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Biomedical Engineering, IEEE Transactions on

Issue 12 • Date Dec. 2010

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  • Table of contents

    Publication Year: 2010 , Page(s): C1
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  • IEEE Transactions on Biomedical Engineering publication information

    Publication Year: 2010 , Page(s): C2
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  • Table of contents

    Publication Year: 2010 , Page(s): 2793 - 2794
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  • Pattern Mining of Multichannel sEMG for Tremor Classification

    Publication Year: 2010 , Page(s): 2795 - 2805
    Cited by:  Papers (4)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (418 KB) |  | HTML iconHTML  

    Tremor is defined as the involuntary rhythmic or quasi-rhythmic oscillation of a body part, resulting from alternating or simultaneous contractions of antagonistic muscle groups. While tremor may be physiological, those who have disabling pathological tremors find that performing typical activities for daily living to be physically challenging and emotionally draining. Detecting the presence of tremor and its proper identification are crucial in prescribing the appropriate therapy to lessen its deleterious physical, emotional, psychological, and social impact. While diagnosis relies heavily on clinical evaluation, pattern analysis of surface electromyogram (sEMG) signals can be a useful diagnostic aid for an objective identification of tremor types. Using sEMG system attached to several parts of the patient's body while performing several tasks, this research aims to develop a classifier system that automates the process of tremor types recognition. Finding the optimal model and its corresponding parameters is not a straightforward process. The resulting workflow, however, provides valuable information in understanding the interplay and impact of the different features and their parameters to the behavior and performance of the classifier system. The resulting model analysis helps identify the necessary locations for the placement of sEMG electrodes and relevant features that have significant impact in the process of classification. These information can help clinicians in streamlining the process of diagnosis without sacrificing its accuracy. View full abstract»

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  • Stimulus Protocol Determines the Most Computationally Efficient Preconditioner for the Bidomain Equations

    Publication Year: 2010 , Page(s): 2806 - 2815
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (549 KB) |  | HTML iconHTML  

    The efficient solution of the bidomain equations is a fundamental tool in the field of cardiac electrophysiology. When choosing a finite element discretization of the coupled system, one has to deal with the solution of a large, highly sparse system of linear equations. The conjugate gradient algorithm, along with suitable preconditioning, is the natural choice in this scenario. In this study, we identify the optimal preconditioners with respect to both stimulus protocol and mesh geometry. The results are supported by a comprehensive study of the mesh-dependence properties of several preconditioning techniques found in the literature. Our results show that when only intracellular stimulus is considered, incomplete LU factorization remains a valid choice for current cardiac geometries. However, when extracellular shocks are delivered to tissue, preconditioners that take into account the structure of the system minimize execution time and ensure mesh-independent convergence. View full abstract»

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  • Multivariate Analysis of Blood Oxygen Saturation Recordings in Obstructive Sleep Apnea Diagnosis

    Publication Year: 2010 , Page(s): 2816 - 2824
    Cited by:  Papers (12)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (327 KB) |  | HTML iconHTML  

    This study focuses on the analysis of blood oxygen saturation (SaO2) from nocturnal pulse oximetry (NPO) to help in the diagnosis of the obstructive sleep apnea (OSA) syndrome. A population of 148 patients suspected of suffering from OSA syndrome was studied. A wide set of 16 features was used to characterize changes in the SaO2 profile during the night. Our feature set included common statistics in the time and frequency domains, conventional spectral characteristics from the power spectral density (PSD) function, and nonlinear features. We performed feature selection by means of a step-forward logistic regression (LR) approach with leave-one-out cross-validation. Second- and fourth-order statistical moments in the time domain (M2t and M4t), the relative power in the 0.014-0.033 Hz frequency band (PR), and the Lempel-Ziv complexity (LZC) were automatically selected. 92.0% sensitivity, 85.4% specificity, and 89.7% accuracy were obtained. The optimum feature set significantly improved the diagnostic ability of each feature individually. Furthermore, our results outperformed classic oximetric indexes commonly used by physicians. We conclude that simultaneous analysis in the time and frequency domains by means of statistical moments, spectral and nonlinear features could provide complementary information from NPO to improve OSA diagnosis. View full abstract»

