Conf Proc IEEE Eng Med Biol Soc
· 2006 · PMID 17947183
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The aim of this work is to implement a feedback sensing and control mechanism via wireless sensor nodes for medical sensing and actuation. To that end, we employ the Zigbee protocol in sensing and precisely commanding an...The aim of this work is to implement a feedback sensing and control mechanism via wireless sensor nodes for medical sensing and actuation. To that end, we employ the Zigbee protocol in sensing and precisely commanding an analog signal to a probe. We employ a user application upon the layer available in the Zigbee stack to achieve our goal. Signal transmission delays, packet losses, and energy consumption are major challenges, we present strategies to minimize or solve these challenges. We also introduce strategies to enable sensor nodes to acquire and command (electrical current) signals from/to sensing/actuating platforms. Finally, we implement algorithms allowing the sensor node to compute and to regulate command signal on line.
Conf Proc IEEE Eng Med Biol Soc
· 2006 · PMID 17947182
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In this paper we provide a quantitative electroencephalogram (EEG) analysis to study the effect of hypothermia on the neurological recovery of brain after cardiac arrest. We hypothesize that the brain injury results in a...In this paper we provide a quantitative electroencephalogram (EEG) analysis to study the effect of hypothermia on the neurological recovery of brain after cardiac arrest. We hypothesize that the brain injury results in a reduction in information of the brain rhythm. To measure the information content of the EEG a new measure called information quantity (IQ), which is the Shannon entropy of decorrelated EEG signals, is developed. For decorrelating EEG signals, we use the discrete wavelet transform (DWT) which is known to have good decorrelating properties and to show a good match to the standard clinical bands in EEG. In simulation for measuring the amount of information, the IQ shows better tracking capability for dynamic amplitude change and frequency component change than conventional entropy-based measures. Experiments are carried out in rodents to monitor the neurological recovery after cardiac arrest. In addition, EEG signal recovery under normothermic (37 degrees C) and hypothermic (33 degrees C) resuscitation following 5, 7 and 9 minutes of cardiac arrest is recorded and analyzed. Experimental results show that the IQ is higher for hypothermic than normothermic rats. The results quantitatively support the hypothesis that hypothermia accelerates the recovery of brain injury after cardiac arrest.
Conf Proc IEEE Eng Med Biol Soc
· 2006 · PMID 17947181
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In disorders such as sleep apnea, sleep is fragmented with frequent EEG-arousal (EEGA) as determined via changes in the sleep-electroencephalogram. EEGA is a poorly understood, complicated phenomenon which is critically...In disorders such as sleep apnea, sleep is fragmented with frequent EEG-arousal (EEGA) as determined via changes in the sleep-electroencephalogram. EEGA is a poorly understood, complicated phenomenon which is critically important in studying the mysteries of sleep. In this paper we study the information flow between the left and right hemispheres of the brain during the EEGA as manifested through inter-hemispheric asynchrony (IHA) of the surface EEG. EEG data (using electrodes A1/C4 and A2/C3 of international 10-20 system) was collected from 5 subjects undergoing routine polysomnography (PSG). Spectral correlation coefficient (R) was computed between EEG data from two hemispheres for delta-delta(0.5-4 Hz), theta-theta(4.1-8 Hz), alpha-alpha(8.1-12 Hz) & beta-beta(12.1-25 Hz) frequency bands, during EEGA events. EEGA were graded in 3 levels as (i) micro arousals (3-6 s), (ii) short arousals (6.1-10 s), & (iii) long arousals (10.1-15 s). Our results revealed that in beta band, IHA increases above the baseline after the onset of EEGA and returns to the baseline after the conclusion of event. Results indicated that the duration of EEGA events has a direct influence on the onset of IHA. The latency (L) between the onset of arousals and IHA were found to be L=2 +/- 0.5 s (for micro arousals), 4 +/- 2.2 s (short arousals) and 6.5 +/- 3.6 s (long arousals).
