IEEE Final Year Projects 2011-2012 :: Elysium Technologies Pvt Ltd::Biomedicalnew

Elysium Technologies Private Limited
ISO 9001:2008 A leading Research and Development Division
Madurai | Chennai | Trichy | Coimbatore | Kollam| Singapore
Website: elysiumtechnologies.com, elysiumtechnologies.info
Email: info@elysiumtechnologies.com

IEEE Final Year Project List 2011-2012

Abstract
BIO-MEDICAL 2011 - 2012

01 A Bayesian Hierarchical Correlation Model for fMRI Cluster Analysis

Data-driven cluster analysis is potentially suitable to search for, and discriminate between, distinct response signals in
blood oxygenation level-dependent functional magnetic resonance imaging (BOLD fMRI), which appear during
cerebrovascular disease. In contrast to model-driven methods, which test for a particular BOLD signal whose shape must be
given beforehand, datadriven methods generate a set of BOLD signals directly from the fMRI data by clustering voxels into
groups with correlated time signals. Here, we address the problem of selecting only the clusters that represent genuine
responses to the experimental stimulus by modeling the correlation structure of the clustered data using a Bayesian
hierarchical model. The model is empirically justified by demonstrating the hierarchical organization of the voxel
correlations after cluster analysis. BOLD signal discrimination is demonstrated using: 1) simulations that contain multiple
pathological BOLD response signals; and 2) fMRI data acquired during an event-related motor task. These demonstrations
are compared with results from a model-driven method based on the general linear model. Our simulations show that the
data-driven method can discriminate between the BOLD response signals, while themodeldriven method only finds one
signal. For fMRI, the data-driven method distinguishes between the BOLD signals appearing in the sensorimotor cortex and
those in basal ganglia and putamen, while themodel-drivenmethod combines these signals into one activation map.We
conclude that the proposed data-driven method provides an objective framework to identify and discriminate between
distinct BOLD response signals.

02 A Brain-Deformation Framework Based on a Linear Elastic Model and Evaluation Using Clinical Data

In image-guided neurosurgery, brain tissue displacement and deformation during neurosurgical procedures are a major
source of error. In this paper, we implement and evaluate a linear-elastic-model-based framework for correction of brain
shift using clinical data from five brain tumor patients. The framework uses a linear elastic model to simulate brain-shift
behavior. The model is driven by cortical surface deformations, which are tracked using a surface-tracking algorithm
combined with a laser-range scanner. The framework performance was evaluated using displacements of anatomical
landmarks, tumor contours and self-defined evaluation parameters. The results show that tumor deformations predicted by
the present framework agreed well with the ones observed intraoperatively, especially in the parts of the larger
deformations. On average, a brain shift of 3.9 mm and a tumor margin shift of 4.2 mm were corrected to 1.2 and 1.3 mm,
respectively. The entire correction process was performed in less than 5 min. The data from this study suggest that the
technique is a suitable candidate for intraoperative brain-deformation correction

03 A Classification Tree Approach for Cardiac Ischemia Detection Using Spatiotemporal Information from Three Standard ECG Leads

The accurate noninvasive diagnosis of cardiac ischemia remains a great challenge. To this end, the ECG is the main source
of information, and personal health systems may now embed intelligence for enabling any citizen to self-record an ECG
anywhere at any time. Our objective is to find a decision-support approach that makes best use of these resources. A new
classification tree based on conditions combinations competition (T-3C) is proposed for building a multibranch tree of
combined decision rules, and its performance is compared to usual methods based either on discriminant analysis or on

Madurai Trichy Kollam
Elysium Technologies Private Limited Elysium Technologies Private Limited Elysium Technologies Private Limited
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classification trees. Moreover, we assessed with these methods, the diagnosis content for ischemia detection of the
spatiotemporal ECG information that can be retrieved either from the standard 12-lead ECG or from only the three
orthogonal leads subset (I, II, and V2), easy to set-up in selfcare. The diagnostic accuracy of 14 decision-making strategies
was compared for ischemia detection induced by angioplasty on a test set from a study population of 90 patients. The best
performance is obtained with the T-3C algorithm on three-lead ECG, reaching 98% of sensitivity and of specificity, thus
exceeding 23% of the diagnostic accuracy of the recommended and currently used standard ECG criteria.

04 A Linear Correction for Principal Component Analysis of Dynamic Fluorescence Diffuse Optical Tomography Images

The analysis of dynamic fluorescence diffuse optical tomography (D-FDOT) is important both for drug delivery research and
for medical diagnosis and treatment. The low spatial resolution and complex kinetics, however, limit the ability of FDOT in
resolving drug distributions within small animals. Principal component analysis (PCA) provides the capability of detecting
and visualizing functional structures with different kinetic patterns from D-FDOT images. A particular challenge in using
PCA is to reduce the level of noise in D-FDOT images. This is particularly relevant in drug study, where the time-varying
fluorophore concentration (drug concentration) will result in the reconstructed images containing more noise and,
therefore, affect the performance of PCA. In this paper, a new linear corrected method is proposed for modeling these time-
varying fluorescence measurements before performing PCA. To evaluate the performance of the new method in resolving
drug biodistribution, the metabolic processes of indocyanine green within mouse is dynamically simulated and used as the
input data of PCA. Simulation results suggest that the principal component (PC) images generated using the new method
improve SNR and discrimination capability, compared to the PC images generated using the uncorrected D-FDOT images.

05 A Minimally Invasive Antenna for Microwave Ablation Therapies: Design, Performances, and Experimental Assessment

A new coaxial antenna for microwave ablation therapies is proposed. The antenna design includes a miniaturized choke and
an arrowhead cap to facilitate antenna insertion into the tissues. Antenna matching and the shape and dimension of the area
of ablated tissue (thermal lesion) obtained in ex vivo conditions are evaluated both numerically and experimentally, finding
an optimal agreement between numerical and experimental data. Results showthat the antenna is wellmatched, and that it is
able to produce a thermal lesion with an average length of 6.5 cm and an average diameter of 4.5 cm in ex vivo bovine liver
when irradiates 60 W for 10 min. Finally, the dependence of antenna performances on possible changes in the antenna’s
structure is investigated, finding an optimal stability with respect to manufacturing tolerances and highlighting the
fundamental role played by the antenna’s choke.

06 A New Measure of Movement Symmetry in Early Parkinson’s Disease Patients Using Symbolic Processing of Inertial Sensor Data

Movement asymmetry is one of the motor symptoms associated with Parkinson’s disease (PD). Therefore, being able to
detect and measure movement symmetry is important for monitoring the patient’s condition. The present paper introduces a
novel symbol based symmetry index calculated from inertial sensor data. The method is explained, evaluated, and compared
to six other symmetrymeasures. Thesemeasures were used to determine the symmetry of both upper and lower limbs during
walking of 11 early-to-mid-stage PD patients and 15 control subjects. The patients included in the study showed minimal
motor abnormalities according to the unified Parkinson’s disease rating scale (UPDRS). The symmetry indices were used to
classify subjects into two different groups corresponding to PD or control. The proposed method presented high sensitivity
and specificity with an area under the receiver operating characteristic (ROC) curve of 0.872, 9% greater than the second
best method. The proposed method also showed an excellent intraclass correlation coefficient (ICC) of 0.949, 55% greater
than the second best method. Results suggest that the proposed symmetry index is appropriate for this particular group of
patients.