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  • Unsupervised Segmentation of Overlapped Nuclei Using Bayesian Classification

    Publication Year: 2010 , Page(s): 2825 - 2832
    Cited by:  Papers (7)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (733 KB) |  | HTML iconHTML  

    In a fully automatic cell extraction process, one of the main issues to overcome is the problem related to extracting overlapped nuclei since such nuclei will often affect the quantitative analysis of cell images. In this paper, we present an unsupervised Bayesian classification scheme for separating overlapped nuclei. The proposed approach first involves applying the distance transform to overlapped nuclei. The topographic surface generated by distance transform is viewed as a mixture of Gaussians in the proposed algorithm. In order to learn the distribution of the topographic surface, the parametric expectation-maximization (EM) algorithm is employed. Cluster validation is performed to determine how many nuclei are overlapped. Our segmentation approach incorporates a priori knowledge about the regular shape of clumped nuclei to yield more accurate segmentation results. Experimental results show that the proposed method yields superior segmentation performance, compared to those produced by conventional schemes. View full abstract»

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  • Calculation of Forward and Backward Arterial Waves by Analysis of Two Pressure Waveforms

    Publication Year: 2010 , Page(s): 2833 - 2839
    Cited by:  Papers (4)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (369 KB) |  | HTML iconHTML  

    We developed a technique to calculate forward and backward arterial waves from proximal and distal pressure waveforms. First, the relationship between the waveforms is represented with an arterial tube model. Then, the model parameters are estimated via least-squares fitting. Finally, the forward and backward waves are calculated using the parameter estimates. Thus, unlike most techniques, the arterial waves are determined without a more difficult flow measurement or an experimental perturbation. We applied the technique to central aortic and femoral artery pressure waveforms from anesthetized dogs during drug infusions, volume changes, and cardiac pacing. The calculated waves predicted an abdominal aortic pressure waveform measurement more accurately (2.4 mmHg error) than the analyzed waveforms (5.3 mmHg average error); reliably predicted relative changes in a femoral artery flow measurement (14.7% error); and changed as expected with selective vasoactive drugs. The ratio of the backward- to forward-wave magnitudes was 0.37 ± 0.05 during baseline. This index increased by ~50% with phenylephrine and norepinephrine, decreased by ~60% with dobutamine and nitroglycerin, and changed little otherwise. The time delay between the waves in the central aorta was 175 ± 14 ms during baseline. This delay varied by ±~25% and was inversely related to mean pressure. View full abstract»

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  • P- and T-Wave Delineation in ECG Signals Using a Bayesian Approach and a Partially Collapsed Gibbs Sampler

    Publication Year: 2010 , Page(s): 2840 - 2849
    Cited by:  Papers (10)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (723 KB) |  | HTML iconHTML  

    Detection and delineation of P- and T-waves are important issues in the analysis and interpretation of electrocardiogram (ECG) signals. This paper addresses this problem by using Bayesian inference to represent a priori relationships among ECG wave components. Based on the recently introduced partially collapsed Gibbs sampler principle, the wave delineation and estimation are conducted simultaneously by using a Bayesian algorithm combined with a Markov chain Monte Carlo method. This method exploits the strong local dependency of ECG signals. The proposed strategy is evaluated on the annotated QT database and compared to other classical algorithms. An important feature of this paper is that it allows not only for the detection of P- and T-wave peaks and boundaries, but also for the accurate estimation of waveforms for each analysis window. This can be useful for some ECG analysis that require wave morphology information. View full abstract»

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  • Automatic Bayesian Classification of Healthy Controls, Bipolar Disorder, and Schizophrenia Using Intrinsic Connectivity Maps From fMRI Data

