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Biomedical and Health Informatics, IEEE Journal of

Issue 3 • Date May 2013

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  • [Front cover]

    Publication Year: 2013 , Page(s): C1
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    Freely Available from IEEE
  • IEEE Journal of Biomedical and Health Informatics publication information

    Publication Year: 2013 , Page(s): C2
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    Freely Available from IEEE
  • Table of contents

    Publication Year: 2013 , Page(s): 501 - 502
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    Freely Available from IEEE
  • Graphical Representation for DNA Sequences via Joint Diagonalization of Matrix Pencil

    Publication Year: 2013 , Page(s): 503 - 511
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (712 KB) |  | HTML iconHTML  

    Graphical representations provide us with a tool allowing visual inspection of the sequences. To visualize and compare different DNA sequences, a novel alignment-free method is proposed in this paper for both graphical representation and similarity analysis of sequences. We introduce a transformation to represent each DNA sequence with neighboring nucleotide matrix. Then, based on approximate joint diagonalization theory, we transform each DNA primary sequence into a corresponding eigenvalue vector (EVV), which can be considered as numerical characterization of DNA sequence. Meanwhile, we get graphical representation for DNA sequence via the plot of EVV in 2-D plane. Moreover, using k-means, we cluster these feature curves of sequences into several reasonable subclasses. In addition, similarity analyses are performed by computing the distances among the obtained vectors. This approach contains more sequence information, and it analyzes all the involved sequence information jointly rather than separately. A typical dendrogram constructed by this method demonstrates the effectiveness of our approach. View full abstract»

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  • Determining the Semantic Similarities Among Gene Ontology Terms

    Publication Year: 2013 , Page(s): 512 - 525
    Cited by:  Papers (2)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1339 KB) |  | HTML iconHTML  

    We present in this paper novel techniques that determine the semantic relationships among Gene Ontology (GO) terms. We implemented these techniques in a prototype system called GoSE, which resides between user application and GO database. Given a set S of GO terms, GoSE would return another set S' of GO terms, where each term in S' is semantically related to each term in S. Most current research is focused on determining the semantic similarities among GO ontology terms based solely on their IDs and proximity to one another in the GO graph structure, while overlooking the contexts of the terms, which may lead to erroneous results. The context of a GO term T is the set of other terms, whose existence in the GO graph structure is dependent on T . We propose novel techniques that determine the contexts of terms based on the concept of existence dependency. We present a stack-based sort-merge algorithm employing these techniques for determining the semantic similarities among GO terms. We evaluated GoSE experimentally and compared it with three existing methods. The results of measuring the semantic similarities among genes in KEGG and Pfam pathways retrieved from the DBGET and Sanger Pfam databases, respectively, have shown that our method outperforms the other three methods in recall and precision. View full abstract»

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  • Phoneme-Based Self Hearing Assessment on a Smartphone

    Publication Year: 2013 , Page(s): 526 - 529
    Save to Project icon | Click to expandQuick Abstract | PDF file iconPDF (374 KB) |  | HTML iconHTML  

    Phonemes provide an interesting alternative to pure tones in hearing tests. We propose a new smartphone-based method for self-hearing assessment using the four Korean phonemes which are similar to the English phonemes /a/, /i/, /sh/, and /s/. We conducted tests on 15 subjects diagnosed with mild to severe hearing loss and estimated their conventional pure-tone hearing thresholds from their phoneme hearing thresholds using regression analysis. The phoneme-based self-hearing assessment was found to be sufficiently reliable in estimating the hearing thresholds of hearing-impaired subjects. The difference between the hearing thresholds obtained through conventional pure-tone audiometry and those obtained using our method was 5.6 dB HL on average. The proposed hearing assessment was able to significantly reduce the mean test time compared to conventional pure-tone audiometry. View full abstract»

    Open Access
  • Adaptive Calibration Algorithm for Plasma Glucose Estimation in Continuous Glucose Monitoring

    Publication Year: 2013 , Page(s): 530 - 538
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1728 KB) |  | HTML iconHTML  