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07 A Portable Image Overlay Projection Device for Computer-Aided Open Liver Surgery

Image overlay projection is a form of augmented reality that allows surgeons to view underlying anatomical structures
directly on the patient surface. It improves intuitiveness of computer-aided surgery by removing the need for sight diversion
between the patient and a display screen and has been reported to assist in 3-D understanding of anatomical structures and
the identification of target and critical structures. Challenges in the development of image overlay technologies for surgery
remain in the projection setup. Calibration, patient registration, view direction, and projection obstruction remain unsolved
limitations to image overlay techniques. In this paper, we propose a novel, portable, and handheld-navigated image overlay
device based onminiature laser projection technology that allows images of 3-D patient-specific models to be projected
directly onto the organ surface intraoperatively without the need for intrusive hardware around the surgical site. The device
can be integrated into a navigation system, thereby exploiting existing patient registration and model generation solutions.
The position of the device is tracked by the navigation system’s position sensor and used to project geometrically correct
images from any position within the workspace of the navigation system. The projector was calibrated using modified
camera calibration techniques and images for projection are rendered using a virtual camera defined by the projectors
extrinsic parameters. Verification of the device’s projection accuracy concluded a mean projection error of 1.3 mm. Visibility
testing of the projection performed on pig liver tissue found the device suitable for the display of anatomical structures on
the organ surface. The feasibility of use within the surgical workflow was assessed during open liver surgery. We show that
the device could be quickly and unobtrusively deployed within the sterile environment

08 A Radio-Frequency Coupling Network for Heating of Citrate-Coated Gold Nanoparticles for Cancer Therapy: Design and Analysis

Gold nanoparticles (GNPs) are nontoxic, can be functionalized with ligands, and preferentially accumulate in tumors. We
have developed a 13.56-MHz RF-electromagnetic field (RFEM) delivery system capable of generating high E-field strengths
required for noninvasive, noncontact heating of GNPs. The bulk heating and specific heating rates were measured as a
function of NP size and concentration. It was found that heating is both size and concentration dependent, with 5 nm
particles producing a 50.6 ± 0.2 ◦C temperature rise in 30 s for 25 µg/mL gold (125 W input). The specific heating rate was
also size and concentration dependent, with 5 nm particles producing a specific heating rate of 356 ± 78 kW/g gold at 16
µg/mL (125 W input). Furthermore, we demonstrate that cancer cells incubated with GNPs are killed when exposed to 13.56
MHz RF-EM fields. Compared to cells that were not incubated with GNPs, three out of four RF-treated groups showed a
significant enhancement of cell death with GNPs (p < 0.05). GNP-enhanced cell killing appears to require temperatures above
50 ◦C for the experimental parameters used in this study. Transmission electron micrographs showextensive
vacuolizationwith the combination of GNPs andRF treatment.

09 A Real-time Heart Rate Analysis for a Remote Millimeter Wave I–Q Sensor

This paper analyzes heart rate (HR) information from physiological tracings collected with a remote millimeter wave (mmW)
I–Q sensor for biometric monitoring applications. A parameter optimization method based on the nonlinear Levenberg–
Marquardt algorithm is used. The mmW sensor works at 94 GHz and can detect the vital signs of a human subject from a few
to tens of meters away. The reflected mmW signal is typically affected by respiration, body movement, background noise,
and electronic system noise. Processing of the mmW radar signal is, thus, necessary to obtain the true HR. The down-
converted received signal in this case consists of both the real part (I-branch) and the imaginary part (Q-branch), which can
be considered as the cosine and sine of the received phase of the HR signal. Instead of fitting the converted phase angle
signal, the method directly fits the real and imaginary parts of the HR signal, which circumvents the need for phase
unwrapping. This is particularly useful when the SNR is low. Also, the method identifies both beat-to-beat HR and individual


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heartbeat magnitude, which is valuable for some medical diagnosis applications. The mean HR here is compared to that
obtained using the discrete Fourier transform.

10 A Self-Powered Telemetry System to Estimate the Postoperative Instability of a Knee Implant

Estimating in vivo the life span of a total knee replacement prosthesis is currently done by estimating the polyethylene (PE) wear
rate from measurement of the femorotibial distance using X-ray photographies. This efficient method requires, however, waiting for
few years to obtain a readout. This letter proposes using another metric that can be obtained within a couple of months of surgery,
namely the center of pressure (COP). This metric represents the point, where the axial force applies the most onto the tibial tray.
The displacement of the COP with respect to its ideal position can be used to estimate the wear and the life span of the PE. This
requires the implant to be fitted with a telemetry system described briefly. The proposed method is supported by measures and
simulations.

11 A Web-Based System for Home Monitoring of Patients With Parkinson’s Disease Using Wearable Sensors

This letter introduces MercuryLive, a platform to enable home monitoring of patients with Parkinson’s disease (PD) using
wearable sensors.MercuryLive contains three tiers: a resourceaware data collection engine that relies upon wearable
sensors, web services for live streaming and storage of sensor data, and a web-based graphical user interface client with
video conferencing capability. Besides, the platform has the capability of analyzing sensor (i.e., accelerometer) data to
reliably estimate clinical scores capturing the severity of tremor, bradykinesia, and dyskinesia. Testing results showed an
average data latency of less than 400 ms and video latency of about 200 ms with video frame rate of about 13 frames/s when
800 kb/s of bandwidth were available and we used a 40% video compression, and data feature upload requiring 1 min of
extra time following a 10 min interactive session. These results indicate that the proposed platform is suitable to monitor
patients with PD to facilitate the titration of medications in the late stages of the disease.

12 A Web-Based System for the Quantitative and Reproducible Assessment of Clinical Indexes From the Retinal Vasculature

A novel system for the vascular tree identification and the quantitative estimation of arteriolar venular ratio clinical index in
retinal fundus images is presented. The system is composed of a module for automatic vascular tracking, an interactive
editing interface to correct errors and set the required parameters of analysis, and a module for the computation of clinical
indexes. The system was organized as a client–server structure to allow clinicians and researchers from all over the world to
work remotely. The system was evaluated by three graders analyzing 30 fundus images. The evaluation of the Pearson’s
correlation coefficient and p-value of a paired t-test for each pair of graders demonstrates the high reproducibility of the
measures provided by the system.

13 Improving Adaptive Sleep–Wake Discrimination for Wearable Devices

Sleep/wake classification systems that rely on physiological signals suffer from intersubject differences that make accurate
classification with a single, subject-independent model difficult. To overcome the limitations of intersubject variability, we
suggest a novel online adaptation technique that updates the sleep/wake classifier in real time. The objective of the present
study was to evaluate the performance of a newly developed adaptive classification algorithm that was embedded on a
wearable sleep/wake classification system called SleePic. The algorithm processed ECG and respiratory effort signals for
the classification task and applied behavioral measurements (obtained from accelerometer and press-button data) for the
automatic adaptation task.Whentrained as a subject-independent classifier algorithm, the SleePic device was only able to


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correctly classify 74.94 ± 6.76% of the humanrated sleep/wake data. By using the suggested automatic adaptation method,
the mean classification accuracy could be significantly improved to 92.98 ± 3.19%. A subject-independent classifier based on
activity data only showed a comparable accuracy of 90.44 ± 3.57%. We demonstrated that subject-independent models used
for online sleep–wake classification can successfully be adapted to previously unseen subjects without the intervention of
human experts or off-line calibration.

