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Early Access Articles

Early Access articles are new content made available in advance of the final electronic or print versions and result from IEEE's Preprint or Rapid Post processes. Preprint articles are peer-reviewed but not fully edited. Rapid Post articles are peer-reviewed and edited but not paginated. Both these types of Early Access articles are fully citable from the moment they appear in IEEE Xplore.

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Displaying Results 1 - 25 of 148
  • Local Bit-plane Decoded Pattern: A Novel Feature Descriptor for Biomedical Image Retrieval

    Publication Year: 2015 , Page(s): 1
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1167 KB)  

    A novel image feature descriptor based on local bit-plane decoded pattern (LBDP) is introduced for indexing and retrieval of biomedical images in this paper. A local bit-plane transformation scheme is proposed to compute the local bit-plane transformed values for each image pixel from the bit-plane binary contents of its each neighboring pixels. The introduced LBDP is generated by finding a binary pattern using the difference of centre pixel’s intensity value with the local bit-plane transformed values. The efficacy of LBDP is tested under biomedical image retrieval using average retrieval precision and average retrieval rate. Three benchmark databases Emphysema-CT, NEMA-CT and OASIS-MRI are used for the evaluation and comparison of the proposed approach with recent state-of-art methods. The experimental results confirm the discriminative ability and the efficiency of the proposed LBDP for biomedical image indexing and retrieval and prove the outperformance of existing biomedical image retrieval approaches. View full abstract»

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  • A Fully-Automatic Method for Gridding Bright Field Images of Bead Based Microarrays

    Publication Year: 2015 , Page(s): 1
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (21998 KB)  

    In this paper a fully automatic method for gridding bright field images of bead based microarrays is proposed. There have been numerous techniques developed for gridding fluorescence images of traditional spotted microarrays but to our best knowledge, no algorithm has yet been developed for gridding bright field images of bead based microarrays. The proposed gridding method is designed for automatic quality control during fabrication and assembly of bead based microarrays. The method begins by estimating the grid parameters using an evolutionary algorithm. This is followed by a grid fitting step that rigidly aligns an ideal grid with the image. Finally, a grid refinement step deforms the ideal grid to better fit the image. The grid fitting and refinement are performed locally and the final grid is a non-linear (piecewise affine) grid. To deal with extreme corruptions in the image, the initial grid parameter estimation and grid fitting steps employ robust search techniques. The proposed method does not have any free parameters that need tuning. The method is capable of identifying the grid structure even in the presence of extreme amounts of artifacts and distortions. Evaluation results on a variety of images are presented. View full abstract»

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  • Application of the Kalman Filter for Faster Strong Coupling of Cardiovascular Simulations

    Publication Year: 2015 , Page(s): 1
    Save to Project icon | Click to expandQuick Abstract | PDF file iconPDF (456 KB)  

    In this article, we propose a method for reducing the computational cost of strong coupling for multiscale cardiovascular simulation models. In such a model, individual model modules of myocardial cell, left ventricular structural dynamics, and circulatory hemodynamics are coupled. The strong coupling method enables stable and accurate calculation, but requires iterative calculations which are computationally expensive. The iterative calculations can be reduced, if accurate initial approximations are made available by predictors. The proposed method uses the Kalman filter to estimate accurate predictions by filtering out noise included in past values. The performance of the proposed method was assessed with an application to a previously published multiscale cardiovascular model. The proposed method reduced the number of iterations by 90% and 62% compared with no prediction and Lagrange extrapolation, respectively. Even when the parameters were varied and number of elements of the left ventricular finite element model increased, the number of iterations required by the proposed method was significantly lower than that without prediction. These results indicate the robustness, scalability, and validity of the proposed method. View full abstract»

    Open Access
  • Mahalanobis Taguchi System to Identify Pre-indicators of Delirium in the ICU

    Publication Year: 2015 , Page(s): 1
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (918 KB)  