Conf Proc IEEE Eng Med Biol Soc
· 2006 · PMID 17947180
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In the paper, EEGs are used to perform a multi facets analysis of the driver status. The EEG tracks, taken by means of electrodes installed in a basket dressed by the driver, are processed by a fuzzy model consisting of...In the paper, EEGs are used to perform a multi facets analysis of the driver status. The EEG tracks, taken by means of electrodes installed in a basket dressed by the driver, are processed by a fuzzy model consisting of rules able to predict possible temporary driver attention deficit due to stress or disease conditions. The driving behavior is evaluated in real time by a hardware fuzzy processing. The possibility of taking into account different facets of the driver status is claimed to give rise to a driver control system with good safety and predictive features.
Bamberg SM, Lastayo P, Dibble L
… +2 more, Musselman J, Raghavendra SK
Conf Proc IEEE Eng Med Biol Soc
· 2006 · PMID 17947179
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This work presents the first phase in the development of an in-shoe sensor system designed to evaluate balance. Sixteen force-sensitive resistors were strategically mounted to a removable insole, and the bilateral output...This work presents the first phase in the development of an in-shoe sensor system designed to evaluate balance. Sixteen force-sensitive resistors were strategically mounted to a removable insole, and the bilateral outputs were recorded. The initial results indicate that these sensors are capable of detecting subtle changes in weight distribution, corresponding to the subject's ability to balance. Preliminary analysis of this data found a clear correlation between the ability to balance and the state of health of the subject.
Barbosa TM, Sene IG, da Rocha AF
… +3 more, Nascimento FA, Carvalho HS, Camapum JF
Conf Proc IEEE Eng Med Biol Soc
· 2006 · PMID 17947178
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This work presents a new programming model for sensor networks embedded in the human body which is based on the concept of multi-programming application-oriented software. This model was conceived with a top-down approac...This work presents a new programming model for sensor networks embedded in the human body which is based on the concept of multi-programming application-oriented software. This model was conceived with a top-down approach of four layers and its main goal is to allow the healthcare professionals to program and to reconfigure the network locally or by the Internet. In order to evaluate this hypothesis, a benchmarking was executed in order to allow the assessment of the mean time spent in the programming of a multi-functional sensor node used for the measurement and transmission of the electrocardiogram.
Conf Proc IEEE Eng Med Biol Soc
· 2006 · PMID 17947177
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In this paper, we investigated the validity of the WHAM (wearable heart activity monitor) in the clinical applications, which has been implemented as a wearable ambulatory device for continuously and long-term monitoring...In this paper, we investigated the validity of the WHAM (wearable heart activity monitor) in the clinical applications, which has been implemented as a wearable ambulatory device for continuously and long-term monitoring user's cardiac conditions. To this end, using the WHAM and the conventional Holter monitor the ECG signals over 24 hours were recorded during daily activities. The signal from the WHAM was compared with that from the conventional Holter monitor in terms of the readability of the signal, the quality of the signal, and the accuracy of arrhythmia detection. The performance of the WHAM was a little lower as compared with the conventional Holter monitor, although showing no significant difference (the readability of the signal: 97.2% vs 99.3%; the quality of the signal: 0.97 vs 0.98; the accuracy of arrhythmia detection: 96.2% vs 98.1%). From these results, it is likely that the WHAM shows the performance enough to be used in the clinical application as a wearable ambulatory monitoring device.
Vuillerme N, Chenu O, Fleury A
… +2 more, Demongeot J, Payan Y
Conf Proc IEEE Eng Med Biol Soc
· 2006 · PMID 17947176
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The performance of an artificial tongue-placed tactile biofeedback device for improving ankle joint position sense was assessed in 12 young healthy adults using an active matching task. The underlying principle of this s...The performance of an artificial tongue-placed tactile biofeedback device for improving ankle joint position sense was assessed in 12 young healthy adults using an active matching task. The underlying principle of this system consisted of supplying individuals with supplementary information about the position of the matching ankle relative to the reference ankle position through a tongue-placed tactile output device generating electrotactile stimulation on a 36- point (6 x 6) matrix held against the surface of the tongue dorsum. Precisely, (1) no electrodes were activated when both ankles were in a similar angular position within predetermined "angular dead zone" (ADZ); (2) 12 electrodes (2 x 6) of the anterior and posterior zones of the matrix were activated (corresponding to the stimulation of the front and rear portion of the tongue) when the matching ankle was in too plantar and dorsiflexed position relative to the reference ankle, respectively. The effects of two ADZ values of 0.5 degrees and 1.5 degrees were evaluated. Results showed (1) more accurate and more consistent matching performances with than without biofeedback and (2) more accurate and more consistent ankle joint matching performances when using the biofeedback device with the smaller ADZ value. These findings suggest that (1) electrotactile stimulation of the tongue can be used to improve ankle joint proprioception and (2) this improvement can be increased through an appropriate specification of the ADZ parameter provided by the biofeedback system. Further investigations are needed to strengthen the potential clinical value of this device.