14 An Adaptive Kalman Filter for ECG Signal Enhancement

The ongoing trend of ECG monitoring techniques to become more ambulatory and less obtrusive generally comes at the
expense of decreased signal quality. To enhance this quality, consecutive ECG complexes can be averaged triggered on the
heartbeat, exploiting the quasi-periodicity of the ECG. However, this averaging constitutes a tradeoff between improvement
of the SNR and loss of clinically relevant physiological signal dynamics. Using a Bayesian framework, in this paper, a
sequential averaging filter is developed that, in essence, adaptively varies the number of complexes included in the
averaging based on the characteristics of the ECG signal. The filter has the form of an adaptive Kalman filter. The adaptive
estimation of the process and measurement noise covariances is performed by maximizing the Bayesian evidence function
of the sequential ECG estimation and by exploiting the spatial correlation between several simultaneously recorded ECG
signals, respectively. The noise covariance estimates thus obtained render the filter capable of ascribing more weight to
newly arriving data when these data contain morphological variability, and of reducing this weight in cases of no
morphological variability. The filter is evaluated by applying it to a variety of ECG signals. To gauge the relevance of the
adaptive noise-covariance estimation, the performance of the filter is compared to that of a Kalman filter with fixed, (a
posteriori) optimized noise covariance. This comparison demonstrates that, without using a priori knowledge on signal
characteristics, the filter with adaptive noise estimation performs similar to the filter with optimized fixed noise covariance,
favoring the adaptive filter in cases where no a priori information is available or where signal characteristics are expected to
fluctuate.

15 An Asynchronous P300 BCI with SSVEP-Based Control State Detection

In this paper, an asynchronous brain–computer interface (BCI) system combining the P300 and steady-state visually an
evoked potentials (SSVEPs) paradigm is proposed. The information transfer is accomplished using P300 event-related
potential paradigm and the control state (CS) detection is achieved using SSVEP, overlaid on the P300 base system. Offline
and online experiments have been performed with ten subjects to validate the proposed system. It is shown to achieve fast
and accurate CS detection without significantly compromising the performance. In online experiments, the system is found
to be capable of achieving an average data transfer rate of 19.05 bits/min, with CS detection accuracy of about 88%.

16 Arrhythmia Discrimination in Implantable Cardioverter Defibrillators Using SupportMachines Representation of Electrograms

Arrhythmia classification remains a major challenge for appropriate therapy delivery in implantable cardioverter
defibrillators (ICDs). The purpose of this paper is to present a new algorithm for arrhythmia discrimination based on a
statistical classification by support vector machines of a novel 2-D representation of electrograms (EGMs) named spatial
projection of tachycardia (SPOT) EGMs. SPOT-based discrimination algorithm provided sensitivity and specificity of 98.8%
and 91.3%, respectively, on a test database. A simplified version of the algorithm is also presented, which can be directly
implemented in the ICD.

17 Assessments of Alterations in the Electrical Impedance of Muscle After Experimental Nerve Injury via Finite-Element Analysis

The surface measurement of electrical impedance of muscle, incorporated as the technique of electrical


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impedancemyography (EIM), provides a noninvasive approach for evaluating neuromuscular diseases, including
amyotrophic lateral sclerosis.However, the relationship between alterations in surface impedance and the electrical
properties of muscle remains uncertain. In order to investigate this further, a group of healthy adult rats, a group of rats two
weeks postsciatic crush, and a group of animals six months postcrush underwent EIM of the gastrocnemius–soleus
complex. The animals were then killed and the conductivity and permittivity of the extracted muscle measured. Finite-
element models based on MRI data were then constructed for each group. The characteristic EIM parameter, 50 kHz phase
(±standard error), obtained with surface impedance measurements was 17.3◦ ± 0.3◦ for normal animals, 13.8◦ ± 0.7◦ for
acutely injured animals, and 16.1◦ ± 0.5◦ for chronically injured animals. The models predicted parallel changes with phase
values of 24.3◦, 18.8◦, and 21.2◦ for the normal, acute, and chronic groups, respectively. Other multifrequency impedance
parameters showed similar alterations. These results confirm that surface impedance measurements taken in conjunction
with anatomical data and finite-element models may offer a noninvasive approach for assessing biophysical alterations in
muscle in neuromuscular disease states.

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18 Automated Segmentation of Cells With IHC Membrane Staining

This study presents a fully automatedmembrane segmentation technique for immunohistochemical tissue images with
membrane staining, which is a critical task in computerized immunohistochemistry (IHC). Membrane segmentation is
particularly tricky in immunohistochemical tissue images because the cellular membranes are visible only in the stained
tracts of the cell, while the unstained tracts are not visible. Our automated method provides accurate segmentation of the
cellularmembranes in the stained tracts and reconstructs the approximate location of the unstained tracts using nuclear
membranes as a spatial reference. Accurate cell-by-cell membrane segmentation allows per cell morphological analysis and
quantification of the target membrane proteins that is fundamental in several medical applications such as cancer
characterization and classification, personalized therapy design, and for any other applications requiring cell morphology
characterization. Experimental results on real datasets from different anatomical locations demonstrate the wide
applicability and high accuracy of our approach in the context of IHC analysis.

19 Automated Segmentation of the Lumbar Pedicle in CT Images for Spinal Fusion Surgery

Exact information about the shape of a lumbar pedicle can increase operation accuracy and safety during computeraided
spinal fusion surgery, which requires extreme caution on the part of the surgeon, due to the complexity and delicacy of the
procedure. In this paper, a robust framework for segmenting the lumbar pedicle in computed tomography (CT) images is
presented. The framework that has been designed takes a CT image, which includes the lumbar pedicle as input, and
provides the segmented lumbar pedicle in the form of 3-D voxel sets. This multistep approach begins with 2-D dynamic
thresholding using local optimal thresholds, followed by procedures to recover the spine geometry in a high curvature
environment. A subsequent canal reference determination using proposed thinning-based integrated cost is then performed.
Based on the obtained segmented vertebra and canal reference, the edge of the spinal pedicle is segmented. This framework
has been tested on 84 lumbar vertebrae of 19 patients requiring spinal fusion. It was successfully applied, resulting in an
average success rate of 93.22% and a final mean error of 0.14±0.05 mm. Precision errors were smaller than 1% for spine
pedicle volumes. Intra- and interoperator precision errors were not significantly different.