    Publication Year: 2010 , Page(s): 2850 - 2860
    Cited by:  Papers (1)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (617 KB) |  | HTML iconHTML  

    We present a method for supervised, automatic, and reliable classification of healthy controls, patients with bipolar disorder, and patients with schizophrenia using brain imaging data. The method uses four supervised classification learning machines trained with a stochastic gradient learning rule based on the minimization of Kullback-Leibler divergence and an optimal model complexity search through posterior probability estimation. Prior to classification, given the high dimensionality of functional MRI (fMRI) data, a dimension reduction stage comprising two steps is performed: first, a one-sample univariate t-test mean-difference Tscore approach is used to reduce the number of significant discriminative functional activated voxels, and then singular value decomposition is performed to further reduce the dimension of the input patterns to a number comparable to the limited number of subjects available for each of the three classes. Experimental results using functional brain imaging (fMRI) data include receiver operation characteristic curves for the three-way classifier with area under curve values around 0.82, 0.89, and 0.90 for healthy control versus nonhealthy, bipolar disorder versus nonbipolar, and schizophrenia patients versus nonschizophrenia binary problems, respectively. The average three-way correct classification rate (CCR) is in the range of 70%-72%, for the test set, remaining close to the estimated Bayesian optimal CCR theoretical upper bound of about 80% , estimated from the one nearest-neighbor classifier over the same data. View full abstract»

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  • Reversible Projection Technique for Colon Unfolding

    Publication Year: 2010 , Page(s): 2861 - 2869
    Cited by:  Papers (2)  |  Patents (1)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (725 KB) |  | HTML iconHTML  

    Colon unfolding provides an efficient way to navigate the colon in computed tomographic colonography (CTC). Most existing unfolding techniques only compute forward projections. When radiologists find abnormalities or conduct measurements on the unfolded view (which is often quicker and easier), it is difficult to locate the corresponding region on the 3-D view for further examination (which is more accurate and reliable). To address this, we propose a reversible projection technique for colon unfolding. The method makes use of advanced algorithms including rotation-minimizing frames, recursive ring sets, mesh skinning, and cylindrical projection. Both forward and reverse mapping can be computed for points on the colon surface. Therefore, it allows for detecting and measuring polyps on the unfolded view and mapping them back to the 3-D surface. We generated realistic colon simulation data incorporating most colon characteristics, such as curved centerline, variable distention, haustral folds, teniae coli, and colonic polyps. Our method was tested on both simulated data and data from 110 clinical CTC studies. The results showed submillimeter accuracy in simulated data and -0.23 ± 1.67 mm in the polyp measurement using clinical CTC data. The major contributions of our technique are: 1) the use of a recursive ring set method to solve the centerline and surface correspondence problem; 2) reverse transformation from the unfolded view to the 3-D view; and 3) quantitative validation using a realistic colon simulation and clinical CTC polyp measurement. View full abstract»

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  • Power Law as a Method for Ultrasound Detection of Internal Bleeding: In Vivo Rabbit Validation

    Publication Year: 2010 , Page(s): 2870 - 2875
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (333 KB) |  | HTML iconHTML  

    New detection methods for vascular injuries can augment the usability of an ultrasound (US) imager in trauma settings. The goal of this study was to evaluate a potential-detection strategy for internal bleeding that employs a well-established theoretical biofluid model, the power law. This law characterizes normal blood-flow rates through an arterial tree by its bifurcation geometry. By detecting flows that deviate from the model, we hypothesized that vascular abnormalities could be localized. We devised a bleed metric, flow-split deviation (FSD), that quantified the difference between patient and model blood flows at vessel bifurcations. Femoral bleeds were introduced into ten rabbits (~5 kg) using a cannula attached to a variable pump. Different bleed rates (0% as control, 5%, 10%, 15%, 20%, 25%, and 30% of descending aortic flow) were created at two physiological states (rest and elevated state with epinephrine). FSDs were found by US imaging the iliac arteries. Our bleed metric demonstrated good sensitivity and specificity at moderate bleed rates; area under receiver-operating characteristic curves were greater than 0.95 for bleed rates 20% and higher. Thus, FSD was a good indicator of bleed severity and may serve as an additional tool in the US bleed detection. View full abstract»