    Minimally or noninvasive continuous glucose monitors estimate plasma glucose from compartments alternative to blood, and may revolutionize the management of diabetes. However, the accuracy of current devices is still poor and it may partly depend on low performance of the implemented calibration algorithm. Here, a new adaptive calibration algorithm based on a population local-model-based inter-compartmental glucose dynamic model is proposed. The novelty consists in the adaptation of data normalization parameters in real time to estimate and compensate patient's sensitivity variations. Adaptation is performed to minimize mean absolute relative deviation at the calibration points with a time window forgetting strategy. Four calibrations are used: preprandial and 1.5 h postprandial at two different meals. Two databases are used for validation: 1) a 9-h CGMS Gold (Medtronic, Northridge, USA) time series with paired reference glucose values from a clinical study in 17 subjects with type 1 diabetes; 2) data from 30 virtual patients (UVa simulator, Virginia, USA), where inter- and intrasubject variability of sensor's sensitivity were simulated. Results show how the adaptation of the normalization parameters improves the performance of the calibration algorithm since it counteracts sensor sensitivity variations. This improvement is more evident in one-week simulations. View full abstract»

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  • Estimation of Sleep Onset Latency Based on the Blood Pressure Regulatory Reflex Mechanism

    Publication Year: 2013 , Page(s): 539 - 544
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (529 KB) |  | HTML iconHTML  

    Sleep onset latency (SOL) is an objective indicator of sleepiness and is being used clinically as a diagnostic tool for sleep deprivation. This study proposes a new and less intrusive approach to estimate SOL based on the blood pressure (BP) regulatory reflex mechanism. We hypothesized that the arterial baroreflex, one kind of reflex mechanism for BP regulation, maintains the toning-down effect sleep has on BP. The arterial baroreflex is strongly activated after the time of sleep onset in order to maintain the lowered BP by leading to an increase in heart beat interval (HBI). This observation suggests that the arterial baroreflex has a marked influence on the HBI control with the onset of sleep. As a result, a positive correlation is expected between fluctuations of BP and those of subsequent HBI after sleep onset. To investigate our hypothesis, we determined the relationship between BP and HBI using the R-J and R-R intervals measured from an electrocardiogram and a ballistocardiogram. We estimated SOL using the correlation coefficients corresponding to the relationship between fluctuations of the R-J interval and those of the subsequent R-R interval. The SOLs of ten healthy subjects [age (mean ± SD): 28.72 ± 3.21 years] were estimated using our proposed method and compared with the polysomnography data. The mean absolute error was 0.25 ± 0.35 min, corresponding to a half epoch (15 s) on average. We expect our method will be applicable as a nonintrusive and automatic SOL estimation system that does not require the use of electroencephalogram sensors. View full abstract»

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  • Boosting-Based EMG Patterns Classification Scheme for Robustness Enhancement

    Publication Year: 2013 , Page(s): 545 - 552
    Cited by:  Papers (1)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1013 KB) |  | HTML iconHTML  

    The high conventional accuracy of pattern recognition-based surface myoelectric classification in laboratory experiments does not necessarily result in high accessibility to practical protheses. An obvious reason is the effect of signals of untrained classes caused by the relatively small training dataset. In order to make the classifier robust to untrained classes, a classification scheme is developed based on boosting and random forest classifiers in this paper. Meanwhile, a threshold, the post probability of the prediction, is introduced as a balance (i.e., adjust) between the accurate classification and the rejection of the samples belonging to some untrained classes. The experiments are conducted to compare with other two schemes using linear discriminant analysis and support vector machines. Surface electromyogram signals, labeled with seven isometric movements, are collected from six healthy subjects' forearm. It is shown that the proposed scheme can reach up to about 92% accuracy in recognizing trained classes and 20% for untrained classes. Through adjusting the threshold, the accuracy of rejecting untrained classes reaches up to around 80%, with small decrease in recognizing trained classes (down to 80%). In the analysis of experiments' results, we also find that the proposed scheme has better error distribution among the classes. View full abstract»