20 Automatic and Unsupervised Snore Sound Extraction From Respiratory Sound Signals

In this paper, an automatic and unsupervised snore detection algorithm is proposed. The respiratory sound signals of 30
patients with different levels of airway obstruction were recorded by twomicrophones: one placed over the trachea (the
tracheal microphone), and the other was a freestanding microphone (the ambient microphone). All the recordings were done
simultaneously with full-night polysomnography during sleep. The sound activity episodes were identified using the vertical


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box (V-Box) algorithm. The 500-Hz subband energy distribution and principal component analysis were used to extract
discriminative features from sound episodes. An unsupervised fuzzy C-means clustering algorithm was then deployed to
label the sound episodes as either snore or no-snore class, which could be breath sound, swallowing sound, or any other
noise. The algorithm was evaluated using manual annotation of the sound signals. The overall accuracy of the proposed
algorithm was found to be 98.6% for tracheal sounds recordings, and 93.1% for the sounds recorded by the ambient
microphone.

21 Automatic Detection of Obstructive Sleep Apnea Using Speech Signals

Obstructive sleep apnea (OSA) is a common disorder associated with anatomical abnormalities of the upper airways that
affects 5% of the population. Acoustic parameters may be influenced by the vocal tract structure and soft tissue properties.
We hypothesize that speech signal properties of OSA patients will be different than those of control subjects not having
OSA. Using speech signal processing techniques, we explored acoustic speech features of 93 subjects who were recorded
using a text-dependent speech protocol and a digital audio recorder immediately prior to polysomnography study. Following
analysis of the study, subjects were divided into OSA (n = 67) and non-OSA (n = 26) groups. A Gaussian mixture model-
based system was developed to model and classify between the groups; discriminative features such as vocal tract length
and linear prediction coefficients were selected using feature selection technique. Specificity and sensitivity of 83% and 79%
were achieved for the male OSA and 86% and 84% for the female OSA patients, respectively. We conclude that acoustic
features from speech signals during wakefulness can detect OSA patients with good specificity and sensitivity. Such a
system can be used as a basis for future development of a tool for OSA screening.

22 Automatic Optic Disc Detection From Retinal Images by a Line Operator

Under the framework of computer-aided eye disease diagnosis, this paper presents an automatic optic disc (OD) detection
technique. The proposed technique makes use of the unique circular brightness structure associated with the OD, i.e., the
OD usually has a circular shape and is brighter than the surrounding pixels whose intensity becomes darker gradually with
their distances from the OD center. A line operator is designed to capture such circular brightness structure, which
evaluates the image brightness variation along multiple line segments of specific orientations that pass through each retinal
image pixel. The orientation of the line segment with the minimum/maximum variation has specific pattern that can be used
to locate the OD accurately. The proposed technique has been tested over four public datasets that include 130, 89, 40, and
81 images of healthy and pathological retinas, respectively. Experiments show that the designed line operator is tolerant to
different types of retinal lesion and imaging artifacts, and an average OD detection accuracy of 97.4% is obtained.

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23 Automatic Tracking of Muscle Fascicles in Ultrasound Images Using Localized Radon Transform

Ultrasound images of muscle fascicles have been widely used to investigate muscle properties under static/dynamic and
pathologic conditions. Fascicle images are usually detected and measured manually, which is subjective and time
consuming, especially when dealing with large number of images. In this study, an automatic linear extraction method based
on localized Radon transform and revoting strategy is proposed to detect and track muscle fascicles in ultrasound images.
The performance of the proposed method is compared to another automatic linear feature extraction method of revoting
Hough transform using both simulated images generated by Field II and clinical images from two human subjects. The
proposed tracking method is further validated using experimental data. Both the simulation and experimental results show
that the proposed method is robust in the presence of speckle noise, accurate in terms of orientation and position
measurement, and feasible for analyzing clinical data.


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24 Basilar-Membrane Responses to Broadband Noise Modeled Using Linear Filters With Rational Transfer Functions

Basilar-membrane responses to white Gaussian noise were recorded using laser velocimetry at basal sites of the chinchilla
cochlea with characteristic frequencies near 10 kHz and first-order Wiener kernels were computed by cross correlation of the
stimuli and the responses. The presence or absence of minimum-phase behavior was explored by fitting the kernels with
discrete linear filters with rational transfer functions. Excellent fits to the kernels were obtained with filters with transfer
functions including zeroes located outside the unit circle, implying nonminimum-phase behavior. These filters accurately
predicted basilar-membrane responses to other noise stimuli presented at the same level as the stimulus for the kernel
computation. Fits with all-pole and other minimumphase discrete filters were inferior to fits with nonminimum-phase filters.
Minimum-phase functions predicted from the amplitude functions of theWiener kernels by Hilbert transforms were different
from the measured phase curves. These results, which suggest that basilar-membrane responses do not have the minimum-
phase property, challenge the validity of models of cochlear processing, which incorporate minimum-phase behavior.

25 Bayesian Regularization Applied to Ultrasound Strain Imaging

Noise artifacts due to signal decorrelation and reverberation are a considerable problem in ultrasound strain imaging. For
block-matching methods, information from neighboring matching blocks has been utilized to regularize the estimated
displacements. We apply a recursive Bayesian regularization algorithm developed by Hayton et al. [Artif. Intell., vol. 114, pp.
125– 156, 1999] to phase-sensitive ultrasound RF signals to improve displacement estimation. The parameter of
regularization is reformulated, and its meaning examined in the context of strain imaging. Tissue-mimicking experimental
phantoms and RF data incorporating finite-element models for the tissue deformation and frequency-domain ultrasound
simulations are used to compute the optimal parameter with respect to nominal strain and algorithmic iterations. The optimal
strain regularization parameter was found to be twice the nominal strain and did not vary significantly with algorithmic
iterations. The technique demonstrates superior performance overmedian filtering in noise reduction at strains 5%and
higher for all quantitative experiments performed. For example, the strain SNR was 11 dB higher than that obtained using
amedian filter at 7% strain. It has to be noted that for applied deformations lower than 1%, since signal decorrelation errors
are minimal, using this approach may degrade the displacement image.

26 Breaking the Fixed-Arrival-Time Restriction in Reaching Movements of Neural Prosthetic Devices

We routinely generate reaching arm movements to function independently. For paralyzed users of upper extremity neural
prosthetic devices, flexible, high-performance reaching algorithms will be critical to restoring quality-of-life. Previously,
algorithms called real-time reach state equations (RSE) were developed to integrate the user’s plan and execution-related
neural activity to drive reaching movements to arbitrary targets. Preliminary validation under restricted conditions
suggested that RSE might yield dramatic performance improvements. Unfortunately, real-world applications of RSE have
been impeded because the RSE assumes a fixed, known arrival time. Recent animal-based prototypes attempted to break the
fixed-arrival-time assumption by proposing a standard model (SM) that instead restricted the user’s movements to a fixed,
known set of targets. Here, we leverage general purpose filter design (GPFD) to break both of these critical restrictions,
freeing the paralyzed user to make reaching movements to arbitrary target sets with various arrival times and definitive
stopping. In silico validation predicts that the new approach, GPFD-RSE, outperforms the SM while offering greater
flexibility. We demonstrate the GPFD-RSE against SM in the simulated control of an overactuated 3-D virtual robotic arm with
a real-time inverse kinematics engine.