    The research was designed to determine if the Mahalanobis Taguchi System (MTS) applied to the delirium evidence based bundle could detect medical patterns in retrospective data sets. The methodology defined the evidence based bundle as a multi-dimensional system that conformed to a parameter diagram. The mahalanobis distance (MD) was calculated for the retrospective healthy observations and the retrospective unhealthy observations. Signal to noise ratios (SNR) were calculated to determine the relative strength of detection of twenty three delirium pre-indicators. The research discovered that sufficient variation in the CAM-ICU assessment, the standard for delirium assessment, would benefit from knowledge of how different the MD is from the healthy average. The sensitivity of the detection system was 0.89 with a 95% confidence interval of between 0.84 and 0.92. The specificity of the detection system was 0.93 with a 95% confidence interval between 0.90 and 0.95. The MTS applied to the delirium evidence based bundle could detect medical patterns in retrospective data sets. The implication of this research to biomedical research is an automated decision support tool for the delirium evidence based bundle providing an early detection capability needed today. View full abstract»

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  • Plane Localization in 3D Fetal Neurosonography for Longitudinal Analysis of the Developing Brain

    Publication Year: 2015 , Page(s): 1
    Save to Project icon | Click to expandQuick Abstract | PDF file iconPDF (969 KB)  

    The para-sagittal (PS) plane is a 2D diagnostic plane used routinely in cranial ultrasonography of the neonatal brain. This paper develops a novel approach to find the PS plane in a 3D fetal ultrasound scan to allow image-based biomarkers to be tracked from pre-birth through the first weeks of post-birth life. We propose an accurate plane-finding solution based on regression forests (RF). The method initially localizes the fetal brain and its midline automatically. The midline on several axial slices is used to detect the mid-sagittal plane, which is used as a constraint in the proposed RF framework to detect the PS plane. The proposed learning algorithm guides the RF learning method in a novel way by 1) using informative voxels and voxel informative strength as a weighting within the training stage objective function, and 2) introducing regularization of the RF by proposing a geometrical feature within the training stage. Results on clinical data indicate that the new automated method is more reproducible than manual plane finding obtained by two clinicians. View full abstract»

    Open Access
  • Posture and Activity Recognition and Energy Expenditure Prediction in a Wearable Platform

    Publication Year: 2015 , Page(s): 1
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (722 KB)  

    The use of wearable sensors coupled with the processing power of mobile phones may be an attractive way to provide real-time feedback about physical activity and energy expenditure (EE). Here we describe the use of a shoe-based wearable sensor system (SmartShoe) with a mobile phone for real-time recognition of various postures/physical activities and the resulting EE. To deal with processing power and memory limitations of the phone, we compare use of Support Vector Machines (SVM), Multinomial Logistic Discrimination (MLD), and Multi-Layer Perceptrons (MLP) for posture and activity classification followed by activity-branched EE estimation. The algorithms were validated using data from 15 subjects who performed up to 15 different activities of daily living during a four-hour stay in a room calorimeter. MLD and MLP demonstrated activity classification accuracy virtually identical to SVM (~95%), while reducing the running time and the memory requirements by a factor of >103. Comparison of per-minute EE estimation using activity-branched models resulted in accurate EE prediction (RMSE=0.78 kcal/min for SVM and MLD activity classification, 0.77 kcal/min for MLP, vs. RMSE of 0.75 kcal/min for manual annotation). These results suggest that low-power computational algorithms can be successfully used for real-time physical activity monitoring and EE prediction on a wearable platform. View full abstract»

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  • The effects of cell asynchrony on gene expression levels: analysis and application to Plasmodium falciparum

    Publication Year: 2015 , Page(s): 1
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1453 KB)  

