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Biomedical Engineering, IEEE Transactions on

Issue 2 • Date Feb. 2013

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  • [Front cover]

    Publication Year: 2013 , Page(s): C1
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  • IEEE Transactions on Biomedical Engineering publication information

    Publication Year: 2013 , Page(s): C2
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  • Table of contents

    Publication Year: 2013 , Page(s): 257 - 258
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  • Quantitative Evaluation of Two-Factor Analysis Applied to Hepatic Perfusion Study Using Contrast-enhanced Ultrasound

    Publication Year: 2013 , Page(s): 259 - 267
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (697 KB) |  | HTML iconHTML  

    Focal liver lesions (FLLs) are usually quantitatively assessed by time-intensity curves (TICs) extracted from contrast-enhanced ultrasound (CEUS) image sequences. To overcome the subjectivity of manual region of interest (ROI) selection and automatically extract TICs, a novel factor analysis method called replace approximation (RA) was proposed. Assuming that the two factors are the arterial and portal vein phases, respectively, the high-dimensional time-series data are mapped into 1-D space, where the TIC at each pixel in the image becomes a point along a one-dimensional axis. The RA method aims to seek two apexes corresponding to the factor curves (the targeted TICs) in the subspace. This method was tested on 18 free-breathing datasets with respiratory motion correction. The experimental results showed that the RA method extracted physiological factor curves and the corresponding factor images efficiently. The mean correlation coefficient between the factor curves and the corresponding ROI measurements was 0.95 ± 0.02. Furthermore, the wash-in time ratio indexes of FLLs derived from the factor curves were used to perform parametric imaging, which could represent the characteristics of different types of FLLs. These results indicate that two-factor analysis has the potential to perform quantitative analysis of hepatic perfusion, which would be helpful to the differential diagnosis of FLLs. View full abstract»

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  • Single Optical Fiber Probe for Fluorescence Detection and Optogenetic Stimulation

    Publication Year: 2013 , Page(s): 268 - 280
    Cited by:  Papers (1)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (936 KB) |  | HTML iconHTML  

    We have developed a fiber-optic-based probe for precise delivery of stimulation/excitation light pulses and detection of faint fluorescence signals for applications in neuroscience and optogenetics. In this design, a thin multimode fiber serves as the head of the probe to be inserted into the brain. This fiber is used to deliver light to the region of interest and guide a sample of the emission signal back to detectors. The major tradeoff in the design of such a system is to decrease the size of the fiber and intensity of input light to minimize physical damage and to avoid photobleaching/phototoxicity but to keep the signal-to-noise ratio (S/N) reasonably high. Here, the excitation light and the associated emission signal are frequency modulated. Then, the output of the detector is passed through a time lens which compresses the distributed energy of the emission signal and maximizes the instantaneous S/N. By measuring the statistics of the noise, the structure of the time lens is designed to achieve the global optimum of S/N. We have also designed side-firing fibers and a micromechanical assembly for distributed light delivery and fluorescence detection. View full abstract»

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  • Robust Alignment of Prostate Histology Slices With Quantified Accuracy

    Publication Year: 2013 , Page(s): 281 - 291
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (498 KB) |  | HTML iconHTML  

    No current imaging technique is capable of detecting with precision tumors in the prostate. To evaluate each technique, the histology data must be precisely mapped to the imaged data. As the histology slices cannot be assumed to be cut along the same plane as the imaged data were acquired, the registration must be considered as a 3-D problem. This requires the prior alignment of the histology slices. We propose a protocol in which three needles are inserted into the fresh prostate, creating internal fiducial markers visible in the histology slices. Our algorithm then automatically detects and identifies these markers, enabling the automatic rigid alignment of each slice. The accuracy of the algorithm was quantified in simulated images, a beef liver sample in which a validation marker had been created, and ten prostate specimens. The simulated images showed that the algorithm has no associated residual error for a situation where there is no deformation. In the beef liver images, the average accuracy of the alignment was 0.12 ± 0.09 mm at the fiducial markers, and 0.62 ± 0.46 mm at a validation marker positioned approximately 20 mm from the fiducial markers. Concerning the ten prostates, there were 19.2 histology slices on average per specimen. On average, 93.7% of the fiducial markers created were visible in the slices, of which 96.1% were then automatically and correctly detected and identified, enabling an alignment of average accuracy 0.18 ± 0.13 mm at the fiducial markers. As a cancer of volume <;0.5 cm3 is classified as clinically insignificant, the accuracy achieved justified the choice of a rigid registration. An attractive feature of this method is the time required, less than 6 min on average per prostate specimen. View full abstract»