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27 Changes in Body-Surface Electrocardiograms From Geometric Remodeling With Obesity

Both diabetes and obesity cause cardiac dysfunction. To separate consequences of geometric changes due to obesity from
electrophysiological ones, we investigated how changes in cardiac and torso geometry affected body-surface ECGs. For this
study, we modified the realistic heart and torso models of the simulation package ECGSIM. ECGs were calculated from
action potentials on the heart surface using our bidomain forward-problem solution. These ECGs were studied using
spectral- and principalcomponent analyses and isopotential and energy maps. We found relative errors over the body-
surface during the QT interval of 12%, 14%, and 68% for hypertrophy of the heart, extension of the abdomen, and heart
displacement with obesity, respectively. The major change to the standard 12-lead set also occurred with heart
displacement. The mean relative error over the QT interval in the precordial leads was 78% with heart displacement. These
results demonstrate the limitations of using standard lead sets to characterize electrocardiographic changes in obese
subjects and point to the need for more inclusive measures, such as body-surface mapping and inverse
electrocardiography, to describe electrical remodeling in the presence of habitus changes due to obesity

28 Classification of Paroxysmal and Persistent Atrial Fibrillation in Ambulatory ECG Recordings

The problem of classifying short atrial fibrillatory segments in ambulatory ECG recordings as being either paroxysmal or
persistent is addressed by investigating a robust approach to signal characterization. The method comprises preprocessing
estimation of the dominant atrial frequency for the purpose of controlling the subbands of a filter bank, computation of the
relative subband (harmonics) energy, and the subband sample entropy. Using minimum-error-rate classification of different
feature vectors, a data set consisting of 24-h ambulatory recordings from 50 subjects with either paroxysmal (26) or
persistent (24) atrial fibrillation (AF) was analyzed on a 10-s segment basis; a total of 212,196 segments were classified. The
best performance in terms of area under the receiver operating characteristic curve was obtained for a feature vector defined
by the subband sample entropy of the dominant atrial frequency and the relative harmonics energy, resulting in a value of
0.923, whereas that of the dominant atrial frequency was equal to 0.826. It is concluded that paroxysmal and persistent AFs
can be discriminated from short segments with good accuracy at any time of an ambulatory recording.

29 Comparison of RootMUSIC and Discrete Wavelet Transform Analysis of Doppler Ultrasound Blood Flow Waveforms I Diabetes

The earliest signs of cardiovascular disease occur in microcirculations. Changes to mechanical and structural properties of
these small resistive vessels alter the impedance to flow, subsequent reflected waves, and consequently, flow waveform
morphology. In this paper, we compare two frequency analysis techniques: 1) rootMUSIC and 2) the discretewavelet
transform (DWT) to extract features of flow velocity waveform morphology captured using Doppler ultrasound from the
ophthalmic artery (OA) in 30 controls and 38 age and sex matched Type I diabetics. Conventional techniques for
characterizing Doppler velocity waveforms, such as mean velocity, resistive index, and pulsatility index, revealed no
significant differences between the groups. However, rootMUSIC and the DWT provided highly correlated results with the
spectral content in bands 2–7 (30–0.8 Hz) significantly elevated in the diabetic group (p< 0.05). The spectral distinction
between the groups may be attributable to manifestations of underlying pathophysiological processes in vascular
impedance and consequent wave reflections, with bands 5 and 7 related to age. Spectral descriptors of OA blood velocity
waveforms are better indicators of preclinical microvascular abnormalities in Type I diabetes than conventional measures.
Although highly correlated DWT proved slightly more discriminatory than rootMUSIC and has the advantage of extending to
subheart rate frequencies, which may be of interest.


9



30 Continuous Intra-Arterial Blood pH Monitoring by a Fiber-Optic Fluorosensor

In the Continuous intra-arterial blood pH monitoring is highly desirable in clinical practice. However, devices with
appreciable accuracy are still not commercially available to date. In this study, we present a fiber-optic fluorosensor that can
be used to continuously and accurately measure blood pH changes. The pH sensor is developed based on a proton-
sensitive fluorescence dye, N-allyl-4-(4_-methyl-piperazinyl)-1,8-naphthalimide, which is bonded covalently to an optical fiber
through heat polymerization. Fluorescence intensity was recorded after the sensor was exposed to different pH buffer
solutions or intra-arterial blood in rabbits. Fluorescence intensity with emission peak at 510 nm decreased immediately as
the blood pH increased. Linear and reproducible responses were observed when pH ranges from 6.8 to 8.0 with resolution of
0.03 pH units. The correlation coefficient between the pH sensor and the conventional blood gas analyzer was 0.93 in vivo (n
= 75, p < 0.001) with a bias and precision of −0.02 ± 0.08 pH units. The pH sensor was stable during measurement for at least
72 h. The pH sensor is not sensitive to fluctuations of various ions’ concentrations and plasma osmosis at
pathophysiological limits, suggesting that it is useful for the continuous measurement of blood pH at various clinical
settings.

.
31 Continuous Intra-Arterial Blood pH Monitoring by a Fiber-Optic Fluorosensor

Continuous intra-arterial blood pH monitoring is highly desirable in clinical practice. However, devices with appreciable
accuracy are still not commercially available to date. In this study, we present a fiber-optic fluorosensor that can be used to
continuously and accurately measure blood pH changes. The pH sensor is developed based on a proton-sensitive
fluorescence dye, N-allyl-4-(4_-methyl-piperazinyl)-1,8-naphthalimide, which is bonded covalently to an optical fiber through
heat polymerization. Fluorescence intensity was recorded after the sensor was exposed to different pH buffer solutions or
intra-arterial blood in rabbits. Fluorescence intensity with emission peak at 510 nm decreased immediately as the blood pH
increased. Linear and reproducible responses were observed when pH ranges from 6.8 to 8.0 with resolution of 0.03 pH
units. The correlation coefficient between the pH sensor and the conventional blood gas analyzer was 0.93 in vivo (n = 75, p
< 0.001) with a bias and precision of −0.02 ± 0.08 pH units. The pH sensor was stable during measurement for at least 72 h.
The pH sensor is not sensitive to fluctuations of various ions’ concentrations and plasma osmosis at pathophysiological
limits, suggesting that it is useful for the continuous measurement of blood pH at various clinical settings.

32 Control of Action Potential Duration Alternans in Canine Cardiac Ventricular Tissue

Cardiac electrical alternans, characterized by a beatto- beat alternation in action potential waveform, is a naturally occurring
phenomenon, which can occur at sufficiently fast pacing rates. Its presence has been putatively linked to the onset of
cardiac reentry, which is a precursor to ventricular fibrillation. Previous studies have shown that closed-loop alternans
control techniques that apply a succession of externally administered cycle perturbations at a single site provide limited
spatially-extended alternans elimination in sufficiently large cardiac substrates. However, detailed experimental
investigations into the spatial dynamics of alternans control have been restricted to Purkinje fiber studies. A complete
understanding of alternans control in the more clinically relevant ventricular tissue is needed. In this paper, we study the
spatial dynamics of alternans and alternans control in arterially perfused canine right ventricular preparations using an
optical mapping system capable of high-resolution fluorescence imaging. Specifically, we quantify the spatial efficacy of
alternans control along 2.5 cm of tissue, focusing on differences in spatial control between different subregions of tissue.
We demonstrate effective control of spatially-extended alternans up to 2.0 cm, with control efficacy attenuating as a function
of distance. Our results provide a basis for future investigations into electrode-based control interventions of alternans in
cardiac tissue.