    To investigate the intraerythrocytic developmental cycle of Plasmodium falciparum, time-series gene expression data is commonly measured of infected red blood cells. However, the observed data is usually blurred due to cell asynchrony during experiments. In this paper, the effect of cell asynchrony is investigated by conducting numerical experiments. The simulation results suggest that cell asynchrony has varying effects on different intrinsic expression patterns. Specifically, the intrinsic patterns with high expression around the late life stage are more likely to be affected by cell asynchrony. It is also investigated how the effect of cell asynchrony depends on the experimental conditions. From this analysis, the burst rate r% in infection period and the standard deviation of growth rate are identified to have a strong impact on the blurring due to cell asynchrony. Consequently, it is important to measure these two parameters during biological experiments in order to deblur time-series gene expression data. View full abstract»

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  • Feature Selection Based on the SVM Weight Vector for Classification of Dementia

    Publication Year: 2015 , Page(s): 1
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (2986 KB)  

    Computer-aided diagnosis of dementia using a support vector machine (SVM) can be improved with feature selection. The relevance of individual features can be quantified from the SVM weights as a significance map (p-map). Although these p-maps previously showed clusters of relevant voxels in dementia-related brain regions, they have not yet been used for feature selection. Therefore, we introduce two novel feature selection methods based on p-maps using a direct approach (filter) and an iterative approach (wrapper). To evaluate these p-map feature selection methods, we compared them with methods based on the SVM weight vector directly, t-statistics and expert knowledge. We used MRI data from the Alzheimer’s Disease Neuroimaging Initiative classifying Alzheimer’s disease (AD) patients, mild cognitive impairment (MCI) patients who converted to AD (MCIc), MCI patients who did not convert to AD (MCInc), and cognitively normal controls (CN). Features for each voxel were derived from gray matter morphometry. Feature selection based on the SVM weights gave better results than t-statistics and expert knowledge. The p-map methods performed slightly better than those using the weight vector. The wrapper method scored better than the filter method. Recursive feature elimination based on the p-map improved most for AD-CN: the area under the receiver-operating-characteristic curve (AUC) significantly increased from 90.3% without feature selection to 92.0% when selecting 1.5%-3% of the features. This feature selection method also improved the other classifications: AD-MCI 0.1% improvement in AUC (not significant), MCI-CN 0.7%, and MCIc-MCInc 0.1% (not significant). Although the performance improvement due to feature selection was limited, the methods based on the p-map generally had the best performance and were therefore better in estimating the relevance of individual features. View full abstract»

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  • Estimating Energy Expenditure with Multiple Models using Different Wearable Sensors

    Publication Year: 2015 , Page(s): 1
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (656 KB)  

    This paper presents an approach to designing a method for the estimation of human energy expenditure (EE). The approach first evaluates different sensors and their combinations. After that, multiple regression models are trained utilising data from different sensors. The EE-estimation method designed in this way was evaluated on a dataset containing a wide range of activities. It was compared against three competing state-of-the-art approaches, including the BodyMedia Fit armband, the leading consumer EE estimation device. The results show that the proposed method outperforms the competition by up to 10.2 percentage points. View full abstract»

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  • Identifying Physical Activity Profiles in COPD Patients Using Topic Models

    Publication Year: 2015 , Page(s): 1
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (473 KB)  

    With the growing amount of physical activity (PA) measures, the need for methods and algorithms that automatically analyse and interpret unannotated data increases. In this paper PA is seen as a combination of multi-modal constructs that can co-occur in different ways and proportions during the day. The design of a methodology able to integrate and analyse them is discussed and its operation is illustrated by applying it to a data set comprising data from COPD patients and healthy subjects acquired in daily life. The method encompasses different stages. The first stage is a completely automated method of labelling low-level multi-modal PA measures. The information contained in the PA labels are further structured using topic modelling techniques, a machine learning method from the text processing community. The topic modelling discovers the main themes that pervade a large set of data. In our case, topic models discover PA routines that are active in the assessed days of the subjects under study. Applying the designed algorithm to our data provides new learnings and insights. As expected, the algorithm discovers that PA routines for COPD patients and healthy subjects are substantially different regarding their composition and moments in time in which transitions occur. Furthermore, it shows consistent trends relating to disease severity as measured by standard clinical practice. View full abstract»