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  • Human Daily Activity Recognition With Sparse Representation Using Wearable Sensors

    Publication Year: 2013 , Page(s): 553 - 560
    Cited by:  Papers (3)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (534 KB) |  | HTML iconHTML  

    Human daily activity recognition using mobile personal sensing technology plays a central role in the field of pervasive healthcare. One major challenge lies in the inherent complexity of human body movements and the variety of styles when people perform a certain activity. To tackle this problem, in this paper, we present a novel human activity recognition framework based on recently developed compressed sensing and sparse representation theory using wearable inertial sensors. Our approach represents human activity signals as a sparse linear combination of activity signals from all activity classes in the training set. The class membership of the activity signal is determined by solving a l1 minimization problem. We experimentally validate the effectiveness of our sparse representation-based approach by recognizing nine most common human daily activities performed by 14 subjects. Our approach achieves a maximum recognition rate of 96.1%, which beats conventional methods based on nearest neighbor, naive Bayes, and support vector machine by as much as 6.7%. Furthermore, we demonstrate that by using random projection, the task of looking for “optimal features” to achieve the best activity recognition performance is less important within our framework. View full abstract»

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  • RSSI/LQI-Based Transmission Power Control for Body Area Networks in Healthcare Environment

    Publication Year: 2013 , Page(s): 561 - 571
    Cited by:  Papers (1)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1754 KB) |  | HTML iconHTML  

    This paper presents a novel transmission power control protocol for body area networks. Conventional transmission power control protocols adjust the transmission power on the basis of the received signal strength indication (RSSI). However, in the case of presence of interference, the RSSI is not a correct indicator to determine the link state. We first present empirical evidence for this and then propose a practical protocol to discriminate between the signal attenuation and interference using the RSSI and link quality indication. This protocol controls the transmission power and avoids interference based on the link state. Finally, we discuss the implementation of the proposed protocol on Tmote Sky and evaluate the performance in the presence and absence of interference. The experimental results showed that the proposed protocol has high energy efficiency and reliability, even in the presence of interference. View full abstract»

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  • A New Framework Based on Recurrence Quantification Analysis for Epileptic Seizure Detection

    Publication Year: 2013 , Page(s): 572 - 578
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (964 KB) |  | HTML iconHTML  

    This study presents applying recurrence quantification analysis (RQA) on EEG recordings and their subbands: delta, theta, alpha, beta, and gamma for epileptic seizure detection. RQA is adopted since it does not require assumptions about stationarity, length of signal, and noise. The decomposition of the original EEG into its five constituent subbands helps better identification of the dynamical system of EEG signal. This leads to better classification of the database into three groups: Healthy subjects, epileptic subjects during a seizure-free interval (Interictal) and epileptic subjects during a seizure course (Ictal). The proposed algorithm is applied to an epileptic EEG dataset provided by Dr. R. Andrzejak of the Epilepsy Center, University of Bonn, Bonn, Germany. Combination of RQA-based measures of the original signal and its subbands results in an overall accuracy of 98.67% that indicates high accuracy of the proposed method. View full abstract»

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  • A Survey on Ambient-Assisted Living Tools for Older Adults

    Publication Year: 2013 , Page(s): 579 - 590
    Cited by:  Papers (3)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (368 KB) |  | HTML iconHTML  

    In recent years, we have witnessed a rapid surge in assisted living technologies due to a rapidly aging society. The aging population, the increasing cost of formal health care, the caregiver burden, and the importance that the individuals place on living independently, all motivate development of innovative-assisted living technologies for safe and independent aging. In this survey, we will summarize the emergence of `ambient-assisted living” (AAL) tools for older adults based on ambient intelligence paradigm. We will summarize the state-of-the-art AAL technologies, tools, and techniques, and we will look at current and future challenges. View full abstract»

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  • A Hybrid Low Power Biopatch for Body Surface Potential Measurement