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  • The Transesophageal Echocardiography Simulator Based on Computed Tomography Images

    Publication Year: 2013 , Page(s): 292 - 299
    Cited by:  Papers (2)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (611 KB) |  | HTML iconHTML  

    Simulators are a new tool in education in many fields, including medicine, where they greatly improve familiarity with medical procedures, reduce costs, and, importantly, cause no harm to patients. This is so in the case of transesophageal echocardiography (TEE), in which the use of a simulator facilitates spatial orientation and helps in case studies. The aim of the project described in this paper is to simulate an examination by TEE. This research makes use of available computed tomography data to simulate the corresponding echocardiographic view. This paper describes the essential characteristics that distinguish these two modalities and the key principles of the wave phenomena that should be considered in the simulation process, taking into account the conditions specific to the echocardiography. The construction of the CT2TEE (Web-based TEE simulator) is also presented. The considerations include ray-tracing and ray-casting techniques in the context of ultrasound beam and artifact simulation. An important aspect of the interaction with the user is raised. View full abstract»

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  • Compressed Sensing for Energy-Efficient Wireless Telemonitoring of Noninvasive Fetal ECG Via Block Sparse Bayesian Learning

    Publication Year: 2013 , Page(s): 300 - 309
    Cited by:  Papers (15)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (2656 KB) |  | HTML iconHTML  

    Fetal ECG (FECG) telemonitoring is an important branch in telemedicine. The design of a telemonitoring system via a wireless body area network with low energy consumption for ambulatory use is highly desirable. As an emerging technique, compressed sensing (CS) shows great promise in compressing/reconstructing data with low energy consumption. However, due to some specific characteristics of raw FECG recordings such as nonsparsity and strong noise contamination, current CS algorithms generally fail in this application. This paper proposes to use the block sparse Bayesian learning framework to compress/reconstruct nonsparse raw FECG recordings. Experimental results show that the framework can reconstruct the raw recordings with high quality. Especially, the reconstruction does not destroy the interdependence relation among the multichannel recordings. This ensures that the independent component analysis decomposition of the reconstructed recordings has high fidelity. Furthermore, the framework allows the use of a sparse binary sensing matrix with much fewer nonzero entries to compress recordings. Particularly, each column of the matrix can contain only two nonzero entries. This shows that the framework, compared to other algorithms such as current CS algorithms and wavelet algorithms, can greatly reduce code execution in CPU in the data compression stage. View full abstract»

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  • Tissue Classification Using Ultrasound-Induced Variations in Acoustic Backscattering Features

    Publication Year: 2013 , Page(s): 310 - 320
    Cited by:  Papers (1)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (513 KB) |  | HTML iconHTML  

    Ultrasound (US) radio-frequency (RF) time series is an effective tissue classification method that enables accurate cancer diagnosis, but the mechanisms underlying this method are not completely understood. This paper presents a model to describe the variations in tissue temperature and sound speed that take place during the RF time series scanning procedures and relate these variations to US backscattering. The model was used to derive four novel characterization features. These features were used to classify three animal tissues, and they obtained accuracies as high as 88.01%. The performance of the proposed features was compared with RF time series features proposed in a previous study. The results indicated that the US-induced variations in tissue temperature and sound speed, which were used to derive the proposed features, were important contributors to the tissue typing capabilities of the RF time series. Simulations carried out to estimate the heating induced during the scanning procedure employed in this study showed temperature rises lower than 2 °C. The model and results presented in this paper can be used to improve the RF time series. View full abstract»

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  • Haustral Fold Segmentation With Curvature-Guided Level Set Evolution