Conf Proc IEEE Eng Med Biol Soc
· 2006 · PMID 17947175
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Estimation of parallel conductance using the impedance electrode technique is usually done assuming isotropic conditions. This may not be the best solution since the myocardium is an anisotropic material. This paper expo...Estimation of parallel conductance using the impedance electrode technique is usually done assuming isotropic conditions. This may not be the best solution since the myocardium is an anisotropic material. This paper exposes the effect of fiber orientation for volume measurement using a conductor model with asymmetrical source electrodes. Simulation results show calculated volumes between surrounding materials with and without myocardial fiber orientation included in the model. We plan to extend these study results to the real heart for developing conductance catheter techniques for use in blood volume measurements in the right ventricle.
Conf Proc IEEE Eng Med Biol Soc
· 2006 · PMID 17947174
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In this paper, we discuss a design for a wearable electrocardiograph device constructed with small, low-powered "mote" sensors for use in wireless sensor networks. A wearable, wireless three-lead electrocardiograph senso...In this paper, we discuss a design for a wearable electrocardiograph device constructed with small, low-powered "mote" sensors for use in wireless sensor networks. A wearable, wireless three-lead electrocardiograph sensor module is utilized and the initial tests are presented that illustrate the viability of this design. This device can be integrated into a suite of wearable wireless sensors used for patient monitoring and other applications.
Conf Proc IEEE Eng Med Biol Soc
· 2006 · PMID 17947173
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The optimal treatment of patients with complex diseases, such as cancers, depends on the accurate diagnosis by using a combination of clinical and histopathological data. In many scenarios, it becomes tremendously diffic...The optimal treatment of patients with complex diseases, such as cancers, depends on the accurate diagnosis by using a combination of clinical and histopathological data. In many scenarios, it becomes tremendously difficult because of the limitations in clinical presentation and histopathology. To accurate diagnose complex diseases, the molecular classification based on gene or protein expression profiles are indispensable for modern medicine. Moreover, many heterogeneous diseases consist of various potential subtypes in molecular basis and differ remarkably in their response to therapies. It is critical to accurate predict subgroup on disease gene expression profiles. More fundamental knowledge of the molecular basis and classification of disease could aid in the prediction of patient outcome, the informed selection of therapies, and identification of novel molecular targets for therapy. In this paper, we propose a new disease diagnostic method, probabilistic boosting tree (PB tree) method, on gene expression profiles of lung tumors. It enables accurate disease classification and subtype discovery in disease. It automatically constructs a tree in which each node combines a number of weak classifiers into a strong classifier. Also, subtype discovery is naturally embedded in the learning process. Our algorithm achieves excellent diagnostic performance, and meanwhile it is capable of detecting the disease subtype based on gene expression profile.
Yoon S, Garg A, Park HS
… +2 more, Park WY, De Micheli G
Conf Proc IEEE Eng Med Biol Soc
· 2006 · PMID 17947172
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We consider computationally reconstructing gene regulatory networks on top of the binary abstraction of gene expression state information. Unlike previous Boolean network approaches, the proposed method does not handle n...We consider computationally reconstructing gene regulatory networks on top of the binary abstraction of gene expression state information. Unlike previous Boolean network approaches, the proposed method does not handle noisy gene expression values directly. Instead, two-valued "hidden state" information is derived from gene expression profiles using a robust statistical technique, and a gene interaction network is inferred from this hidden state information. In particular, we exploit Espresso, a well-known 2-level Boolean logic optimizer in order to determine the core network structure. The resulting gene interaction networks can be viewed as dynamic Bayesian networks, which have key advantages over more conventional Bayesian networks in terms of biological phenomena that can be represented. The authors tested the proposed method with a time-course gene expression data set from microarray experiments on anti-cancer drugs doxorubicin and paclitaxel. A gene interaction network was produced by our method, and the identified genes were validated with a public annotation database. The experimental studies we conducted suggest that the proposed method inspired by engineering systems can be a very effective tool to decipher complex gene interactions in living systems.