    Publication Year: 2013 , Page(s): 591 - 599
    Cited by:  Papers (2)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (904 KB) |  | HTML iconHTML  

    This paper presents a wearable biopatch prototype for body surface potential measurement. It combines three key technologies, including mixed-signal system on chip (SoC) technology, inkjet printing technology, and anisotropic conductive adhesive (ACA) bonding technology. An integral part of the biopatch is a low-power low-noise SoC. The SoC contains a tunable analog front end, a successive approximation register analog-to-digital converter, and a reconfigurable digital controller. The electrodes, interconnections, and interposer are implemented by inkjet-printing the silver ink precisely on a flexible substrate. The reliability of printed traces is evaluated by static bending tests. ACA is used to attach the SoC to the printed structures and form the flexible hybrid system. The biopatch prototype is light and thin with a physical size of 16 cm × 16 cm. Measurement results show that low-noise concurrent electrocardiogram signals from eight chest points have been successfully recorded using the implemented biopatch. View full abstract»

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  • Removal of Ocular Artifacts in EEG—An Improved Approach Combining DWT and ANC for Portable Applications

    Publication Year: 2013 , Page(s): 600 - 607
    Cited by:  Papers (2)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (709 KB) |  | HTML iconHTML  

    A new model to remove ocular artifacts (OA) from electroencephalograms (EEGs) is presented. The model is based on discrete wavelet transformation (DWT) and adaptive noise cancellation (ANC). Using simulated and measured data, the accuracy of the model is compared with the accuracy of other existing methods based on stationary wavelet transforms and our previous work based on wavelet packet transform and independent component analysis. A particularly novel feature of the new model is the use of DWTs to construct an OA reference signal, using the three lowest frequency wavelet coefficients of the EEGs. The results show that the new model demonstrates an improved performance with respect to the recovery of true EEG signals and also has a better tracking performance. Because the new model requires only single channel sources, it is well suited for use in portable environments where constraints with respect to acceptable wearable sensor attachments usually dictate single channel devices. The model is also applied and evaluated against data recorded within the EUFP 7 Project-Online Predictive Tools for Intervention in Mental Illness (OPTIMI). The results show that the proposed model is effective in removing OAs and meets the requirements of portable systems used for patient monitoring as typified by the OPTIMI project. View full abstract»

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  • Classification of Finger Movements for the Dexterous Hand Prosthesis Control With Surface Electromyography

    Publication Year: 2013 , Page(s): 608 - 618
    Cited by:  Papers (8)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (3410 KB) |  | HTML iconHTML  

    A method for the classification of finger movements for dexterous control of prosthetic hands is proposed. Previous research was mainly devoted to identify hand movements as these actions generate strong electromyography (EMG) signals recorded from the forearm. In contrast, in this paper, we assess the use of multichannel surface electromyography (sEMG) to classify individual and combined finger movements for dexterous prosthetic control. sEMG channels were recorded from ten intact-limbed and six below-elbow amputee persons. Offline processing was used to evaluate the classification performance. The results show that high classification accuracies can be achieved with a processing chain consisting of time domain-autoregression feature extraction, orthogonal fuzzy neighborhood discriminant analysis for feature reduction, and linear discriminant analysis for classification. We show that finger and thumb movements can be decoded accurately with high accuracy with latencies as short as 200 ms. Thumb abduction was decoded successfully with high accuracy for six amputee persons for the first time. We also found that subsets of six EMG channels provide accuracy values similar to those computed with the full set of EMG channels (98% accuracy over ten intact-limbed subjects for the classification of 15 classes of different finger movements and 90% accuracy over six amputee persons for the classification of 12 classes of individual finger movements). These accuracy values are higher than previous studies, whereas we typically employed half the number of EMG channels per identified movement. View full abstract»

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  • High-Resolution, Low-Delay, and Error-Resilient Medical Ultrasound Video Communication Using H.264/AVC Over Mobile WiMAX Networks

