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IEEE Journal of Biomedical and Health Informatics

Munendra Singh, Ashish Verma, Neeraj Sharma
Magnetic resonance imaging (MRI) is the modality of choice as far as imaging diagnosis of pathologies in the pituitary gland is concerned. Further, the advent of dynamic contrast enhanced (DCE) has enhanced the capability of this modality in detecting minute benign but endocrinologically significant tumors called microadenoma. These lesions are visible with difficulty and a low confidence level in routine MRI sequences, even after administration of intravenous gadolinium. Techniques to enhance the visualization of such foci would be an asset in improving the overall accuracy of DCE-MRI for detection of pituitary microadenomas...
June 13, 2017: IEEE Journal of Biomedical and Health Informatics
Rinku Rabidas, Abhishek Midya, Jayasree Chakraborty
In this paper, two novel feature extraction methods, using Neighborhood Structural Similarity (NSS), are proposed for the characterization of mammographic masses as benign or malignant. Since the gray-level distribution of pixels is different in benign and malignant masses; more regular and homogeneous patterns are visible in benign masses compared to malignant masses, the proposed method exploits the similarity between neighboring regions of masses by designing two new features, namely NSS-I and NSS-II, which capture global similarity at different scales...
June 13, 2017: IEEE Journal of Biomedical and Health Informatics
Young-Zoon Yoon, Jae Min Kang, Yongjoo Kwon, Sangyun Park, Seungwoo Noh, Younho Kim, Jongae Park, Sung Woo Hwang
Using the massive MIMIC physiological database, we tried to validate pulse wave analysis (PWA) based on multi-parameters model whether it can continuously estimate blood pressure (BP) values on single site of one hand. In addition, to consider the limitation of insufficient data acquirement for home user, we used pulse arrival time (PAT)-driven BP information to determine the individual scale factors of the PWA-BP estimation model. Experimental results indicate that the accuracy of the average regression model has error standard deviations of 10...
June 12, 2017: IEEE Journal of Biomedical and Health Informatics
Filip Velickovski, Robert Marti, Luigi Ceccaroni, Felip Burgos, Concepcion Gistau, Xavier Alsina-Restoy, Josep Roca
Forced spirometry testing is gradually becoming available across different healthcare tiers including primary care. It has been demonstrated in earlier work that commercially available spirometers are not fully able to assure the quality of individual spirometry manoeuvres. Thus a need to expand the availability of high quality spirometry assessment beyond specialist pulmonary centres has arisen. In this work we propose a method to select and optimise a classifier using supervised learning techniques by learning from previously classified forced spirometry tests from a group of experts...
June 8, 2017: IEEE Journal of Biomedical and Health Informatics
Arnaud Moreau, Peter Anderer, Marco Ross, Andreas Cerny, Timothy Almazan, Barry Peterson
Atopic dermatitis is a chronic inflammatory skin condition affecting both children and adults and is associated with pruritus. A method for objectively quantifying nocturnal scratching events could aid in the development of therapies for atopic dermatitis and other pruritic disorders. High-resolution wrist actigraphy (3-D accelerometer sensors sampled at 20 Hz) is a non-invasive method to record movement. This work presents an algorithm to detect nocturnal scratching events based on actigraphy data. The twofold process consists of segmenting the data into "no motion", "single handed motion" and "both handed motion" followed by discriminating motion segments into scratching and other motion using a bi-directional recurrent neural network classifier...
June 8, 2017: IEEE Journal of Biomedical and Health Informatics
Hee Nam Yoon, Su Hwan Hwang, Jae Won Choi, Yu Jin Lee, Do Un Jeong, Kwang Suk Park
We developed an automatic slow-wave sleep (SWS) detection algorithm that can be applied to groups of healthy subjects and patients with obstructive sleep apnea (OSA). This algorithm detected SWS based on autonomic activations derived from the heart rate variations of a single sensor. An autonomic stability, which is an SWS characteristic, was evaluated and quantified using R-R intervals from an electrocardiogram (ECG). The thresholds and the heuristic rule to determine SWS were designed based on the physiological backgrounds for sleep process and distribution across the night...
June 7, 2017: IEEE Journal of Biomedical and Health Informatics
Ahmad Shalbaf, Mohsen Saffar, Jamie W Sleigh, Reza Shalbaf
Accurate and noninvasive monitoring of the depth of anesthesia (DoA) is highly desirable. Since the anesthetic drugs act mainly on the central nervous system, the analysis of brain activity using electroencephalogram (EEG) is very useful. This paper proposes a novel automated method for assessing the DoA using EEG. Firstly, 11 features including spectral, fractal and entropy are extracted from EEG signal and then, by applying an algorithm according to exhaustive search of all subsets of features, a combination of the best features (Beta-index, sample entropy, shannon permutation entropy and detrended fluctuation analysis) is selected...