    Publication Year: 2013 , Page(s): 321 - 331
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (894 KB) |  | HTML iconHTML  

    Human colon has complex structures mostly because of the haustral folds. The folds are thin flat protrusions on the colon wall, which complicate the shape analysis for computer-aided detection (CAD) of colonic polyps. Fold segmentation may help reduce the structural complexity, and the folds can serve as an anatomic reference for computed tomographic colonography (CTC). Therefore, in this study, based on a model of the haustral fold boundaries, we developed a level-set approach to automatically segment the fold surfaces. To evaluate the developed fold segmentation algorithm, we first established the ground truth of haustral fold boundaries by experts' drawing on 15 patient CTC datasets without severe under/over colon distention from two medical centers. The segmentation algorithm successfully detected 92.7% of the folds in the ground truth. In addition to the sensitivity measure, we further developed a merit of segmented-area ratio (SAR), i.e., the ratio between the area of the intersection and union of the expert-drawn folds and the area of the automatically segmented folds, to measure the segmentation accuracy. The segmentation algorithm reached an average value of SAR = 86.2%, showing a good match with the ground truth on the fold surfaces. We believe the automatically segmented fold surfaces have the potential to benefit many postprocedures in CTC, such as CAD, taenia coli extraction, supine-prone registration, etc. View full abstract»

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  • Local Intensity Feature Tracking and Motion Modeling for Respiratory Signal Extraction in Cone Beam CT Projections

    Publication Year: 2013 , Page(s): 332 - 342
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1253 KB) |  | HTML iconHTML  

    Accounting for respiration motion during imaging can help improve targeting precision in radiation therapy. We propose local intensity feature tracking (LIFT), a novel markerless breath phase sorting method in cone beam computed tomography (CBCT) scan images. The contributions of this study are twofold. First, LIFT extracts the respiratory signal from the CBCT projections of the thorax depending only on tissue feature points that exhibit respiration. Second, the extracted respiratory signal is shown to correlate with standard respiration signals. LIFT extracts feature points in the first CBCT projection of a sequence and tracks those points in consecutive projections forming trajectories. Clustering is applied to select trajectories showing an oscillating behavior similar to the breath motion. Those “breathing” trajectories are used in a 3-D reconstruction approach to recover the 3-D motion of the lung which represents the respiratory signal. Experiments were conducted on datasets exhibiting regular and irregular breathing patterns. Results showed that LIFT-based respiratory signal correlates with the diaphragm position-based signal with an average phase shift of 1.68 projections as well as with the internal marker-based signal with an average phase shift of 1.78 projections. LIFT was able to detect the respiratory signal in all projections of all datasets. View full abstract»

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  • Detection of Cancer Using Advanced Computerized Analysis of Infrared Spectra of Peripheral Blood

    Publication Year: 2013 , Page(s): 343 - 353
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (875 KB) |  | HTML iconHTML  

    We have developed a novel approach for detection of cancer based on biochemical analysis of peripheral blood plasma using Fourier transform infrared spectroscopy. This approach has proven to be quick, safe, minimal invasive, and effective. Our approach recognizes any signs of solid tumor presence, regardless of location in the body or cancer type by measuring a spectrum that gives information regarding the total molecular composition and structure of the peripheral blood samples. The analysis includes clinically relevant preprocessing and feature extraction with principal component analysis, and uses Fisher's linear discriminant analysis to classify between cancer patients and healthy controls. We evaluated our method with leave-one-out cross validation and were able to establish sensitivity of 93.33%, specificity of 87.8%, and overall accuracy of 90.7%. Using our method for cancer detection should result in fewer unnecessary invasive procedures and yield fast detection of solid tumors. View full abstract»

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  • Microwave Radar and Microwave-Induced Thermoacoustics: Dual-Modality Approach for Breast Cancer Detection

    Publication Year: 2013 , Page(s): 354 - 360
    Cited by:  Papers (2)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (446 KB) |  | HTML iconHTML  