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  • A Combined Fluorescence and Microcomputed Tomography System for Small Animal Imaging

    Publication Year: 2010 , Page(s): 2876 - 2883
    Cited by:  Papers (6)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (534 KB) |  | HTML iconHTML  

    Fluorescence molecular tomography (FMT) plays an important role in studying physiological and pathological processes of small animals in vivo at molecular level. However, this technique suffers from relatively low spatial resolution. To complement the problem, there has been a strong demand for providing functional and morphological analysis at the same time. In this paper, we proposed a hybrid full-angle free-space FMT and X-ray micro-cone-beam computed tomography (CT) (micro-CBCT) prototype system, providing both functional and anatomical images. During the whole acquisition, the two subsystems acquire projection images (fluorescence and CT) synchronously to keep consistent body position without moving the animals. The acquired datasets are intrinsically coregistered in the corresponding coordinate and identified geometry. Tomographic fluorescence and CT images are reconstructed using normalized Born-based spatial regularization and Feldkamp-Davis-Kress methods, respectively. The experimental results of both phantom and in vivo mouse preliminarily validate the accuracy and performance of the integrated system. View full abstract»

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  • Direct Identification of Bacteria in Blood Culture Samples Using an Electronic Nose

    Publication Year: 2010 , Page(s): 2884 - 2890
    Cited by:  Papers (4)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (549 KB) |  | HTML iconHTML  

    In this paper, we introduce a method for identification of bacteria in human blood culture samples using an electronic nose. The method uses features, which capture the static (steady state) and dynamic (transient) properties of the signal from the gas sensor array and proposes a means to ensemble results from consecutive samples. The underlying mechanism for ensembling is based on an estimation of posterior probability, which is extracted from a support vector machine classifier. A large dataset representing ten different bacteria cultures has been used to validate the presented methods. The results detail the performance of the proposed algorithm and show that through ensembling decisions on consecutive samples, significant reliability in classification accuracy can be achieved. View full abstract»

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  • Design and Fabrication of a Magnetic Propulsion System for Self-Propelled Capsule Endoscope

    Publication Year: 2010 , Page(s): 2891 - 2902
    Cited by:  Papers (12)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1606 KB) |  | HTML iconHTML  

    This paper investigates design, modeling, simulation, and control issues related to self-propelled endoscopic capsule navigated inside the human body through external magnetic fields. A novel magnetic propulsion system is proposed and fabricated, which has great potential of being used in the field of noninvasive gastrointestinal endoscopy. Magnetic-analysis model is established and finite-element simulations as well as orthogonal design are performed for obtaining optimized mechanical and control parameters for generating appropriate external magnetic field. Simulated intestinal tract experiments are conducted, demonstrating controllable movement of the capsule under the developed magnetic propulsion system. View full abstract»

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  • Optical Fiber-Coupled Ocular Spectrometer for Measurement of Drug Concentration in the Anterior Eye—Applications in Pharmaceuticals Research

    Publication Year: 2010 , Page(s): 2903 - 2909
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (270 KB) |  | HTML iconHTML  

    This paper describes in detail a novel optoelectronic system designed to measure drug absorption in the anterior segment of the eye following topical application of drug formulations. This minimally invasive measurement technique offers both a method for determining drug concentration in human eyes, and demonstrates an alternative to current testing processes in model animals, which require paracentesis of the anterior chamber of the eye. The optoelectronic technique can be used with formulations, which possess appropriate spectral characteristics, namely unique absorption or fluorescence spectra. Preliminary experiments using our measurement system have been performed in rabbit and man, where we have been successful in achieving the direct measurement of topically applied brimonidine, an alpha-2 agonist used in the treatment of glaucoma. This demonstrates the feasibility of performing real-time, in vivo testing of ophthalmic drug formulations in the eye of human test subjects. We further demonstrate the novel application of the optoelectronic system for detection of topically applied UV-absorbing compounds in rabbit cadaver eyes, with a view to evaluating potential ocular sunscreen formulations. In summary, this method can be applied for the rapid comparison of the penetration of different drug formulations into the anterior eye at greatly reduced cost and time. View full abstract»