May 29, 2017: IEEE Journal of Biomedical and Health Informatics
Yangzhou Gan, Zeyang Xia, Jing Xiong, Guanglin Li, Qunfei Zhao
Three-dimensional (3D) models of tooth-alveolar bone complex are needed in treatment planning and simulation for computer-aided orthodontics. Tooth and alveolar bone segmentation from computed tomography (CT) images is a fundamental step in reconstructing their models. Due to less application of alveolar bone in conventional orthodontic treatment which may cause undesired side effects, the previous studies mainly focused on tooth segmentation and reconstruction and did not consider the alveolar bone. In this study, we proposed a method to implement both tooth and alveolar bone segmentation from dental CT images for reconstructing their 3D models...
May 29, 2017: IEEE Journal of Biomedical and Health Informatics
Andreas Tobola, Heike Leutheuser, Markus Pollak, Peter Spies, Christian Hofmann, Christian Weigand, Bjoern M Eskofier, Georg Fischer
Wearable health sensors are about to change our health system. While several technological improvements have been presented to enhance performance and energy-efficiency, battery runtime is still a critical concern for practical use of wearable biomedical sensor systems. The runtime limitation is directly related to the battery size, which is another concern regarding practicality and customer acceptance. We introduced ULPSEK-Ultra-Low-Power Sensor Evaluation Kit- for evaluation of biomedical sensors and monitoring applications (http://ulpsek...
May 25, 2017: IEEE Journal of Biomedical and Health Informatics
Zhe-Xiao Guo, Guo Dan, Jianghuai Xiang, Jun Wang, Wanzhang Yang, Huijun Ding, Oliver Deussen, Yongjin Zhou
Unilateral peripheral facial paralysis (UPFP) is a form of facial nerve paralysis and clinically classified according to conditions of facial symmetry. Prompt and precise assessment is crucial to neural rehabilitation of UPFP. The prevalent House-Brackmann (HB) grading system relies on subjective judgments with significant inter-observation variation. Therefore to explore an objective method for UPFP assessment, clinical image sequences are captured using a web camera setup while 5 healthy and 27 UPFP subjects performing a group of pre-defined actions, including keeping expressionless, raising brows, closing eyes, bulging cheek and showing teeth in turn...
May 24, 2017: IEEE Journal of Biomedical and Health Informatics
Hamed Danandeh Hesar, Maryam Mohebbi
Model-based Bayesian frameworks have a common problem in processing electrocardiogram (ECG) signals with sudden morphological changes. This situation often happens in the case of arrhythmias where ECGs don't obey the predefined state models. To solve this problem, in this paper, a model-based Bayesian denoising framework is proposed using marginalized particle-extended Kalman filter (MP-EKF), Variational Mode Decomposition (VMD) and a novel fuzzy-based adaptive particle weighting strategy. This strategy helps MP-EKF to perform well even when the morphology of signal does not comply with the predefined dynamic model...
May 19, 2017: IEEE Journal of Biomedical and Health Informatics
Ling Zhang, Le Lu, Isabella Nogues, Ronald Summers, Shaoxiong Liu, Jianhua Yao
Automation-assisted cervical screening via Pap smear or liquid-based cytology (LBC) is a highly effective cell imaging based cancer detection tool, where cells are partitioned into "abnormal" and "normal" categories. However, the success of most traditional classification methods relies on the presence of accurate cell segmentations. Despite sixty years of research in this field, accurate segmentation remains a challenge in the presence of cell clusters and pathologies. Moreover, previous classification methods are only built upon the extraction of hand-crafted features, such as morphology and texture...
May 19, 2017: IEEE Journal of Biomedical and Health Informatics
Siqi Li, Huiyan Jiang, Yu-Dong Yao, Benqiang Yang
Organ segmentation on computed tomography (CT) images is of great importance in medical diagnoses and treatment. This paper proposes organ location determination and contour sparse representation methods (OLD-CSR) for multiorgan segmentation (liver, kidney, and spleen) on abdomen CT images using an extreme learning machine (ELM) classifier. First, a location determination method is designed to obtain location information of each organ, which is used for coarse segmentation. Second, for coarse-to-fine segmentation, a contour gradient and rate change based feature point extraction method is proposed...