    Microwave radar and microwave-induced thermoacoustics, recently proposed as promising breast cancer detection techniques, each have shortcomings that reduce detection performance. Making the assumption that the measurement noises experienced when applying these two techniques are independent, we propose a methodology to process the input signals jointly based on a hypothesis testing framework. We present two test statistics and derive their distributions to set the thresholds. The methodology is evaluated on numerically simulated signals acquired from 2-D numerical breast models using finite-difference time-domain method. Our results show that the proposed dual-modality approach can give a significant improvement in detection performance. View full abstract»

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  • Simultaneously Extracting Multiple Parameters via Fitting One Single Autocorrelation Function Curve in Diffuse Correlation Spectroscopy

    Publication Year: 2013 , Page(s): 361 - 368
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (516 KB) |  | HTML iconHTML  

    Near-infrared diffuse correlation spectroscopy (DCS) has recently been employed for noninvasive acquisition of blood flow information in deep tissues. Based on the established correlation diffusion equation, the light intensity autocorrelation function detected by DCS is determined by a blood flow index αDB, tissue absorption coefficient μa, reduced scattering coefficient μs', and a coherence factor β. This study is designed to investigate the possibility of extracting multiple parameters such as μa, μs', β, and αDB through fitting one single autocorrelation function curve and evaluate the performance of different fitting methods. For this purpose, computer simulations, tissue-like phantom experiments, and in vivo tissue measurements were utilized. The results suggest that it is impractical to simultaneously fit αDB and μa or αDB and μs' from one single autocorrelation function curve due to the large crosstalk between these paired parameters. However, simultaneously fitting β and αDB is feasible and generates more accurate estimation with smaller standard deviation compared to the conventional two-step fitting method (i.e., first calculating β and then fitting αDB). The outcomes from this study provide a crucial guidance for DCS data analysis. View full abstract»

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  • Computerized Detection of Lung Nodules by Means of “Virtual Dual-Energy” Radiography

    Publication Year: 2013 , Page(s): 369 - 378
    Cited by:  Papers (1)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (939 KB) |  | HTML iconHTML  

    Major challenges in current computer-aided detection (CADe) schemes for nodule detection in chest radiographs (CXRs) are to detect nodules that overlap with ribs and/or clavicles and to reduce the frequent false positives (FPs) caused by ribs. Detection of such nodules by a CADe scheme is very important, because radiologists are likely to miss such subtle nodules. Our purpose in this study was to develop a CADe scheme with improved sensitivity and specificity by use of “virtual dual-energy” (VDE) CXRs where ribs and clavicles are suppressed with massive-training artificial neural networks (MTANNs). To reduce rib-induced FPs and detect nodules overlapping with ribs, we incorporated the VDE technology in our CADe scheme. The VDE technology suppressed rib and clavicle opacities in CXRs while maintaining soft-tissue opacity by use of the MTANN technique that had been trained with real dual-energy imaging. Our scheme detected nodule candidates on VDE images by use of a morphologic filtering technique. Sixty morphologic and gray-level-based features were extracted from each candidate from both original and VDE CXRs. A nonlinear support vector classifier was employed for classification of the nodule candidates. A publicly available database containing 140 nodules in 140 CXRs and 93 normal CXRs was used for testing our CADe scheme. All nodules were confirmed by computed tomography examinations, and the average size of the nodules was 17.8 mm. Thirty percent (42/140) of the nodules were rated “extremely subtle” or “very subtle” by a radiologist. The original scheme without VDE technology achieved a sensitivity of 78.6% (110/140) with 5 (1165/233) FPs per image. By use of the VDE technology, more nodules overlapping with ribs or clavicles were detected and the sensitivity was improved substantially to 85.0% (119/140) at the same FP rate in a leave-one-out cross-validation test, whereas the FP rate was reduced to 2.5 (583/233) p- r image at the same sensitivity level as the original CADe scheme obtained (Difference between the specificities of the original and the VDE-based CADe schemes was statistically significant). In particular, the sensitivity of our VDE-based CADe scheme for subtle nodules (66.7% = 28/42) was statistically significantly higher than that of the original CADe scheme (57.1% = 24/42). Therefore, by use of VDE technology, the sensitivity and specificity of our CADe scheme for detection of nodules, especially subtle nodules, in CXRs were improved substantially. View full abstract»