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  • Regulatory Elements in Low-Methylated Regions Predict Directional Change of Gene Expression

    Publication Year: 2015 , Page(s): 1
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (5808 KB)  

    Recent studies on methylomes obtained from the whole genome bisulfite sequencing (WGBS) indicate that lowmethylated regions (LMRs) are related to potential active distal regulatory regions such as enhancers in mammalian genomes. To investigate the potential effect of regulatory elements in LMRs on gene expression, we proposed penalized logistic regression models to predict the directional change of differentially expressed genes using predicted transcription factor binding sites (TFBSs) in LMRs that are distinctive between two cell types. We evaluated our models on four cell types where the WGBS and RNA-seq data were available. The average area under the ROC curve (AUC) from the 10-fold cross-validation was computed over the 6 pairs of cell types in each model. The models using TFBSs in LMRs in intergenic or genebody region are more predictive (AUC 0.71 and 0.66 respectively) compared with the one using TFBSs from promoter regions alone (AUC 0.62). When using a model that combines TFBSs in LMRs from both intergenic and genebody regions, the best prediction was obtained (AUC 0.78). Our models are capable of identifying subsets of LMRs that are significantly enriched for the ChIP-seq binding sites of the insulator protein CTCF and p300 co-activator and other transcription factors. Our framework provides further evidences of putative distal regulatory elements from LMRs located in intergenic and genebody regions. View full abstract»

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  • Admission Control over Internet of Vehicles attached with Medical sensors for Ubiquitous Healthcare Applications

    Publication Year: 2015 , Page(s): 1
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (330 KB)  

    Wireless technologies and vehicle-mounted or wearable medical sensors are pervasive to support ubiquitous healthcare applications. However, a critical issue of using wireless communications under a healthcare scenario rests at the electromagnetic interference (EMI) caused by radio frequency (RF) transmission. A high level of EMI may lead to a critical malfunction of medical sensors, and in such a scenario, a few users who are not transmitting emergency data could be required to reduce their transmit power or even temporarily disconnect from the network in order to guarantee the normal operation of medical sensors as well as the transmission of emergency data. In this paper, we propose a joint power and admission control algorithm to schedule the users’ transmission of medical data. The objective of this algorithm is to minimize the number of users who are forced to disconnect from the network while keeping the EMI on medical sensors at an acceptable level. We show that a fixed point of proposed algorithm always exists, and at the fixed point, our proposed algorithm can minimize the number of low-priority users who are required to disconnect from the network. Numerical results illustrate that the proposed algorithm can achieve robust performance against the variations of mobile hospital environments. View full abstract»

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  • Automating Risk of Bias Assessment for Clinical Trials

    Publication Year: 2015 , Page(s): 1
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (6306 KB)  

    Systematic reviews, which summarize the entirety of the evidence pertaining to a specific clinical question, have become critical for evidence-based decision making in healthcare. But such reviews have become increasingly onerous to produce due to the exponentially expanding biomedical literature base. This work proposes a step toward mitigating this problem by automating risk of bias assessment in systematic reviews, in which reviewers determine whether study results may be affected by biases (e.g., poor randomization or blinding). Conducting risk of bias assessment is an important but onerous task. We thus describe a machine learning approach to automate this assessment, using the standard Cochrane Risk of Bias Tool which assesses seven common types of bias. Training such a system would typically require a large labeled corpus, which would be prohibitively expensive to collect here. Instead, we use distant supervision, using data from the Cochrane Database of Systematic Reviews (a large repository of systematic reviews), to pseudoannotate a corpus of 2,200 clinical trial reports in PDF format. We then develop a joint model which, using the full text of a clinical trial report as input, predicts the risks of bias while simultaneously extracting the text fragments supporting these assessments. This work represents a step toward automating or semi-automating extraction of data necessary for the synthesis of clinical trials. View full abstract»

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  • Detecting Elementary Arm Movements by Tracking Upper Limb Joint Angles with MARG Sensors