Conf Proc IEEE Eng Med Biol Soc
· 2006 · PMID 17947171
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A latent-threshold model and misclassification algorithm were implemented to examine potential misdiagnosis among 16 Alzheimer's disease (AD) subjects using gene expression data. Results obtained without invoking the mis...A latent-threshold model and misclassification algorithm were implemented to examine potential misdiagnosis among 16 Alzheimer's disease (AD) subjects using gene expression data. Results obtained without invoking the misclassification algorithm showed limited predictive power of the model. When the misclassification algorithm was invoked, four subjects were identified as being potentially misdiagnosed. Results obtained after adjustment of the AD status of these four samples showed a significant increase in the model's predictive ability. Mixed model analysis detected no AD related genes as differentially expressed when using original classifications; conversely, multiple AD genes were identified using the new classifications. These results suggest that this algorithm can identify misclassified subjects which, in turn, can increase power to predict disease status and identify disease related genes.
Conf Proc IEEE Eng Med Biol Soc
· 2006 · PMID 17947170
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With the introduction of microarray, cancer classification, diagnosis and prediction are made more accurate and effective. However, the final outcome of the data analyses very much depend on the huge number of genes with...With the introduction of microarray, cancer classification, diagnosis and prediction are made more accurate and effective. However, the final outcome of the data analyses very much depend on the huge number of genes with relatively small number of samples present in each experiment. It is thus crucial to select relevant genes to be used for future specific cancer markers. Many feature selection methods have been proposed but none is able to classify all kinds of microarray data accurately, especially on those multi-class datasets. We propose a one-versus-one comparison method for selecting discriminatory features instead of performing the statistical test in a one-versus-all manner. Brain cancer is chosen as an example. Here, 3 types of statistics are used: signal-to-noise ratio (SNR), t-statistics and Pearson correlation coefficient. Results are verified by performing hierarchical and k-means clustering. Using our one-versus-one comparisons, best performance accuracies of 90.48% and 97.62% can be obtained by hierarchical and k-means clustering respectively. However best performance accuracies of 88.10% and 80.95% can be obtained respectively when using one-versus-all comparison. This shows that one-versus-one comparison is superior.
Conf Proc IEEE Eng Med Biol Soc
· 2006 · PMID 17947169
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Interpretation and classification of mass spectra is usually performed using a list of peaks as their mathematical representation. This fact makes peak detection a bottleneck of mass spectra analysis, since quality and b...Interpretation and classification of mass spectra is usually performed using a list of peaks as their mathematical representation. This fact makes peak detection a bottleneck of mass spectra analysis, since quality and biological relevance of any results strongly depends on the accuracy of peak detection process. Many algorithms utilize intensity to differentiate between peaks and noise and thus bias the detection process to the high abundant peaks. However important information may also be contained in the lower-intensity peaks that are more difficult to discover. We present an algorithm specifically designed for detection of low-abundant peaks.
Conf Proc IEEE Eng Med Biol Soc
· 2006 · PMID 17947168
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While high throughput screening (HTS) techniques are capable of generating large amounts of biologically significant data, assimilating and mining this information can be extremely complex and potentially crucial informa...While high throughput screening (HTS) techniques are capable of generating large amounts of biologically significant data, assimilating and mining this information can be extremely complex and potentially crucial information patterns can easily be lost in the mounds of data. The predominantly life-science oriented scientific training of the researchers in this area furthermore, precludes their using complex querying or data-mining algorithms. Keeping in account these challenges, our goal in this paper is to provide a highly intuitive environment for storing and interacting with large amounts of HTS assay data. The principal modes of user-data interactions supported in the proposed paradigm are interaction and visualization rich. Moreover, they span the heterogeneous data modalities common to drug discovery, including but not limited to chemical structures, high-throughput assay formats, graphical information, and alpha-numeric data types. Case studies and experiments demonstrate the efficacy of the proposed approach in terms of its ease of use as well as its capability to discern complex information patterns in the data.