10



33 Depth of Anesthesia During Multidrug Infusion: Separating the Effects of Propofol and Remifentanil Using the Spectral Features of EEG

General anesthesia is usually induced with a combination of drugs. In addition to the hypnotic agent, such as propofol,
opioids are often used due to their synergistic hypnotic and analgesic properties. However, the effects of opioids on the EEG
changes and the clinical state of the patient during anesthesia are complex and hinder the interpretation of the EEG-based
depth of anesthesia indexes. In this paper, a novel technology for separating the anesthetic effects of propofol and an
ultrashort-acting opioid, remifentanil, using the spectral features of EEG is proposed. By applying a floating search method,
a well-performing feature set is achieved to estimate the effects of propofol during induction of anesthesia and to classify
whether or not remifentanil has been coadministered. It is shown that including the detection of the presence of opioids to
the estimated effect of propofol significantly improves the determination of the clinical state of the patient, i.e., if the patient
will respond to a painful stimulation.

34 Detection of Viruses Via Statistical Gene Expression Analysis

We develop a new Bayesian construction of the elastic net (ENet), with variational Bayesian analysis. This modeling
framework is motivated by analysis of gene expression data for viruses, with a focus on H3N2 and H1N1 influenza, as well as
Rhino virus and RSV (respiratory syncytial virus). Our objective is to understand the biological pathways responsible for the
host response to such viruses, with the ultimate objective of developing a clinical test to distinguish subjects infected by
such viruses from subjects with other symptom causes (e.g., bacteria). In addition to analyzing these new datasets, we
provide a detailed analysis of the Bayesian ENet and compare it to related models.
.

35 Development of a Flexible System for Measuring Muscle Area Using Ultrasonography

Muscular strength can be estimated by quantification of muscle area. For this purpose, we developed a flexible measuring
system for muscle area using ultrasonography. This method is completely safe and is particularly suitable for elderly people
because the subjects are not required to perform any muscular contraction during measurement. The ultrasound probe is
installed on a mechanical arm, and continuously scans fragmental images along the body surface.Awide-area cross-
sectional image is then constructed using the measured images. The link mechanism is very flexible, enabling the operator
to measure images for any body posture and body site. Use of the spatial compounding method reduces speckle and artifact
noise in the resultant cross-sectional images. The operator can observe individual muscles (extensor, flexor muscle, etc.) in
detail. We conducted experiments to evaluate the performance of the system. In the experiments, the position of the
ultrasound probe was calculated with high accuracy according to the link posture. In addition, a high degree of correlation
was verified between MR images and those of the developed system. We observed a reduction in noise due to use of the
spatial compounding method, and propose a new calibration method for correcting the measured muscle area, which were
slightly deformed by the contact pressure of the ultrasound probe. Finally, we examined the relation between muscular area
and muscular strength in young and middle-aged subjects. The results of these experiments confirm that the developed
system can estimate muscular strength based on muscular area.

36 Directed Differential Connectivity Graph of Interictal Epileptiform Discharges

In this paper, we study temporal couplings between interictal events of spatially remote regions in order to localize the
leading epileptic regions from intracerebral EEG (iEEG). We aim to assess whether quantitative epileptic graph analysis
during interictal period may be helpful to predict the seizure onset zone of ictal iEEG. Using wavelet transform, cross-


11



correlation coefficient, and multiple hypothesis test, we propose a differential connectivity graph (DCG) to represent the
connections that change significantly between epileptic and nonepileptic states as defined by the interictal events.
Postprocessings based on mutual information and multiobjective optimization are proposed to localize the leading epileptic
regions through DCG. The suggested approach is applied on iEEG recordings of five patients suffering from focal epilepsy.
Quantitative comparisons of the proposed epileptic regions within ictal onset zones detected by visual inspection and using
electrically stimulated seizures, reveal good performance of the present method.

37 Driver Drowsiness Classification Using Fuzzy Wavelet-Packet-Based Feature-Extraction Algorithm

Driver drowsiness and loss of vigilance are a major cause of road accidents. Monitoring physiological signals while driving
provides the possibility of detecting and warning of drowsiness and fatigue. The aim of this paper is to maximize the amount
of drowsiness-related information extracted from a set of electroencephalogram (EEG), electrooculogram (EOG), and
electrocardiogram (ECG) signals during a simulation driving test. Specifically, we develop an efficient fuzzy mutual-
information (MI)- based wavelet packet transform (FMIWPT) feature-extraction method for classifying the driver drowsiness
state into one of predefined drowsiness levels. The proposed method estimates the requiredMI using a novel approach
based on fuzzy memberships providing an accurate-information content-estimation measure. The quality of the extracted
features was assessed on datasets collected from 31 drivers on a simulation test. The experimental results proved the
significance of FMIWPT in extracting features that highly correlate with the different drowsiness levels achieving a
classification accuracy of 95%–97% on an average across all subjects.

.
38 Duodenum Identification Mechanism for Capsule Endoscopy

The aim of this study is to implement a duodenum identificationmechanism for capsule endoscopes because commercially
available capsule endoscopes sometimes present a false negative diagnosis of the duodenum. One reason for the false
negative diagnosis is that the duodenum is the fastest moving part within the gastrointestinal tract and the current frame
rate of the capsule is not fast enough. When the capsule can automatically identify that it is in the duodenum, the frame rate
of the capsule can be temporarily increased to reduce the possibility of a false negative diagnosis. This study proposes a
mechanism to identify the duodenum using capacitive proximity sensors that can distinguish the surrounding tissue and
transmit data using RF communication. The implemented capsule (D11 mm × L22 mm) was smaller than the commercially
available capsule endoscopes, and power consumption was as low as 0.642 mW. Preexperiments were conducted to select
an appropriate electrode width in order to increase the signal-to-noise ratio (SNR), and in vitro experiments were conducted
to verify whether the implemented capsule could identify the duodenum within 3 s. The experiment showed that the
identification rate of duodenum was 93% when the velocity of the capsule was less than 1 cm/s.

39 Dynamic Brain Phantom for Intracranial Volume Measurements

Knowledge of intracranial ventricular volume is important for the treatment of hydrocephalus, a disease in which
cerebrospinal fluid (CSF) accumulates in the brain. Current monitoring options involve MRI or pressure monitors (InSite,
Medtronic). However, there are no existing methods for continuous cerebral ventricle volume measurements. In order to test
a novel impedance sensor for direct ventricular volume measurements, we present a model that emulates the expansion of
the lateral ventricles seen in hydrocephalus. To quantify the ventricular volume, sensor prototypes were fabricated and
tested with this experimental model. Fluidwas injected andwithdrawn cyclically in a controlledmanner and volume
measurements were tracked over 8 h. Pressure measurements were also comparable to conditions seen clinically. The
results from the bench-top model served to calibrate the sensor for preliminary animal experiments. A hydrocephalic rat
model was used to validate a scaled-down, microfabricated prototype sensor. CSF was removed from the enlarged ventricles
and a dynamic volume decrease was properly recorded. This method of testing new designs on brain phantoms prior to
animal experimentation accelerates medical device design by determining sensor specifications and optimization in a


12



rational process.