    Publication Year: 2015 , Page(s): 1
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    This paper reports an algorithm for the detection of three elementary upper limb movements i.e. reach and retrieve, bend the arm at the elbow and rotation of the arm about the long axis. We employ two MARG sensors, attached at the elbow and wrist, from which the kinematic properties (joint angles, position) of the upper arm and forearm are calculated through data fusion using a quaternion-based gradient-descent method and a 2-link model of the upper limb. By studying the kinematic patterns of the three movements on a small dataset, we derive discriminative features that are indicative of each movement; these are then used to formulate the proposed detection algorithm. Our novel approach of employing the joint angles and position to discriminate the three fundamental movements was evaluated in a series of experiments with 22 volunteers who participated in the study: 18 healthy subjects and 4 stroke survivors. In a controlled experiment, each volunteer was instructed to perform each movement a number of times. This was complimented by a semi-naturalistic experiment where the volunteers performed the same movements as subtasks of an activity that emulated the preparation of a cup of tea. In the stroke survivors group, the overall detection accuracy for all three movements was 93.75% and 83.00%, for the controlled and semi-naturalistic experiment respectively. The performance was higher in the healthy group where 96.85% of the tasks in the controlled experiment and 89.69% in the semi-naturalistic were detected correctly. Finally, the detection ratio remains close (6%) to the average value, for different task durations further attesting to the algorithms robustness. View full abstract»

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  • A Fuzzy Kernel Motion Classifier for Autonomous Stroke Rehabilitation

    Publication Year: 2015 , Page(s): 1
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (722 KB)  

    Autonomous post-stroke rehabilitation systems which can be deployed outside hospital with no or reduced supervision have attracted increasing amount of research attentions due to the high expenditure associated with the current inpatient stroke rehabilitation systems. To realize an autonomous systems, a reliable patient monitoring technique which can automatically record and classify patient’s motion during training sessions is essential. In order to minimize the cost and operational complexity, the combination of non-visual based inertia sensing devices and pattern recognition algorithms are often considered more suitable in such applications. However, the high motion irregularity due to stroke patients’ body function impairment has significantly increased the classification difficulty. A novel fuzzy kernel motion classifier specifically designed for stroke patient’s rehabilitation training motion classification is presented in this paper. The proposed classifier utilizes geometrically unconstrained fuzzy membership functions to address the motion class overlapping issue and thus it can achieve highly accurate motion classification even with poorly performed motion samples. In order to validate the performance of the classifier, experiments have been conducted using real motion data sampled from stroke patients with a wide range of impairment level and the results have demonstrated that the proposed classifier is superior in terms of error rate compared to other popular algorithms. View full abstract»

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  • Estimation of Respiratory Rate from Photoplethysmographic Imaging Videos Compared to Pulse Oximetry

    Publication Year: 2015 , Page(s): 1
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    We present a study evaluating two respiratory rate estimation algorithms using videos obtained from placing a finger on the camera lens of a mobile phone. The two algorithms, based on Smart Fusion and Empirical Mode Decomposition (EMD), consist of previously developed signal processing methods to detect features and extract respiratory induced variations in photoplethysmographic signals to estimate respiratory rate. With custom-built software on an Android phone, photoplethysmographic imaging videos were recorded from 19 healthy adults while breathing spontaneously at respiratory rates between 6 to 32 breaths/min. Signals from two pulse oximeters were simultaneously recorded to compare the algorithms’ performance using mobile phone data and clinical data. Capnometry was recorded to obtain reference respiratory rates. Two hundred seventy-two recordings were analyzed. The Smart Fusion algorithm reported 39 recordings with insufficient respiratory information from the photoplethysmographic imaging data. Of the 232 remaining recordings, a root mean square error (RMSE) of 6 breaths/min was obtained. The RMSE for the pulse oximeter data was lower at 2.3 breaths/min. RMSE for the EMD method was higher throughout all data sources as, unlike the Smart Fusion, the EMD method did not screen for inconsistent results. The study showed that it is feasible to estimate respiratory rates by placing a finger on a mobile phone camera, but that it becomes increasingly challenging at respiratory rates greater than 20 breaths/min, independent of data source or algorithm tested. View full abstract»