    Publication Year: 2013 , Page(s): 619 - 628
    Cited by:  Papers (4)
    Save to Project icon | Click to expandQuick Abstract | PDF file iconPDF (649 KB) |  | HTML iconHTML  

    In this study, we describe an effective video communication framework for the wireless transmission of H.264/AVC medical ultrasound video over mobile WiMAX networks. Medical ultrasound video is encoded using diagnostically driven, error resilient encoding, where quantization levels are varied as a function of the diagnostic significance of each image region. We demonstrate how our proposed system allows for the transmission of high-resolution clinical video that is encoded at the clinical acquisition resolution and can then be decoded with low delay. To validate performance, we perform OPNET simulations of mobile WiMAX medium access control and physical layers characteristics that include service prioritization classes, different modulation and coding schemes, fading channel's conditions, and mobility. We encode the medical ultrasound videos at the 4CIF (704×576) resolution that can accommodate clinical acquisition that is typically performed at lower resolutions. Video quality assessment is based on both clinical (subjective) and objective evaluations. View full abstract»

    Open Access
  • Improved Estimation of the Number of Independent Components for Functional Magnetic Resonance Data by a Whitening Filter

    Publication Year: 2013 , Page(s): 629 - 641
    Cited by:  Papers (1)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1402 KB) |  | HTML iconHTML  

    Independent component analysis (ICA) has been widely applied to the analysis of fMRI data. Accurate estimation of the number of independent components (ICs) in fMRI data is critical to reduce over/underfitting. Various methods based on information theoretic criteria (ITC) have been used to estimate the intrinsic dimension of fMRI data. An important assumption of ITC is that the noise is purely white. However, this assumption is often violated by the existence of temporally correlated noise in fMRI data. In this study, we introduced a filtering method into the order selection to remove the autocorrelation from the colored noise by using the whitening filter proposed by Prudon and Weisskoff. Results of the simulated data show that the filtering method has strong robustness to noise and significantly improves the accuracy of order selection from data with colored noise. Moreover, the multifiltering method proposed by us was applied to real fMRI data to improve the performance of ITC. Results of the real fMRI data show that the proposed method can alleviate the overestimation due to the autocorrelation of colored noise. We further compared the stability of IC estimates of real fMRI data at order estimated by minimum description length criterion based on the filtered and unfiltered data by using the software package ICASSO. Results show that ICA yields more stable IC estimates using the reduced order by filtering. View full abstract»

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  • A Novel Framework for Cellular Tracking and Mitosis Detection in Dense Phase Contrast Microscopy Images

    Publication Year: 2013 , Page(s): 642 - 653
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1096 KB) |  | HTML iconHTML  

    The aim of this paper is to detail the development of a novel tracking framework that is able to extract the cell motility indicators and to determine the cellular division (mitosis) events in large time-lapse phase-contrast image sequences. To address the challenges induced by nonstructured (random) motion, cellular agglomeration, and cellular mitosis, the process of automatic (unsupervised) cell tracking is carried out in a sequential manner, where the interframe cell association is achieved by assessing the variation in the local cellular structures in consecutive frames of the image sequence. In our study, a strong emphasis has been placed on the robust use of the topological information in the cellular tracking process and in the development of targeted pattern recognition techniques that were designed to redress the problems caused by segmentation errors, and to precisely identify mitosis using a backward (reversed) tracking strategy. The proposed algorithm has been evaluated on dense phase-contrast cellular data and the experimental results indicate that the proposed algorithm is able to accurately track epithelial and endothelial cells in time-lapse image sequences that are characterized by low contrast and high level of noise. Our algorithm achieved 86.10% overall tracking accuracy and 90.12% mitosis detection accuracy. View full abstract»

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  • A Suction Detection System for Rotary Blood Pumps Based on the Lagrangian Support Vector Machine Algorithm

    Publication Year: 2013 , Page(s): 654 - 663
    Cited by:  Papers (2)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (871 KB) |  | HTML iconHTML  