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  • Coaxial Needle Insertion Assistant With Enhanced Force Feedback

    Publication Year: 2013 , Page(s): 379 - 389
    Cited by:  Papers (2)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1015 KB) |  | HTML iconHTML  

    Many medical procedures involving needle insertion into soft tissues, such as anesthesia, biopsy, brachytherapy, and placement of electrodes, are performed without image guidance. In such procedures, haptic detection of changing tissue properties at different depths during needle insertion is important for needle localization and detection of subsurface structures. However, changes in tissue mechanical properties deep inside the tissue are difficult for human operators to sense, because the relatively large friction force between the needle shaft and the surrounding tissue masks the smaller tip forces. A novel robotic coaxial needle insertion assistant, which enhances operator force perception, is presented. This one-degree-of-freedom cable-driven robot provides to the operator a scaled version of the force applied by the needle tip to the tissue, using a novel design and sensors that separate the needle tip force from the shaft friction force. The ability of human operators to use the robot to detect membranes embedded in artificial soft tissue was tested under the conditions of 1) tip force and shaft force feedback, and 2) tip force only feedback. The ratio of successful to unsuccessful membrane detections was significantly higher (up to 50%) when only the needle tip force was provided to the user. View full abstract»

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  • Quantitative Analysis of Locomotive Behavior of Human Sperm Head and Tail

    Publication Year: 2013 , Page(s): 390 - 396
    Cited by:  Papers (3)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (744 KB) |  | HTML iconHTML  

    Sperm selection plays a significant role in in vitro fertilization (IVF). Approaches for assessing sperm quality include noninvasive techniques based on sperm morphology and motility as well as invasive techniques for checking DNA integrity. In 2006, a new device using hyaluronic acid (HA)-coated dish for sperm selection was cleared by the Food and Drug Administration (FDA) and entered IVF clinics. In this technique, only sperms with DNA integrity bind to the HA droplet, after which these bound sperm stop revealing head motion and their tail movement becomes more vigorous. However, selecting a single sperm cell from among HA-bound sperms is ad hoc in IVF clinics. Different from existing sperm tracking algorithms that are largely limited to tracking sperm head only and are only able to track one sperm at a time, this paper presents a multisperm tracking algorithm that tracks both sperm heads and low-contrast sperm tails. The tracking results confirm a significant correlation between sperm head velocity and tail beating amplitude, demonstrate that sperms bound to HA generally have a higher velocity (before binding) than those sperms that are not able to bind to HA microdots, and quantitatively reveal that HA-bound sperms' tail beating amplitudes are different among HA-bound sperms. View full abstract»

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  • Joint Source/Channel Coding for Prioritized Wireless Transmission of Multiple 3-D Regions of Interest in 3-D Medical Imaging Data

    Publication Year: 2013 , Page(s): 397 - 405
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1064 KB) |  | HTML iconHTML  

    This paper presents a 3-D medical image coding method featuring two major improvements to previous work on 3-D region of interest (RoI) coding for telemedicine applications. Namely, 1) a data prioritization scheme that allows coding of multiple 3-D-RoIs; and 2) a joint/source channel coding scheme that allows prioritized transmission of multiple 3-D-RoIs over wireless channels. The method, which is based on the 3-D integer wavelet transform and embedded block coding with optimized truncation with 3-D context modeling, generates scalable and error-resilient bit streams with 3-D-RoI decoding capabilities. Coding of multiple 3-D-RoIs is attained by prioritizing the wavelet-transformed data according to a Gaussian mixed distribution, whereas error resiliency is attained by employing the error correction capabilities of rate-compatible punctured turbo codes. The robustness of the proposed method is evaluated for transmission of real 3-D medical images over Rayleigh-fading channels with a priori knowledge of the channel condition. Evaluation results show that the proposed coding method provides a superior performance compared to equal error protection and unequal error protection techniques. View full abstract»