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  • A Robust Deep Model for Improved Classification of AD/MCI Patients

    Publication Year: 2015 , Page(s): 1
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    Accurate classification of Alzheimer’s Disease (AD) and its prodromal stage, Mild Cognitive Impairment (MCI), plays a critical role in possibly preventing progression of memory impairment and improving quality of life for AD patients. Among many research tasks, it is of particular interest to identify noninvasive imaging biomarkers for AD diagnosis. In this paper, we present a robust deep learning system to identify different progression stages of AD patients based on MRI and PET scans. We utilized the dropout technique to improve classical deep learning by preventing its weight co-adaptation, which is a typical cause of over-fitting in deep learning. In addition, we incorporated stability selection, an adaptive learning factor, and a multi-task learning strategy into the deep learning framework. We applied the proposed method to the ADNI data set and conducted experiments for AD and MCI conversion diagnosis. Experimental results showed that the dropout technique is very effective in AD diagnosis, improving the classification accuracies by 5.9% on average as compared to the classical deep learning methods. View full abstract»

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  • Automatic Localization of the Anterior Commissure, Posterior Commissure, and Midsagittal Plane in MRI Scans using Regression Forests

    Publication Year: 2015 , Page(s): 1
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1347 KB)  

    Localizing the anterior and posterior commissures (AC/PC) and the midsagittal plane (MSP) is crucial in stereotactic and functional neurosurgery, human brain mapping, and medical image processing. We present a learning-based method for auto-matic and efficient localization of these landmarks and the plane using regression forests. Given a point in an image, we first extract a set of multi-scale long-range contextual features. We then build random forests models to learn a nonlinear relationship between these features and the probability of the point being a landmark or in the plane. Three-stage coarse-to-fine models are trained for the AC, PC, and MSP separately using down-sampled by 4, down-sampled by 2, and the original images. Localization is per-formed hierarchically, starting with a rough estimation that is progressively refined. We evaluate our method using a leave-one-out approach with 100 clinical T1-weighted images and compare it to state-of-the-art methods including an atlas-based approach with six nonrigid registration algorithms and a mod-el-based approach for the AC and PC, and a global sym-metry-based approach for the MSP. Our method results in an overall error of 0.55±0.30mm for AC, 0.56±0.28mm for PC, 1.08˚±0.66˚ in the plane’s normal direction and 1.22±0.73 voxels in average distance for MSP; it performs significantly better than four registration algorithms and the model-based method for AC and PC, and the global symmetry-based method for MSP. We also evaluate the sensitivity of our method to image quality and pa-rameter values. We show that it is robust to asymmetry, noise, and rotation. Computation time is 25 seconds. View full abstract»

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  • Optimal MAP Parameters Estimation in STAPLE using Local Intensity Similarity Information

    Publication Year: 2015 , Page(s): 1
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (541 KB)  

    In recent years, fusing segmentation results obtained based on multiple template images has become a standard practice in many medical imaging applications; such multipletemplates- based methods are found to provide more reliable and accurate segmentations than the single-template-based methods. In this paper, we present a new approach for learning prior knowledge about the performance parameters of template images using the local intensity similarity information; we also propose a methodology to incorporate that prior knowledge through the estimation of the optimal MAP parameters. The proposed method is evaluated in the context of segmentation of structures in the brain Magnetic Resonance (MR) images by comparing our results with some of the state-of-the-art segmentation methods. These experiments have clearly demonstrated the advantages of learning and incorporating prior knowledge about the performance parameters using the proposed method. View full abstract»

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  • A Natural Walking Monitor for Pulmonary Patients using Mobile Phones

    Publication Year: 2015 , Page(s): 1
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (335 KB)  