    The left ventricular assist device is a rotary mechanical pump that is implanted in patients with congestive heart failure to help the left ventricle in pumping blood in the circulatory system. However, using such a device may result in a very dangerous event, called ventricular suction, that can cause ventricular collapse due to overpumping of blood from the left ventricle when the rotational speed of the pump is high. Therefore, a reliable technique for detecting ventricular suction is crucial. This paper presents a new suction detection system that can precisely classify pump flow patterns, based on a Lagrangian support vector machine (LSVM) model that combines six suction indices extracted from the pump flow signal to make a decision about whether the pump is in suction, approaching suction, or not in suction. The proposed method has been tested using in vivo experimental data based on two different pumps. The simulation results show that the system can produce superior performance in terms of classification accuracy, stability, learning speed, and good robustness compared to three other existing suction detection methods and the original support vector machine (SVM) algorithm. The ability of the proposed algorithm to detect suction provides a reliable platform for the development of a feedback control system to control the speed of the pump while at the same time ensuring that suction is avoided. View full abstract»

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  • Secure and Lightweight Network Admission and Transmission Protocol for Body Sensor Networks

    Publication Year: 2013 , Page(s): 664 - 674
    Cited by:  Papers (3)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (560 KB) |  | HTML iconHTML  

    A body sensor network (BSN) is a wireless network of biosensors and a local processing unit, which is commonly referred to as the personal wireless hub (PWH). Personal health information (PHI) is collected by biosensors and delivered to the PWH before it is forwarded to the remote healthcare center for further processing. In a BSN, it is critical to only admit eligible biosensors and PWH into the network. Also, securing the transmission from each biosensor to PWH is essential not only for ensuring safety of PHI delivery, but also for preserving the privacy of PHI. In this paper, we present the design, implementation, and evaluation of a secure network admission and transmission subsystem based on a polynomial-based authentication scheme. The procedures in this subsystem to establish keys for each biosensor are communication efficient and energy efficient. Moreover, based on the observation that an adversary eavesdropping in a BSN faces inevitable channel errors, we propose to exploit the adversary's uncertainty regarding the PHI transmission to update the individual key dynamically and improve key secrecy. In addition to the theoretical analysis that demonstrates the security properties of our system, this paper also reports the experimental results of the proposed protocol on resource-limited sensor platforms, which show the efficiency of our system in practice. View full abstract»

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  • Enhancing Bowel Sounds by Using a Higher Order Statistics-Based Radial Basis Function Network

    Publication Year: 2013 , Page(s): 675 - 680
    Cited by:  Papers (1)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (951 KB) |  | HTML iconHTML  

    Auscultation of bowel sounds provides a noninvasive method to the diagnosis of gastrointestinal motility diseases. However, bowel sounds can be easily contaminated by background noises, and the frequency band of bowel sounds is easily overlapped with background noise. Therefore, it is difficult to enhance the noisy bowel sounds by using precise digital filters. In this study, a higher order statistics (HOS)-based radial basis function (RBF) network was proposed to enhance noisy bowel sounds. An HOS technique provides the ability of suppressing Gaussian noises and symmetrically distributed non-Gaussian noises due to their natural tolerance. Therefore, the influence of additional noises on the HOS-based learning algorithm can be reduced effectively. The simulated and experimental results show that the HOS-based RBF can exactly provide better performance for enhancing bowel sounds under stationary and nonstationary Gaussian noises. Therefore, the HOS-based RBF can be considered as a good approach for enhancing noisy bowel sounds. View full abstract»

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  • 360° Fourier Transform Profilometry in Surface Reconstruction for Fluorescence Molecular Tomography

    Publication Year: 2013 , Page(s): 681 - 689
    Cited by:  Papers (1)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (833 KB) |  | HTML iconHTML  

