ieee projects 2012 2013 - bio medicine

Elysium Technologies Private Limited Approved by ISO 9001:2008 and AICTE for SKP Training Singapore | Madurai | Trichy | Coimbatore | Cochin | Kollam | Chennai http://www.elysiumtechnologies.com, [email protected]

IEEE Final Year Projects 2012 |Student Projects | Information Technology & Bio Medicine

Projects

IEEE FINAL YEAR PROJECTS 2012 – 2013

BIO- MEDICINE

Corporate Office: Madurai

227-230, Church road, Anna nagar, Madurai – 625 020.

0452 – 4390702, 4392702, +9199447933980

Email: [email protected], [email protected]

Website: www.elysiumtechnologies.com

Branch Office: Trichy

15, III Floor, SI Towers, Melapudur main road, Trichy – 620 001.

0431 – 4002234, +919790464324.

Email: [email protected], [email protected].


Branch Office: Coimbatore

577/4, DB Road, RS Puram, Opp to KFC, Coimbatore – 641 002.

+919677751577

Website: Elysiumtechnologies.com, Email: [email protected]

Branch Office: Kollam

Surya Complex, Vendor junction, Kollam – 691 010, Kerala.

0474 – 2723622, +919446505482.

Email: [email protected].


Branch Office: Cochin

4th

Floor, Anjali Complex, near south over bridge, Valanjambalam,

Cochin – 682 016, Kerala.

0484 – 6006002, +917736004002.

mailto:[email protected]








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Email: [email protected], Website: www.elysiumtechnologies.com

INFORMATION TECHNOLOGY AND BIO MEDICINE 2012 - 2013

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.

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.

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

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

Therapy

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

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




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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.

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.

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

A Reliable Transmission Protocol for ZigBee-Based Wireless Patient Monitoring

A RESTful Image Gateway for Multiple Medical Image Repositories



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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.

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.

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

A Smart Health Monitoring Chair for Nonintrusive Measurement of Biological Signals

A Review on Digital ECG Formats and the Relationships Between Them



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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.

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).

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.

Abnormality Segmentation in Brain Images Via Distributed Estimation

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

Analysis of Using Inter pulse Intervals to Generate128-Bit Biometric Random Binary

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.

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.

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.

Anonymization of Longitudinal Electronic Medical Records

Application of Evolutionary Fuzzy Cognitive Mapsfor Prediction of Pulmonary

Infections



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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.

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.

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

Artifact Removal in Physiological Signals—Practices and Possibilities

ARTreat Project: Three-Dimensional Numerical Simulation of Plaque Formation and

Developmentin the Arteries

Assessment of Sensing Fire Fighters Uniforms for Physiological Parameter Measurement

in Harsh Environment



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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).

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.

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

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

Sensors

Automated Recognition of Obstructive Sleep ApneaSyndrome Using Support Vector

Machine Classifier



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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.

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

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.

Compensation of Sound Speed Deviations in3-D B-Mode Ultrasound for Intraoperative

Determination of the Anterior Pelvic Plane

Cardiovascular Modeling of Congenital Heart Disease Based on Neonatal Echo

cardiographic Images



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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.

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.

Contextualized Access to Electronical HealthRecords in Cardiology

Context-Based Electronic Health Record: TowardPatient Specific Healthcare

Cross-Layer Ultrasound Video Streaming OverMobile WiMAX and HSUPA Networks



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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.

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.

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.

Design and Evaluation of a Telemonitoring ConceptBased on NFC-Enabled Mobile

Phonesand Sensor Devices

Detection and Analysis of Transitional Activity in Manifold Space



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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.

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

patients were studied

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.

Experimental Study of a Hybrid Microwave Radiometry—Hyperthermia Apparatus With

the Use of an Anatomical Head Phantom

Emerging Technologies for Patient-Specific Healthcare

Development and Evaluation of an Ambulatory Stress Monitor Based on Wearable

Sensors



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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.

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.

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

HBS: A Novel Biometric Feature Based on Heartbeat Morphology

Fuzzy Local Gaussian Mixture Model for Brain MR Image Segmentation

High-Grade Glioma Diffusive Modeling Using Statistical Tissue Information and

Diffusion Tensors Extracted from Atlases



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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.

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.

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.

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

In-Depth Analysis and Evaluation of DiffusiveGlioma Models

Integrated e-Health Approach Based on Vascula Ultrasound and Pulse Wave Analysis

for Asymptomatic Atherosclerosis Detection and Cardiovascular Risk Stratification in

the Community

Low-Complexity Image Processing for Real-Time Detection of Neonatal Clonic

Seizures



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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.

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.

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.

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.

Mining Data From Hemodynamic Simulations for Generating Prediction and Explanation Models

Mobile Medical Visual Information Retrieval

Multisensor Data Fusion in an Integrated Tracking System for Endoscopic Surgery



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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.

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

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

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

Noise-Assisted Correlation Algorithm for Suppressing Noise-Induced Artifacts in

Ultrasonic Nakagami Images

Numerical Characterization and Modeling of Subject-Specific Ultra wide band Body-

Centric Radio Channels and Systems for Healthcare Applications



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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.

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.

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.

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

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

Prediction of Interstitial Glucose Level

Prediction of the Timing and the Rhythm of the Parkinsonian Subthalamic Nucleus

Neural Spikes Using the Local Field Potentials



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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.

.

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.

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

Probabilistic Learning From Incomplete Data for Recognition of Activities of Daily Living

in Smart Homes

Prognosis of Right Ventricular Failure in Patients With Left Ventricular Assist Device

Based on Decision Tree With SMOTE



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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.

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.

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.

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

Real-Time Sleep Apnea Detection by Classifier Combination



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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.

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.

Robust Real-Time-Constrained Estimation of Respiratory Motion for Interventional MRI on

Mobile Organs

Secure Management of Biomedical Data With Cryptographic Hardware



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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

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.

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

Semantic Image Retrieval in Magnetic Resonance Brain Volumes

Spark Med: A Framework for Dynamic Integration of Multimedia Medical Data Into

Distributed m-Health Systems

SEMPATH Ontology: Modeling Multidisciplinary Treatment Schemes Utilizing Semantics



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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.

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

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

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

Systems

Subject-Specific Estimation of Central Aortic Blood Pressure Using an Individualized

Transfer Function: A Preliminary Feasibility Study



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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.

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

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.

Topic Mining over Asynchronous Text Sequences

Toward Semantic Interoperability of Electronic Health Records



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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.

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.

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

Ultrasound Beam forming Using Compressed Data

Video Distribution Techniques Over WiMAX Networks for m-Health Applications

Tumor Recognition in Wireless Capsule Endoscopy Images Using Textural Features and

SVM-Based Feature Selection



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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

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.

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.

Wireless Capsule Endoscopy Video Segmentation Using an Unsupervised Learning

Approach Based on Probabilistic Latent Semantic Analysis With Scale Invariant Features

Wavelet-Based Energy Features for Glaucomatous Image Classification

ieee projects 2012 2013 - bio medicine

Health & Medicine

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heart rate variability

inclusion dependency discovery

body radio channel

information technology

wireless sensor networks