40 Evaluation of Scaffolds based on α-Tricalcium Phosphate Cements for Tissue Engineering Applications

Growth of cells in 3-D porous scaffolds has gained importance in the field of tissue engineering. The scaffolds guide cellular
growth, synthesize extracellular matrix and other biological molecules, and make the formation of tissues and functional
organs easier. The aim of this study is to use α-tricalcium phosphate cement in order to obtain new types of scaffolds with
the aid of paraffin spheres as pore generators. The porosity of the scaffolds produced with paraffin spheres was analyzed
and compared to the literature, and the study of scaffold permeability using the Forchheimer equation allowed the analysis
of pore interconnectivity. In vitro tests showed the behavior of scaffolds in solutions of simulated body fluid, and viability
and cell proliferation were also evaluated. The results show the potential use of the materials developed for scaffolds for use
in tissue engineering applications.

41 Fast Technique for Noninvasive Fetal ECG Extraction

This letter describes a fast and very simple algorithm for estimating the fetal electrocardiogram (FECG). It is based on
independent component analysis, but we substitute its computationally demanding calculations for a much simpler
procedure. The resulting method consists of two steps: 1) a dimensionality reduction step and 2) a computationally light
postprocessing stage used to enhance the FECG signal.

42 FEM-Based 3-D Tumor Growth Prediction for Kidney Tumor

It is important to predict the tumor growth so that appropriate treatment can be planned in the early stage. In this letter, we
propose a finite-element method (FEM)-based 3-D tumor growth prediction system using longitudinal kidney tumor images.
To the best of our knowledge, this is the first kidney tumor growth prediction system. The kidney tissues are classified into
three types: renal cortex, renal medulla, and renal pelvis. The reaction–diffusion model is applied as the tumor growth
model. Different diffusion properties are considered in the model: the diffusion for renal medulla is considered as
anisotropic, while those of renal cortex and renal pelvis are considered as isotropic. The FEM is employed to solve the
diffusion model. Themodel parameters are estimated by the optimization of an objective function of overlap accuracy using a
hybrid optimization parallel search package. The proposed method was tested on two longitudinal studies with seven time
points on five tumors. The average true positive volume fraction and false positive volume fraction on all tumors is 91.4%
and 4.0%, respectively. The experimental results showed the feasibility and efficacy of the proposed method.

43 Finite-Element-Based Discretization and Regularization Strategies for 3-D Inverse Electrocardiography

We consider the inverse electrocardiographic problem of computing epicardial potentials from a body-surface potential map.
We study how to improve numerical approximation of the inverse problem when the finite-element method is used. Being ill-
posed, the inverse problem requires different discretization strategies from its corresponding forward problem. We propose
refinement guidelines that specifically address the ill-posedness of the problem. The resulting guidelines necessitate the use
of hybrid finite elements composed of tetrahedra and prism elements. Also, in order to maintain consistent numerical quality
when the inverse problem is discretized into different scales, we propose a new family of regularizers using the variational
principle underlying finiteelement methods. These variational-formed regularizers serve as an alternative to the traditional
Tikhonov regularizers, but preserves the L2 norm and thereby achieves consistent regularization in multiscale simulations.
The variational formulation also enables a simple construction of the discrete gradient operator over irregularmeshes, which
is difficult to define in traditional discretization schemes.We validated our hybrid element technique and the variational


13



regularizers by simulations on a realistic 3-D torso/heart model with empirical heart data. Results show that discretization
based on our proposed strategies mitigates the ill-conditioning and improves the inverse solution, and that the variational
formulation may benefit a broader range of potential-based bioelectric problems.

44 Hilbert–Huang-Based Tremor Removal to Assess Postural Properties From Accelerometers

Tremor is one of the symptoms of several disorders of the central and peripheral nervous system, such as Parkinson’s
disease (PD). The impairment of postural control is another symptom of PD. The conventional method of posture analysis
uses force plates, but accelerometers can be a valid and reliable alternative. Both these measurement techniques are
sensitive to tremor.Tremor affects postural measures and may thus lead to misleading results or interpretations. Linear low-
pass filters (LPFs) are commonly employed for tremor removal. In this study, an alternative method, based on Hilbert–Huang
transformation (HHT), is proposed. We examined 20 PD subjects, with and without tremor, and 20 control subjects. We
compared the effectiveness of LPF and HHT-based filtering on a set of postural parameters extracted from acceleration
signals. HHT has the advantage of providing a filter, which with no a priori knowledge, efficiently manages the nonlinear,
nonstationary interference due to tremor, and beyond tremor, gives descriptive measures of postural function. Some of the
differences found using LPF can instead be ascribed to inefficient noise/tremor suppression. Filter order and cutoff
frequency are indeed critical when subjects exhibit a tremorous behavior, in which case LPF parameters should be chosen
very carefully.

45 Identification and Control for Automated Regulation of Hemodynamic Variables During Hemodialysis

This paper proposes a novel model-based control methodology for a computer-controlled hemodialysis system, designed to
maintain the hemodynamic stability of end-stage renal failure patients undergoing fluid removal during hemodialysis. The
first objective of this paper is to introduce a linear parameter varying system to model the hemodynamic response of
patients during hemodialysis. Ultrafiltration rate (UFR) and dialysate sodium concentration (DSC) are imposed as the inputs,
and the model computes the relative blood volume (RBV), percentage change in heart rate (∆HR), and systolic blood
pressure (SBP) during the course of hemodialysis. The model parameters were estimated based on data collected from 12
patients undergoing 4 profiled hemodialysis sessions. The modeling results demonstrated that the proposed model could be
useful for estimating the individual patient’s hemodynamic behavior during hemodialysis. Based on the model, the second
objective is to implement a computer-controlled hemodialysis system for the regulation of RBV and HR during hemodialysis
while maintaining SBP within stable range. The proposed controller is based on a model predictive control approach utilizing
pre-defined constraints on the control inputs (UFR and DSC) as well as the output (SBP). The designed control system was
experimentally verified on four patients. The results demonstrated that the proposed computer-controlled hemodialysis
system regulated the RBV and HR of the patients according to individual reference profiles with an average mean square
error of 0.24% and 2.6%, respectively, and thus can be potentially useful for ensuring the stability of patients undergoing
hemodialysis by avoiding sudden changes in hemodynamic variables.