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  • Light-Scattering Study of the Normal Human Eye Lens: Elastic Properties and Age Dependence

    Publication Year: 2010 , Page(s): 2910 - 2917
    Cited by:  Papers (1)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (390 KB) |  | HTML iconHTML  

    The human ocular lens is a tissue capable of changing its shape to dynamically adjust the optical power of the eye, a function known as accommodation, which gradually declines with age. This capability is the response of the lens tissue to external forces, which, in turn, is modulated by the biomechanical characteristics of lens tissues. In order to investigate the contributions of lens sclerosis to loss of accommodation, we report on in vitro confocal Brillouin light scattering studies of human ocular lenses spanning over a 30-70 year age range. Using this nondestructive measurement method, we determined that the longitudinal bulk modulus (average ± SD) of the lens nucleus (2.79 ± 0.14 GPa) was consistently greater than the bulk modulus of the lens cortex (2.36 ± 0.09 GPa). Moreover, our results showed that these differences were not age dependent over the 40 year age range that we evaluated using healthy lens tissues. Our results are consistent with the hypothesis that an age-dependent change in the bulk modulus of lens tissues does not fully account for the natural decline of accommodation. View full abstract»

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  • Quantitative Falls Risk Assessment Using the Timed Up and Go Test

    Publication Year: 2010 , Page(s): 2918 - 2926
    Cited by:  Papers (23)  |  Patents (1)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (682 KB) |  | HTML iconHTML  

    Falls are a major problem in older adults worldwide with an estimated 30% of elderly adults over 65 years of age falling each year. The direct and indirect societal costs associated with falls are enormous. A system that could provide an accurate automated assessment of falls risk prior to falling would allow timely intervention and ease the burden on overstretched healthcare systems worldwide. An objective method for assessing falls risk using body-worn kinematic sensors is reported. The gait and balance of 349 community-dwelling elderly adults was assessed using body-worn sensors while each patient performed the “timed up and go” (TUG) test. Patients were also evaluated using the Berg balance scale (BBS). Of the 44 reported parameters derived from body-worn kinematic sensors, 29 provided significant discrimination between patients with a history of falls and those without. Cross-validated estimates of retrospective falls prediction performance using logistic regression models yielded a mean sensitivity of 77.3% and a mean specificity of 75.9%. This compares favorably to the cross-validated performance of logistic regression models based on the time taken to complete the TUG test (manually timed TUG) and the Berg balance score. These models yielded mean sensitivities of 58.0% and 57.8%, respectively, and mean specificities of 64.8% and 64.2%, respectively. Results suggest that this method offers an improvement over two standard falls risk assessments (TUG and BBS) and may have potential for use in supervised assessment of falls risk as part of a longitudinal monitoring protocol. View full abstract»

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  • Adaptation in P300 Brain–Computer Interfaces: A Two-Classifier Cotraining Approach

    Publication Year: 2010 , Page(s): 2927 - 2935
    Cited by:  Papers (6)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (748 KB) |  | HTML iconHTML  

