treatment scheme.

EGC
6250 Spark Med: A Framework for Dynamic Integration of Multimedia Medical Data Into
Distributed m-Health Systems

With the advent of 4G and other long-term evolution (LTE) wireless networks, the traditional boundaries of patient
record propagation are diminishing as networking technologies extend the reach of hospital infrastructure and provide
on-demand mobile access to medical multimedia data. However, due to legacy and proprietary software, storage and
decommissioning costs, and the price of centralization and redevelopment, it remains complex, expensive, and often
unfeasible for hospitals to deploy their infrastructure for online and mobile use. This paper proposes the SparkMed data
integration framework for mobile healthcare (m-Health), which significantly benefits from the enhanced network

Projects


capabilities of LTE wireless technologies, by enabling a wide range of heterogeneous medical software and database
systems (such as the picture archiving and communication systems, hospital information system, and reporting
systems) to be dynamically integrated into a cloud-like peer-to-peer multimedia data store. Our framework allows
medical data applications to share data with mobile hosts over a wireless network (such as WiFi and 3G), by binding to
existing software systems and deploying them as m-Health applications. SparkMed integrates techniques from
multimedia streaming, rich Internet applications (RIA), and remote procedure call (RPC) frameworks to construct a Self-
managing, Pervasive Automated netwoRK for Medical Enterprise Data (SparkMed). Further, it is resilient to failure, and
able to use mobile and handheld devices to maintain its network, even in the absence of dedicated server devices. We
have developed a prototype of the SparkMed framework for evaluation on a radiological workflow simulation, which
uses SparkMed to deploy a radiological image viewer as an m-Health application for telemedical use by radiologists and
stakeholders. We have evaluated our prototype using ten devices over WiFi and 3G, verifying that our framework meets
its two main objectives: 1) interactive- delivery of medical multimedia data to mobile devices; and 2) attaching to non-
networked medical software processes without significantly impacting their performance. Consistent response times of
under 500 ms and graphical frame rates of over 5 frames per second were observed under intended usage conditions.
Further, overhead measurements displayed linear scalability and low resource requirements.

EGC
Study of Attenuation and Dispersion Through the Skin in Intra body Communications
6251
Systems

Intrabody communication (IBC) is a technique that uses the human body as a transmission medium for electrical signals
to connect wireless body sensors, e.g., in biomedical monitoring systems. In this paper, we propose a simple, but
accurate propagation model through the skin based on a distributed-parameter circuit in order to obtain general
expressions that could assist in the design of IBC systems. In addition, the model is based on the major
electrophysiological properties of the skin. We have found the attenuation and dispersion parameters and they have
been successfully compared with several published results, thus showing the tuning capability of the model to different
experimental conditions. Finally, we have evaluated different digital modulation schemes in order to assess the tradeoffs
between symbol rate, bit error rate, and distance between electrodes of the skin communication channel

EGC Subject-Specific Estimation of Central Aortic Blood Pressure Using an Individualized
6252
Transfer Function: A Preliminary Feasibility Study

This paper presents a new approach to the estimation of unknown central aortic blood pressure waveform from a
directly measured peripheral blood pressure waveform, in which a physics-based model is employed to solve for a
subject- and state-specific individualized transfer function (ITF). The ITF provides the means to estimate the unknown
central aortic blood pressure from the peripheral blood pressure. Initial proof-of-principle for the ITF is demonstrated
experimentally through an in vivo protocol. In swine subjects taken through wide range of physiologic conditions, the
ITF was on average able to provide central aortic blood pressure waveforms more accurately than a nonindividualized

Projects


transfer function. Its usefulness was most evident when the subject's pulse transit time deviated from normative values.
In these circumstances, the ITF yielded statistically significant reductions over a nonindividualized transfer function in
the following three parameters: 1) 30% reduction in the root-mean-squared error between estimated versus actual
central aortic blood pressure waveform (p <; 10-4), 2) >;50% reduction in the error between estimated versus actual
systolic and pulse pressures ( p <; 10), and 3) a reduction in the overall breakdown rate (i.e., the frequency of estimation
errors >;3 mmHg, p <; 10-4). In conclusion, the ITF may offer an attractive alternative to existing methods that estimates
the central aortic blood pressure waveform, and may be particularly useful in nonnormative physiologic conditions.

EGC
6253
Topic Mining over Asynchronous Text Sequences

Time stamped texts, or text sequences, are ubiquitous in real-world applications. Multiple text sequences are often
related to each other by sharing common topics. The correlation among these sequences provides more meaningful and
comprehensive clues for topic mining than those from each individual sequence. However, it is nontrivial to explore the
correlation with the existence of asynchronism among multiple sequences, i.e., documents from different sequences
about the same topic may have different time stamps. In this paper, we formally address this problem and put forward a
novel algorithm based on the generative topic model. Our algorithm consists of two alternate steps: the first step
extracts common topics from multiple sequences based on the adjusted time stamps provided by the second step; the
second step adjusts the time stamps of the documents according to the time distribution of the topics discovered by the
first step. We perform these two steps alternately and after iterations a monotonic convergence of our objective function
can be guaranteed. The effectiveness and advantage of our approach were justified through extensive empirical studies
on two real data sets consisting of six research paper repositories and two news article feeds, respectively