46 Improved Pressure–Frequency Sensing Subxiphoid Pericardial Access System: Performance Characteristics During In Vivo Testing

We have designed, synthesized, and tested an improved version of our original subxiphoid access system intended to
facilitate epicardial electrophysiology. The new version of the system incorporates a precision fiber-optic pressure sensor
and a novel signal analysis algorithm for identifying pressure–frequency signatures which, in the clinical setting, may allow
for safer access to the pericardial space. Following in vivo studies on ten adult canine models, we analyzed 215 pressure–
frequency measurements made at the distal tip of the access needle, of which 98 were from nonpericardial, 112 were from
pericardial, and five were from ventricular locations. The needle locations as identified by the algorithm were significantly
different from each other (p < 0.01), and the algorithm had improved performance when compared to a standard fast Fourier
transform (FFT) analysis of the same data. Moreover, the structure of the algorithm can potentially overcome the time lags


14



intrinsic to FFT analysis such that the needle’s location can be determined in nearreal time. Hydrodynamic pressure–
frequencymeasurementsmade during traversal of the pericardial membrane revealed a distinct change in signal structure
between the pericardial and nonpericardial anatomy. We present and discuss the design principles, details of construction,
and performance characteristics of this system.

47 Incremental Fuzzy Mining of Gene Expression Data for Gene Function Prediction

Due to the complexity of the underlying biological processes, gene expression data obtained from DNA microarray
technologies are typically noisy and have very high dimensionality and these make the mining of such data for gene function
prediction very difficult. To tackle these difficulties, we propose to use an incremental fuzzy mining technique called
incremental fuzzy mining (IFM). By transforming quantitative expression values into linguistic terms, such as highly or lowly
expressed, IFM can effectively capture heterogeneity in expression data for pattern discovery. It does so using a fuzzy
measure to determine if interesting association patterns exist between the linguistic gene expression levels. Based on these
patterns, IFM can make accurate gene function predictions and these predictions can be made in such a way that each gene
can be allowed to belong to more than one functional class with different degrees of membership. Gene function prediction
problem can be formulated both as classification and clustering problems, and IFM can be used either as a classification
technique or together with existing clustering algorithms to improve the cluster groupings discovered for greater prediction
accuracies. IFM is characterized also by its being an incremental data mining technique so that the discovered patterns can
be continually refined based only on newly collected data without the need for retraining using the whole dataset. For
performance evaluation, IFM has been tested with real expression datasets for both classification and clustering tasks.
Experimental results show that it can effectively uncover hidden patterns for accurate gene function predictions.

48 Intervention in Biological Phenomena Modeled by S-Systems

Recent years have witnessed extensive research activity in modeling biological phenomena as well as in developing
intervention strategies for such phenomena. S-systems, which offer a good compromise between accuracy and
mathematical flexibility, are a promising framework for modeling the dynamical behavior of biological phenomena. In this
paper, two different intervention strategies, namely direct and indirect, are proposed for the S-system model. In the indirect
approach, the prespecified desired values for the target variables are used to compute the reference values for the control
inputs, and two control algorithms, namely simple sampled-data control and model predictive control (MPC), are developed
for transferring the control variables from their initial values to the computed reference ones. In the direct approach, a MPC
algorithm is developed that directly guides the target variables to their desired values. The proposed intervention strategies
are applied to the glycolytic–glycogenolytic pathway and the simulation results presented demonstrate the effectiveness of
the proposed schemes.

49 Model-Based Human Circadian Phase Estimation Using a Particle Filter

We present a method for tracking an individual’s circadian phase that integrates dynamic models of circadian physiology
with physiological measurements in a Bayesian statistical framework. A model of the circadian pacemaker’s response to
light exposure is transformed into a nonlinear state-space model with a circadian phase state. The probability distribution of
the circadian phase is estimated by a particle filter that predicts changes over time based on themodel, and performs
updates with information gained fromphysiologicalmeasurements. Simulations demonstrate how probability distributions
allow flexible initialization of model states and enable statistical quantification of entrainment and divergence properties of
the circadian pacemaker. The combined use of sleep–wake scheduling data and physiological measurements is
demonstrated in a case study highlighting advantages for addressing the challenge of noninvasive ambulatory monitoring of
circadian physiology.


15



50 Modeling and Percept of Transcorneal Electrical Stimulation in Humans

Retinal activation via transcorneal electrical stimulation (TcES) in normal humans was investigated by comparing subject
perception, model predictions, and brain activation patterns. The preferential location of retinal stimulation was predicted
from3-D admittancemodeling. Visual cortex activation was measured using positron emission tomography (PET) and 18
Ffluorodeoxyglucose (FDG). Two different corneal electrodes were investigated: DTL-Plus and ERG-Jet. Modeling results
predicted preferential stimulation of the peripheral, inferior, nasal retina during right eye TcES using DTL-Plus, but more
extensive activation of peripheral, nasal hemiretina using ERG-Jet. The results from human FDG PET study using both
corneal electrodes showed areas of visual cortex activation that consistently corresponded with the reported phosphene
percept and modeling predictions. ERG-Jet was able to generate brighter phosphene percept than DTL-Plus and elicited
retinotopically mapped primary visual cortex activation. This study demonstrates that admittance modeling and PET imaging
consistently predict the perceived location of electrically elicited phosphenes produced during TcES.

51 Modeling Conserved Structure Patterns for Functional Noncoding RNA

This RNA regulation has been increasingly recognized as a potential and perhaps overlooked genetics of higher
organisms. Noncoding RNAs (ncRNA) may play various catalytic and regulatory roles in the genetic operating system.
Recent studies using comparative genomics andmolecular genetics show evidence of the presence of varied ncRNAs.
Unlike protein coding genes, there is a lack of comparable information or outstanding signal for ncRNAs. Traditional
computational linguistics show limitations in modeling complicated secondary structures and prevent us from
identifying structure–function relationships of ncRNAs. This paper presents a novel approach, based on a set of
distance constraints, to model the predicted RNA secondary structures. Further, a filtering schema is presented to
identify matched models for the queried secondary structures.

52 Modeling Study of the Light Stimulation of a Neuron Cell With Channelrhodopsin-2 Mutants

Channelrhodopsin-2 (ChR2) has become a widely used tool for stimulating neurons with light. Nevertheless, the underlying
dynamics of the ChR2-evoked spikes are still not yet fully understood. Here, we develop a model that describes the response
of ChR2-expressing neurons to light stimuli and use the model to explore the light-to-spike process. We show that an
optimal stimulation yield is achieved when the optical energies are delivered in short pulses. The model allows us to
theoretically examine the effects of using various types of ChR2 mutants. We show that while increasing the lifetime and
shuttering speed of ChR2 have limited effect, reducing the threshold irradiance by increased conductance will eliminate
adaptation and allow constant dynamic range. The model and the conclusion presented in this study can help to interpret
experimental results, design illumination protocols, and seek improvement strategies in the nascent ontogenetic field..

53 Modified Kinematic Technique for Measuring Pathological Hyperextension and Hypermobility of the Interphalangeal Joints

Dynamic finger joint motion is difficult to measure using optical motion analysis techniques due to the limited surface
area allowed for adequate marker placement. This paper describes an extension of a previously validated kinematic
measurement technique using a reduced surface marker set and outlines the required calculations based on a specific
surface marker placement to calculate flexion/extension and hyperextension of the metacarpophalangeal, proximal
interphalangeal, and distal interphalangeal joints. The modified technique has been assessed for accuracy using a
series of static reference frames (absolute residual error = ±3.7◦, cross correlation between new method and reference


16

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