    Fluorescence molecular tomography (FMT) is an emerging tool in the observation of diseases. A fast and accurate surface reconstruction of the experimental object is needed as a boundary constraint for FMT reconstruction. In this paper, an automatic, noncontact, and 3-D surface reconstruction method named 360° Fourier transform profilometry (FTP) is proposed to reconstruct 3-D surface profiles for FMT system. This method can reconstruct 360° integrated surface profiles utilizing the single-frame FTP at different angles. Results show that the relative mean error of the surface reconstruction of this method is less than 1.4% in phantom experiments, and is no more than 2.9% in mouse experiments in vivo. Compared with the Radon transform method, the proposed method reduces the computation time by more than 90% with a minimal error increase. At last, a combined 360° FTP/FMT experiment is conducted on a nude mouse. Not only can the 360° FTP system operate with the FMT system simultaneously, but it can also help to monitor the status of animals. Moreover, the 360° FTP system is independent of FMT system and can be performed to reconstruct the surface by itself. View full abstract»

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  • ACM-Based Automatic Liver Segmentation From 3-D CT Images by Combining Multiple Atlases and Improved Mean-Shift Techniques

    Publication Year: 2013 , Page(s): 690 - 698
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (826 KB) |  | HTML iconHTML  

    In this paper, we present an autocontext model (ACM)-based automatic liver segmentation algorithm, which combines ACM, multiatlases, and mean-shift techniques to segment liver from 3-D CT images. Our algorithm is a learning-based method and can be divided into two stages. At the first stage, i.e., the training stage, ACM is performed to learn a sequence of classifiers in each atlas space (based on each atlas and other aligned atlases). With the use of multiple atlases, multiple sequences of ACM-based classifiers are obtained. At the second stage, i.e., the segmentation stage, the test image will be segmented in each atlas space by applying each sequence of ACM-based classifiers. The final segmentation result will be obtained by fusing segmentation results from all atlas spaces via a multi-classifier fusion technique. Specially, in order to speed up segmentation, given a test image, we first use an improved mean-shift algorithm to perform oversegmentation and then implement the region-based image labeling instead of the original inefficient pixel-based image labeling. The proposed method is evaluated on the datasets of MICCAI 2007 liver segmentation challenge. The experimental results show that the average volume overlap error and the average surface distance achieved by our method are 8.3% and 1.5 m, respectively, which are comparable to the results reported in the existing state-of-the-art work on liver segmentation. View full abstract»

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  • Objective Study of Sensor Relevance for Automatic Cough Detection

    Publication Year: 2013 , Page(s): 699 - 707
    Cited by:  Papers (3)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (674 KB) |  | HTML iconHTML  

    The development of a system for the automatic, objective, and reliable detection of cough events is a need underlined by the medical literature for years. The benefit of such a tool is clear as it would allow the assessment of pathology severity in chronic cough diseases. Even though some approaches have recently reported solutions achieving this task with a relative success, there is still no standardization about the method to adopt or the sensors to use. The goal of this paper is to study objectively the performance of several sensors for cough detection: ECG, thermistor, chest belt, accelerometer, contact, and audio microphones. Experiments are carried out on a database of 32 healthy subjects producing, in a confined room and in three situations, voluntary cough at various volumes as well as other event categories which can possibly lead to some detection errors: background noise, forced expiration, throat clearing, speech, and laugh. The relevance of each sensor is evaluated at three stages: mutual information conveyed by the features, ability to discriminate at the frame level cough from these latter other sources of ambiguity, and ability to detect cough events. In this latter experiment, with both an averaged sensitivity and specificity of about 94.5%, the proposed approach is shown to clearly outperform the commercial Karmelsonix system which achieved a specificity of 95.3% and a sensitivity of 64.9%. View full abstract»

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Aims & Scope

J-BHI publishes original papers describing recent advances in the field of biomedical and health informatics where information and communication technologies intersect with health, healthcare, life sciences and biomedicine.  Papers must contain original content in theoretical analysis, methods, technical development, and/or novel clinical applications of information systems.

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Meet Our Editors

Editor-in-Chief

GUANG-ZHONG YANG,
Director, The Hamlyn Centre
Imperial College London, UK
g.z.yang@imperial.ac.uk
jbhi-eic@embs.org