EGC
6254
Toward Semantic Interoperability of Electronic Health Records

Time stamped texts, or text sequences, are ubiquitous in real-world applications. Multiple text sequences are often
related to each other by sharing common topics. The correlation among these sequences provides more meaningful and
comprehensive clues for topic mining than those from each individual sequence. However, it is nontrivial to explore the
correlation with the existence of asynchronism among multiple sequences, i.e., documents from different sequences
about the same topic may have different time stamps. In this paper, we formally address this problem and put forward a
novel algorithm based on the generative topic model. Our algorithm consists of two alternate steps: the first step
extracts common topics from multiple sequences based on the adjusted time stamps provided by the second step; the
second step adjusts the time stamps of the documents according to the time distribution of the topics discovered by the
first step. We perform these two steps alternately and after iterations a monotonic convergence of our objective function
can be guaranteed. The effectiveness and advantage of our approach were justified through extensive empirical studies
on two real data sets consisting of six research paper repositories and two news article feeds, respectively.

Projects


EGC Tumor Recognition in Wireless Capsule Endoscopy Images Using Textural Features and
6255
SVM-Based Feature Selection

Tumor in digestive tract is a common disease and wireless capsule endoscopy (WCE) is a relatively new technology to
examine diseases for digestive tract especially for small intestine. This paper addresses the problem of automatic
recognition of tumor for WCE images. Candidate color texture feature that integrates uniform local binary pattern and
wavelet is proposed to characterize WCE images. The proposed features are invariant to illumination change and
describe multiresolution characteristics of WCE images. Two feature selection approaches based on support vector
machine, sequential forward floating selection and recursive feature elimination, are further employed to refine the
proposed features for improving the detection accuracy. Extensive experiments validate that the proposed computer-
aided diagnosis system achieves a promising tumor recognition accuracy of 92.4% in WCE images on our collected
data.

EGC Ultrasound Beam forming Using Compressed Data
6256

Traditional clustering techniques are inapplicable to problems where the relationships between data points evolve over
time. Not only is it important for the clustering algorithm to adapt to the recent changes in the evolving data, but it also
needs to take the historical relationship between the data points into consideration. In this paper, we propose ECKF, a
general framework for evolutionary clustering large-scale data based on low-rank kernel matrix factorization. To the best
of our knowledge, this is the first work that clusters large evolutionary data sets by the amalgamation of low-rank matrix
approximation methods and matrix factorization-based clustering. Since the low-rank approximation provides a compact
representation of the original matrix, and especially, the near-optimal low-rank approximation can preserve the sparsity
of the original data, ECKF gains computational efficiency and hence is applicable to large evolutionary data sets.
Moreover, matrix factorization-based methods have been shown to effectively cluster high-dimensional data in text
mining and multimedia data analysis. From a theoretical standpoint, we mathematically prove the convergence and
correctness of ECKF, and provide detailed analysis of its computational efficiency (both time and space). Through
extensive experiments performed on synthetic and real data sets, we show that ECKF outperforms the existing methods
in evolutionary clustering.

EGC
6257
Video Distribution Techniques Over WiMAX Networks for m-Health Applications

In this paper, we propose a novel approach for video distribution over IEEE 802.16 networks for mobile Healthcare (m-
Health) applications. The technique incorporates resource distribution, scheduling, and content-aware video streaming
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taking advantage of a flexible quality of service functionality offered by IEEE 802.16/WiMAX technology. The proposed
technique is thoroughly investigated using network simulator software under various real-life m-Health scenarios, which
include streaming video over medium access control layer service connections. It is shown that the technique is fully
compatible with the WiMAX standard specification and allows a 9-16% increase in the overall network throughput, which
is dependent upon the initial system configuration and the selection of WiMAX user parameters

EGC
6258
Wavelet-Based Energy Features for Glaucomatous Image Classification

Texture features within images are actively pursued for accurate and efficient glaucoma classification. Energy
distribution over wavelet subbands is applied to find these important texture features. In this paper, we investigate the
discriminatory potential of wavelet features obtained from the daubechies (db3), symlets (sym3), and biorthogonal
(bio3.3, bio3.5, and bio3.7) wavelet filters. We propose a novel technique to extract energy signatures obtained using 2-D
discrete wavelet transform, and subject these signatures to different feature ranking and feature selection strategies. We
have gauged the effectiveness of the resultant ranked and selected subsets of features using a support vector machine,
sequential minimal optimization, random forest, and naive Bayes classification strategies. We observed an accuracy of
around 93% using tenfold cross validations to demonstrate the effectiveness of these methods.

EGC Wireless Capsule Endoscopy Video Segmentation Using an Unsupervised Learning
6259 Approach Based on Probabilistic Latent Semantic Analysis With Scale Invariant Features

Since wireless capsule endoscopy (WCE) is a novel technology for recording the videos of the digestive tract of a
patient, the problem of segmenting the WCE video of the digestive tract into subvideos corresponding to the entrance,
stomach, small intestine, and large intestine regions is not well addressed in the literature. A selected few papers
addressing this problem follow supervised leaning approaches that presume availability of a large database of correctly
labeled training samples. Considering the difficulties in procuring sizable WCE training data sets needed for achieving
high classification accuracy, we introduce in this paper an unsupervised learning approach that employs Scale Invariant
Feature Transform (SIFT) for extraction of local image features and the probabilistic latent semantic analysis (pLSA)
model used in the linguistic content analysis for data clustering. Results of experimentation indicate that this method
compares well in classification accuracy with the state-of-the-art supervised classification approaches to WCE video
segmentation.

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