Zaffari CA, Zaffari P, de Azevedo DF
… +3 more, Russomano T, Helegda S, Figueira MV
Conf Proc IEEE Eng Med Biol Soc
· 2006 · PMID 17947167
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This article presents a software program for visualization and processing of medical images. It provides an expansible set of techniques to help extracting visual information from medical images to be used in diagnosis s...This article presents a software program for visualization and processing of medical images. It provides an expansible set of techniques to help extracting visual information from medical images to be used in diagnosis support and in advanced scientific investigations.
Torres A, Fiz JA, Galdiz JB
… +3 more, Gea J, Morera J, Jané R
Conf Proc IEEE Eng Med Biol Soc
· 2006 · PMID 17947166
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The study of the mechanomyographic (MMG) signal of respiratory muscles is a promising technique in order to evaluate the respiratory muscles effort. The relationship between amplitude and power parameters of this signal...The study of the mechanomyographic (MMG) signal of respiratory muscles is a promising technique in order to evaluate the respiratory muscles effort. The relationship between amplitude and power parameters of this signal with the respiratory effort performed during respiration is of great interest for researchers and physicians due to its diagnostic potentials. In this study, it was analyzed the MMG signal of the diaphragm muscle acquired by means of a capacitive accelerometer applied on the costal wall. The new methodology investigated was based in the calculation of the Shannon entropy of the MMG signal during the diaphragm muscle voluntary contraction. The method was tested in an animal model, with two incremental respiratory protocols performed by two non anesthetized mongrel dogs. The results obtained in the respiratory tests analyzed indicate that the Shannon entropy was superior to other amplitude parameter methods, obtaining higher correlation coefficients (with p-values lower than 0.001) with the mean and maximum inspiratory pressures. Furthermore in this study we have proposed a moving mode high pass filter in order to eliminate the very low frequency component recorded by the sensor and due to movement artifacts and the gross movement of the thorax during respiration. With this non linear filtering method we have obtained higher correlation coefficients (with both entropy and amplitude parameters) than with the Wavelet multiresolution technique proposed in a previous work.
Conf Proc IEEE Eng Med Biol Soc
· 2006 · PMID 17947165
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EEG spectral power has been shown to correlate with level of arousal and alertness in humans. In this paper, we assess its usefulness in the detection of behavioral microsleeps (BMs). Eight non-sleep-deprived normal subj...EEG spectral power has been shown to correlate with level of arousal and alertness in humans. In this paper, we assess its usefulness in the detection of behavioral microsleeps (BMs). Eight non-sleep-deprived normal subjects performed two 1-hour sessions of a continuous tracking task while EEG and facial video were recorded. BMs were identified independent of tracking performance by a human rater by viewing the video recordings. Spectral power, normalized spectral power, and power ratios in the standard EEG bands were calculated using the Burg method on 16 bipolar derivations to form an EEG feature matrix. PCA was used to reduce the dimensionality of the feature matrix and linear discriminant analysis used to form a classifier for each subject. The 8 classifiers were combined using stacked generalization to create an overall detection model and N-fold cross-validation used to determine its performance (Phi=0.30 +/- 0.05, mean +/- SE). While modest, the detection of BMs at such a high temporal resolution (1 s) has not been achieved previously other than by our group.
Conf Proc IEEE Eng Med Biol Soc
· 2006 · PMID 17947164
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The time interval between the aortic (A2) and pulmonary (P2) components of the second heart sound (S2) is an indicator of the presence and severity of several cardiac abnormalities. However, in many cases identification...The time interval between the aortic (A2) and pulmonary (P2) components of the second heart sound (S2) is an indicator of the presence and severity of several cardiac abnormalities. However, in many cases identification of the A2 and P2 components is difficult due to their temporal overlap and significant spectral similarity. In this work, we present a method to extract the A2 and P2 components from the S2 sound, by assuming their mutual statistical independence. Once extracted, the A2 and P2 components are identified by using a physiological reference signal. Results obtained from real data are encouraging, and show promise for utilizing the proposed method in a clinical setting to non-invasively tract the A2-P2 time interval.