    Mobile devices have the potential to continuously monitor health by collecting movement data including walking speed during natural walking. Natural walking is walking without artificial speed constraints present in both treadmill and nurse assisted walking. Fitness trackers have become popular which record steps taken and distance, typically using a fixed stride length. While useful for everyday purposes, medical monitoring requires precise accuracy and testing on real patients with a scientifically valid measure. Walking speed is closely linked to morbidity in patients and widely used for medical assessment via measured walking. The six-minute walk test (6MWT) is a standard assessment for chronic obstructive pulmonary disease (COPD) and congestive heart failure (CHF). Current generation smartphone hardware contains similar sensor chips as in medical devices and popular fitness devices. We developed a middleware software, MoveSense, which runs on standalone smartphones while providing comparable readings to medical accelerometers. We evaluate six machine learning methods to obtain gait speed during natural walking training models to predict natural walking speed and distance during a 6MWT with 28 pulmonary patients and 10 subjects without a pulmonary condition. We also compare our models accuracy to popular fitness devices. Our universally trained support vector machine models produce 6MWT distance with 3.23% error during a controlled 6MWT and 11.2% during natural free walking. Furthermore, our model attains 7.9% error when tested on five subjects for distance estimation compared to the 50-400% error seen in fitness devices during natural walking. View full abstract»

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  • Detecting Subclinical Diabetic Cardiac Autonomic Neuropathy by Analysing Ventricular Repolarization Dynamics

    Publication Year: 2015 , Page(s): 1
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    In this study, a linear parametric modeling technique was applied to model ventricular repolarization (VR) dynamics. Three features were selected from the surface ECG recordings to investigate the changes in VR dynamics in healthy and cardiac autonomic neuropathy (CAN) participants with diabetes including heart rate variability (calculated from RR intervals), repolarization variability (calculated from QT intervals) and respiration (calculated by ECG derived respiration (EDR. Surface ECGs were recorded in a supine resting position from 80 age-matched participants (40 with no cardiac autonomic neuropathy (NCAN) and 40 with CAN). In the CAN group, 25 participants had early/subclinical CAN (ECAN) and 15 participants were identified with definite/clinical CAN (DCAN). Detecting subclinical CAN is crucial for designing an effective treatment plan to prevent further cardiovascular complications. For CAN diagnosis, VR dynamics was analyzed using linear parametric autoregressive bivariate (ARXAR) and trivariate (ARXXAR) models, which were estimated using 250 beats of derived QT, RR and EDR time series extracted from the first 5 minutes of the recorded ECG signal. Results showed that the EDR based models gave a significantly higher fitting value (p<0.0001) than models without EDR, which indicates that QT-RR dynamics is better explained by respiratory information based models. Moreover, the QT-RR-EDR model values gradually decreased from the NCAN group to ECAN and DCAN groups, which indicate a decoupling of QT from RR and the respiration signal with the increase in severity of CAN. In this study, only the EDR based model significantly distinguished ECAN and DCAN groups from the NCAN group (p<0.05) with large effect sizes (Cohen’s d>0.75) showing the effectiveness of this modeling technique in detecting subclinical CAN. In conclusion, the EDR based trivariate QT-RR-EDR model was found to be better in detecting the presence and severity of CAN than the bivariate- QTRR model. This finding also establishes the importance of adding respiratory information for analyzing the gradual deterioration of normal VR dynamics in pathological conditions such as diabetic CAN. View full abstract»

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  • Fall Detection using Smartphone Audio Features

    Publication Year: 2015 , Page(s): 1
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    An automated fall detection system based on smartphone audio features is developed. The spectrogram, mel frequency cepstral coefficents (MFCCs), linear predictive coding (LPC) and matching pursuit (MP) features of different fall and no-fall sound events are extracted from experimental data. Based on the extracted audio features, four different machine learning classifiers: k-nearest neighbor classifier (k-NN), support vector machine (SVM), least squares method (LSM) and artificial neural network (ANN) are investigated for distinguishing between fall and no-fall events. For each audio feature, the performance of each classifier in terms of sensitivity, specificity, accuracy and computational complexity is evaluated. The best performance is achieved using spectrogram features with ANN classifier with sensitivity, specificity and accuracy all above 98%. The classifier also has acceptable computational requirement for training and testing. The system is applicable in home environments where the phone is placed in the vicinity of the user. View full abstract»