May 17, 2017: IEEE Journal of Biomedical and Health Informatics
Alok Chowdhury, Dian Tjondronegoro, Vinod Chandran, Stewart Trost
This paper proposes the use of posterior-adapted class-based weighted decision fusion to effectively combine multiple accelerometers data for improving physical activity recognition. The cutting-edge performance of this method is benchmarked against model-based weighted fusion and class-based weighted fusion without posterior adaptation, based on two publicly available datasets, namely PAMAP2 and MHEALTH. Experimental results show that: (a) posterior-adapted class-based weighted fusion outperformed model-based and class-based weighted fusion; (b) decision fusion with two accelerometers showed statistically significant improvement in average performance compared to the use of a single accelerometer;...
May 17, 2017: IEEE Journal of Biomedical and Health Informatics
Zhen Yu, Ee-Leng Tan, Dong Ni, Jing Qin, Siping Chen, Shenli Li, Baiying Lei, Tianfu Wang
Ultrasound imaging has become a prevalent examination method in prenatal diagnosis. Accurate acquisition of fetal facial standard plane (FFSP) is the most important precondition for subsequent diagnosis and measurement. In the past few years, considerable effort has been devoted to FFSP recognition using various hand-crafted features, but the recognition performance is still unsatisfactory due to the high intra-class variation of FFSPs and the high degree of visual similarity between FFSPs and other non-FFSPs...
May 17, 2017: IEEE Journal of Biomedical and Health Informatics
Suvra Pal, N Balakrishnan
In this paper, we develop likelihood inference based on the expectation maximization (EM) algorithm for the Box- Cox transformation cure rate model assuming the lifetimes to follow a Weibull distribution. A simulation study is carried out to demonstrate the performance of the proposed estimation method. Through Monte Carlo simulations, we also study the effect of model mis-specification on the estimate of cure rate. Finally, we analyze a well-known data on melanoma with the model and the inferential method developed here...
May 16, 2017: IEEE Journal of Biomedical and Health Informatics
Jun Zhang, Mingxia Liu, Le An, Yaozong Gao, Dinggang Shen
Structural magnetic resonance imaging (MRI) has been proven to be an effective tool for Alzheimer's disease (AD) diagnosis. While conventional MRI-based AD diagnosis typically uses images acquired at a single time point, a longitudinal study is more sensitive in detecting early pathological changes of AD, making it more favorable for accurate diagnosis. In general, there are two challenges faced in MRI-based diagnosis. First, extracting features from structural MR images requires timeconsuming nonlinear registration and tissue segmentation, whereas the longitudinal study with involvement of more scans further exacerbates the computational costs...
May 16, 2017: IEEE Journal of Biomedical and Health Informatics
Paul Nickerson, Raheleh Baharloo, Amal A Wanigatunga, Todd D Manini, Patrick J Tighe, Parisa Rashidi
The modern healthcare landscape has seen the rapid emergence of techniques and devices which temporally monitor and record physiological signals. The prevalence of time series data within the healthcare field necessitates the development of methods which can analyze the data in order to draw meaningful conclusions. Time series behavior is notoriously difficult to intuitively understand due to its intrinsic high-dimensionality, which is compounded in the case of analyzing groups of time series collected from different patients...
May 16, 2017: IEEE Journal of Biomedical and Health Informatics
Sidra Minhas, Aasia Khanum, Farhan Riaz, Shoab Khan, Atif Alvi
Mild Cognitive Impairment is a preclinical stage of Alzheimer's disease (AD). For effective treatment of AD, it is important to identify MCI patients who are at a high risk of developing AD over the course of time. İn this study, autoregressive modelling of multiple heterogeneous predictors of Alzheimer's dısease is performed to capture their evolution over time. The models are trained using three different arrangements of longitudinal data. These models are then used to estimate future biomarker readings of individual test subjects...
May 16, 2017: IEEE Journal of Biomedical and Health Informatics
Shuxia Zhu, Fei Shi, Dehui Xiang, Weifang Zhu, Haoyu Chen, Xinjian Chen
Choroid neovascularization (CNV) is caused by new blood vessels growing in the choroid and penetrating the Bruch membrane. It is the major cause of vision disability in many retinal diseases. Though anti-vascular endothelial growth factor (VEGF) injection has proved to be effective for treating CNV, treatment planning is essential to ensure the efficacy while reducing the risk. For this purpose, we propose a CNV growth model based on longitudinal Optical Coherence Tomography (OCT) images. The reaction-diffusion model is applied to simulate the growth and shrinkage of CNV volumes, and is solved by using finite element method (FEM)...
May 16, 2017: IEEE Journal of Biomedical and Health Informatics
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