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  • An Online Failure Detection Method of the Glucose Sensor-Insulin Pump System: Improved Overnight Safety of Type-1 Diabetic Subjects

    Publication Year: 2013 , Page(s): 406 - 416
    Cited by:  Papers (1)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1214 KB) |  | HTML iconHTML  

    Sensors for real-time continuous glucose monitoring (CGM) and pumps for continuous subcutaneous insulin infusion (CSII) have opened new scenarios for Type-1 diabetes treatment. However, occasional failures of either CGM or CSII may expose diabetic patients to possibly severe risks, especially overnight (e.g., inappropriate insulin administration). In this contribution, we present a method to detect in real time such failures by simultaneously using CGM and CSII data streams and a black-box model of the glucose-insulin system. First, an individualized state-space model of the glucose-insulin system is identified offline from CGM and CSII data collected during a previous monitoring. Then, this model, CGM and CSII real-time data streams are used online to obtain predictions of future glucose concentrations together with their confidence intervals by exploiting a Kalman filtering approach. If glucose values measured by the CGM sensor are not consistent with the predictions, a failure alert is generated in order to mitigate the risks for patient safety. The method is tested on 100 virtual patients created by using the UVA/Padova Type-1 diabetic simulator. Three different types of failures have been simulated: spike in the CGM profile, loss of sensitivity of glucose sensor, and failure in the pump delivery of insulin. Results show that, in all cases, the method is able to correctly generate alerts, with a very limited number of false negatives and a number of false positives, on average, lower than 10%. The use of the method in three subjects supports the simulation results, demonstrating that the accuracy of the method in generating alerts in presence of failures of the CGM sensor-CSII pump system can significantly improve safety of Type-1 diabetic patients overnight. View full abstract»

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  • Advantages and Limitations of Using Matrix Pencil Method for the Modal Analysis of Medical Percussion Signals

    Publication Year: 2013 , Page(s): 417 - 426
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1274 KB) |  | HTML iconHTML  

    Although clinical percussion remains one of the most widespread traditional noninvasive methods for diagnosing pulmonary disease, the available analysis of physical characteristics of the percussion sound using modern signal processing techniques is still quite limited. The majority of existing literature on the subject reports either time-domain or spectral analysis methods. However, Fourier analysis, which represents the signal as a sum of infinite periodic harmonics, is not naturally suited for decomposition of short and aperiodic percussion signals. Broadening of the spectral peaks due to damping leads to their overlapping and masking of the lower amplitude peaks, which could be important for the fine-level signal classification. In this study, an attempt is made to automatically decompose percussion signals into a sum of exponentially damped harmonics, which in this case form a more natural basis than Fourier harmonics and thus allow for a more robust representation of the signal in the parametric space. The damped harmonic decomposition of percussion signals recorded on healthy volunteers in clinical setting is performed using the matrix pencil method, which proves to be quite robust in the presence of noise and well suited for the task. View full abstract»

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  • Automatic Monocular System for Human Fall Detection Based on Variations in Silhouette Area

    Publication Year: 2013 , Page(s): 427 - 436
    Cited by:  Papers (5)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1030 KB) |  | HTML iconHTML  

    Population of old generation is growing in most countries. Many of these seniors are living alone at home. Falling is among the most dangerous events that often happen and may need immediate medical care. Automatic fall detection systems could help old people and patients to live independently. Vision-based systems have advantage over wearable devices. These visual systems extract some features from video sequences and classify fall and normal activities. These features usually depend on camera's view direction. Using several cameras to solve this problem increases the complexity of the final system. In this paper, we propose to use variations in silhouette area that are obtained from only one camera. We use a simple background separation method to find the silhouette. We show that the proposed feature is view invariant. Extracted feature is fed into a support vector machine for classification. Simulation of the proposed method using a publicly available dataset shows promising results. View full abstract»

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  • Adaptive Wavelet Wiener Filtering of ECG Signals

    Publication Year: 2013 , Page(s): 437 - 445
    Cited by:  Papers (2)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (928 KB) |  | HTML iconHTML  