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  • Standard Plane Localization in Fetal Ultrasound via Domain Transferred Deep Neural Networks

    Publication Year: 2015 , Page(s): 1
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    Automatic localization of the standard plane containing complicated anatomical structures in ultrasound (US) videos remains a challenging problem. In this paper, we present a learning based approach to locate the fetal abdominal standard plane (FASP) in US videos by constructing a domain transferred deep convolutional neural network (CNN). Compared with previous works based on low-level features, our approach is able to represent the complicated appearance of the FASP and hence achieve better classification performance. More importantly, in order to reduce the overfitting problem caused by the small amount of training samples, we propose a transfer learning strategy, which transfers the knowledge in the low layers of a base CNN trained from a large database of natural images to our task-specific CNN. Extensive experiments demonstrate that our approach outperforms the state-of-the-art method for the FASP localization as well as the CNN only trained on the limited US training samples. The proposed approach can be easily extended to other similar medical image computing problems, which often suffer from the insufficient training samples when exploiting the deep CNN to represent high-level features. View full abstract»

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  • A Multidimensional Time Series Similarity Measure with Applications to Eldercare Monitoring

    Publication Year: 2015 , Page(s): 1
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    n the last decade, data mining techniques have been applied to sensor data in a wide range of application domains, such as healthcare monitoring systems, manufacturing processes, intrusion detection, database management and others. Many data mining techniques are based on computing the similarity between two sensor data patterns. A variety of representations and similarity measures for multi-attribute time series have been proposed in literature. In this paper, we describe a novel method for computing the similarity of two multi-attribute time series based on a temporal version of Smith-Waterman (SW), a wellknown bioinformatics algorithm. We then apply our method to sensor data from an eldercare application for early illness detection. Our method mitigates difficulties related to data uncertainty and aggregation that often arise when processing sensor data. The experiments take place at an aging-in-place facility, TigerPlace, located in Columbia, MO. To validate our method we used data from nine non-wearable sensor networks placed in TigerPlace apartments, combined with information from an Electronic Health Record (EHR). We provide a set of experiments that investigate temporal version of SW properties, together with experiments on TigerPlace datasets. On a pilot sensor dataset from nine residents, with a total of 1902 days and around 2.1 million sensor hits of collected data, we obtained an average abnormal events prediction F-measure of 0.75. View full abstract»

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  • Modeling of an Optimized Electro-Stimulative Hip Revision System Under Consideration of Uncertainty in the Conductivity of Bone Tissue

    Publication Year: 2015 , Page(s): 1
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    Since several years, the number of total hip arthroplasty (THA) revision surgeries is substantially growing. One of the main reasons for this procedure to become necessary is the loosening or damage of the prothesis, which is facilitated by bone necrosis at the implant-bone-interface. Electro-stimulation is one promising technique, which can accelerate the growth of bone cells and, therefore, enhance the anchorage of the implant to the bone. We present computational models of an electro-stimulative total hip revision system to enhance bone regeneration. In the present work, the influence of uncertainty in the conductivity of bone tissue on the electric field strength and the beneficial stimulation volume for an optimized electrode geometry and arrangement is investigated. The generalized Polynomial Chaos technique is used to quantify the uncertainty in the stimulation volumes with respect to the uncertain conductivity of cancellous bone, bone marrow, and bone substitute, which is used to fill defective areas. The results suggest that the overall beneficial stimulation areas are only slightly sensitive to the uncertainty in conductivity of bone tissue. However, in the proximity of tissue boundaries, larger uncertainties, especially in the transition between beneficial and understimulation areas, can be expected. 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