    A cotraining-based approach is introduced for constructing high-performance classifiers for P300-based brain-computer interfaces (BCIs), which were trained from very little data. It uses two classifiers: Fisher's linear discriminant analysis and Bayesian linear discriminant analysis progressively teaching each other to build a final classifier, which is robust and able to learn effectively from unlabeled data. Detailed analysis of the performance is carried out through extensive cross-validations, and it is shown that the proposed approach is able to build high-performance classifiers from just a few minutes of labeled data and by making efficient use of unlabeled data. An average bit rate of more than 37 bits/min was achieved with just one and a half minutes of training, achieving an increase of about 17 bits/min compared to the fully supervised classification in one of the configurations. This performance improvement is shown to be even more significant in cases where the training data as well as the number of trials that are averaged for detection of a character is low, both of which are desired operational characteristics of a practical BCI system. Moreover, the proposed method outperforms the self-training-based approaches where the confident predictions of a classifier is used to retrain itself. View full abstract»

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  • Regularized Common Spatial Pattern With Aggregation for EEG Classification in Small-Sample Setting

    Publication Year: 2010 , Page(s): 2936 - 2946
    Cited by:  Papers (18)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (751 KB) |  | HTML iconHTML  

    Common spatial pattern (CSP) is a popular algorithm for classifying electroencephalogram (EEG) signals in the context of brain-computer interfaces (BCIs). This paper presents a regularization and aggregation technique for CSP in a small-sample setting (SSS). Conventional CSP is based on a sample-based covariance-matrix estimation. Hence, its performance in EEG classification deteriorates if the number of training samples is small. To address this concern, a regularized CSP (R-CSP) algorithm is proposed, where the covariance-matrix estimation is regularized by two parameters to lower the estimation variance while reducing the estimation bias. To tackle the problem of regularization parameter determination, R-CSP with aggregation (R-CSP-A) is further proposed, where a number of R-CSPs are aggregated to give an ensemble-based solution. The proposed algorithm is evaluated on data set IVa of BCI Competition III against four other competing algorithms. Experiments show that R-CSP-A significantly outperforms the other methods in average classification performance in three sets of experiments across various testing scenarios, with particular superiority in SSS. View full abstract»

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  • Interhemispheric Asynchrony Correlates With Severity of Respiratory Disturbance Index in Patients With Sleep Apnea

    Publication Year: 2010 , Page(s): 2947 - 2955
    Cited by:  Papers (1)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (403 KB) |  | HTML iconHTML  

    Obstructive sleep apnea (OSA) hypopnea syndrome is a disorder characterized by airway obstructions during sleep; full obstructions are known as apnea and partial obstructions are called hypopnea. Sleep in OSA patients is significantly disturbed with frequent apnea/hypopnea and arousal events. We illustrate that these events lead to functional asymmetry of the brain as manifested by the interhemispheric asynchrony (IHA) computed using EEG recorded on the scalp. In this paper, based on the higher order spectra of IHA time series, we propose a new index [interhemispheric synchrony index (IHSI)], for characterizing brain asynchrony in OSA. The IHSI computation does not depend on subjective criteria and can be completely automated. The proposed method was evaluated on overnight EEG data from a clinical database of 36 subjects referred to a hospital sleep laboratory. Our results indicated that the IHSI could classify the patients into OSA/non-OSA classes with an accuracy of 91% (ρ = 0.0001), at the respiratory disturbance index threshold of 10, suggesting that the brain asynchrony carries vital information on OSA. View full abstract»

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  • Call for papers ICORR 2011

    Publication Year: 2010 , Page(s): 2956
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  • 2010 Index IEEE Transactions on Biomedical Engineering Vol. 57

    Publication Year: 2010 , Page(s): 2957 - 3011
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  • IEEE Transactions on Biomedical Engineering information for authors

    Publication Year: 2010 , Page(s): C3
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  • IEEE Transactions on Biomedical Engineering Associate Editors

    Publication Year: 2010 , Page(s): C4
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Aims & Scope

IEEE Transactions on Biomedical Engineering contains basic and applied papers dealing with biomedical engineering. Papers range from engineering development in methods and techniques with biomedical applications to experimental and clinical investigations with engineering contributions.

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Meet Our Editors

Editor-in-Chief
Bin He
Department of Biomedical Engineering