    In this study, we focused on the reduction of broadband myopotentials (EMG) in ECG signals using the wavelet Wiener filtering with noise-free signal estimation. We used the dyadic stationary wavelet transform (SWT) in the Wiener filter as well as in estimating the noise-free signal. Our goal was to find a suitable filter bank and to choose other parameters of the Wiener filter with respect to the signal-to-noise ratio (SNR) obtained. Testing was performed on artificially noised signals from the standard CSE database sampled at 500 Hz. When creating an artificial interference, we started from the generated white Gaussian noise, whose power spectrum was modified according to a model of the power spectrum of an EMG signal. To improve the filtering performance, we used adaptive setting parameters of filtering according to the level of interference in the input signal. We were able to increase the average SNR of the whole test database by about 10.6 dB. The proposed algorithm provides better results than the classic wavelet Wiener filter. View full abstract»

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  • Monitoring Epidemic Alert Levels by Analyzing Internet Search Volume

    Publication Year: 2013 , Page(s): 446 - 452
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (778 KB) |  | HTML iconHTML  

    The prevention of infectious diseases is a global health priority area. The early detection of possible epidemics is the first and important defense line against infectious diseases. However, conventional surveillance systems, e.g., the Centers for Disease Control and Prevention (CDC), rely on clinical data. The CDC publishes the surveillance results weeks after epidemic outbreaks. To improve the early detection of epidemic outbreaks, we designed a syndromic surveillance system to predict the epidemic trends based on disease-related Google search volume. Specifically, we first represented the epidemic trend with multiple alert levels to reduce the noise level. Then, we predicted the epidemic alert levels using a continuous density HMM, which incorporated the intrinsic characteristic of the disease transmission for alert level estimation. Respective models are built to monitor both national and regional epidemic alert levels of the U.S. The proposed system can provide real-time surveillance results, which are weeks before the CDC's reports. This paper focusses on monitoring the infectious disease in the U.S., however, we believe similar approach may be used to monitor epidemics for the developing countries as well. View full abstract»

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  • Electrosurgical Vessel Sealing Tissue Temperature: Experimental Measurement and Finite Element Modeling

    Publication Year: 2013 , Page(s): 453 - 460
    Cited by:  Papers (1)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (606 KB) |  | HTML iconHTML  

    The temporal and spatial tissue temperature profile in electrosurgical vessel sealing was experimentally measured and modeled using finite element modeling (FEM). Vessel sealing procedures are often performed near the neurovascular bundle and may cause collateral neural thermal damage. Therefore, the heat generated during electrosurgical vessel sealing is of concern among surgeons. Tissue temperature in an in vivo porcine femoral artery sealed using a bipolar electrosurgical device was studied. Three FEM techniques were incorporated to model the tissue evaporation, water loss, and fusion by manipulating the specific heat, electrical conductivity, and electrical contact resistance, respectively. These three techniques enable the FEM to accurately predict the vessel sealing tissue temperature profile. The averaged discrepancy between the experimentally measured temperature and the FEM predicted temperature at three thermistor locations is less than 7%. The maximum error is 23.9%. Effects of the three FEM techniques are also quantified. View full abstract»

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  • Quantifying Limb Movements in Epileptic Seizures Through Color-Based Video Analysis

    Publication Year: 2013 , Page(s): 461 - 469
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (5019 KB) |  | HTML iconHTML  

    This paper proposes a color-based video analytic system for quantifying limb movements in epileptic seizure monitoring. The system utilizes colored pyjamas to facilitate limb segmentation and tracking. Thus, it is unobtrusive and requires no sensor/marker attached to patient's body. We employ Gaussian mixture models in background/foreground modeling and detect limbs through a coarse-to-fine paradigm with graph-cut-based segmentation. Next, we estimate limb parameters with domain knowledge guidance and extract displacement and oscillation features from movement trajectories for seizure detection/analysis. We report studies on sequences captured in an epilepsy monitoring unit. Experimental evaluations show that the proposed system has achieved comparable performance to EEG-based systems in detecting motor seizures. View full abstract»

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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.

Full Aims & Scope

Meet Our Editors

Editor-in-Chief
Bin He
Department of Biomedical Engineering