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IEEE Journal of Biomedical and Health Informatics

Shuang Yu, Di Xiao, Yogesan Kanagasingam
In this paper, the automatic detection of neovascularization in the optic disc region (NVD) for color fundus retinal image is presented. NV is the indicator for the onset of proliferative diabetic retinopathy and it is featured by the presence of new vessels in the retina. The new vessels are fragile and pose a high risk for sudden vision loss. Therefore, the importance of accurate and timely detection of NV cannot be underestimated. We propose an automatic image processing procedure for NVD detection that involves vessel segmentation using multilevel Gabor filtering, feature extraction of vessel morphological features and texture features, and image classification with support vector machine...
May 2018: IEEE Journal of Biomedical and Health Informatics
Robespierre Pita, Clicia Pinto, Samila Sena, Rosemeire Fiaccone, Leila Amorim, Sandra Reis, Mauricio L Barreto, Spiros Denaxas, Marcos Ennes Barreto
Data linkage refers to the process of identifying and linking records that refer to the same entity across multiple heterogeneous data sources. This method has been widely utilized across scientific domains, including public health where records from clinical, administrative, and other surveillance databases are aggregated and used for research, decision making, and assessment of public policies. When a common set of unique identifiers does not exist across sources, probabilistic linkage approaches are used to link records using a combination of attributes...
March 2018: IEEE Journal of Biomedical and Health Informatics
Kyle N Winfree, Gregory Dominick
Consumer-grade wearable activity devices such as Fitbits are increasingly being used in research settings to promote physical activity (PA) due to their low-cost and widespread popularity. However, Fitbit-derived measures of activity intensity are consistently reported to be less accurate than intensity estimates obtained from research-grade accelerometers (i.e., ActiGraph). As such, the potential for using a Fitbit to measure PA intensity within research contexts remains limited. This study aims to model ActiGraph-based intensity estimates from the validated Freedson vector magnitude (VM3) algorithm using measures of steps, metabolic equivalents, and intensity levels obtained from Fitbit...
March 2018: IEEE Journal of Biomedical and Health Informatics
Chen Song, Aosen Wang, Feng Lin, Mohammadnabi Asmani, Ruogang Zhao, Zhanpeng Jin, Jian Xiao, Wenyao Xu
As a micro-engineered biomimetic system to replicate key functions of living organs, organ-on-a-chip (OC) technology provides a high-throughput model for investigating complex cell interactions with both high temporal and spatial resolutions in biological studies. Typically, microscopy and high-speed video cameras are used for data acquisition, which are expensive and bulky. Recently, compressed sensing (CS) has increasingly attracted attentions due to its extremely low-complexity structure and low sampling rate...
March 2018: IEEE Journal of Biomedical and Health Informatics
William J Allen, Refaat E Gabr, Getaneh B Tefera, Amol S Pednekar, Matthew W Vaughn, Ponnada A Narayana
Biomedical data are quickly growing in volume and in variety, providing clinicians an opportunity for better clinical decision support. Here, we demonstrate a robust platform that uses software automation and high performance computing (HPC) resources to achieve real-time analytics of clinical data, specifically magnetic resonance imaging (MRI) data. We used the Agave application programming interface to facilitate communication, data transfer, and job control between an MRI scanner and an off-site HPC resource...
March 2018: IEEE Journal of Biomedical and Health Informatics
Glen Wright Colopy, Stephen J Roberts, David A Clifton
Gaussian process regression (GPR) provides a means to generate flexible personalized models of time series of patient vital signs. These models can perform useful clinical inference in ways that population-based models cannot. A challenge for the use of personalized models is that they must be amenable to a wide range of parameterizations, to accommodate the plausible physiology of any patient in the population. Additionally, optimal performance is typically achieved when models are regularized in light of the knowledge of the physiology of the individual patient...
March 2018: IEEE Journal of Biomedical and Health Informatics
Hui Yan, Pieter Van Gorp, Uzay Kaymak, Xudong Lu, Lei Ji, Choo Chiap Chiau, Hendrikus H M Korsten, Huilong Duan
Clinical pathways (CPs) are popular healthcare management tools to standardize care and ensure quality. Analyzing CP compliance levels and variances is known to be useful for training and CP redesign purposes. Flexible semantics of the business process model and notation (BPMN) language has been shown to be useful for the modeling and analysis of complex protocols. However, in practical cases one may want to exploit that CPs often have the form of task-time matrices. This paper presents a new method parsing complex BPMN models and aligning traces to the models heuristically...
March 2018: IEEE Journal of Biomedical and Health Informatics
Paul V Nickerson, Raheleh Baharloo, Amal A Wanigatunga, Todd M Manini, Patrick J Tighe, Parisa Rashidi
The modern healthcare landscape has seen the rapid emergence of techniques and devices that temporally monitor and record physiological signals. The prevalence of time-series data within the healthcare field necessitates the development of methods that 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...
March 2018: IEEE Journal of Biomedical and Health Informatics
Yang Li, Xu-Dong Wang, Mei-Lin Luo, Ke Li, Xiao-Feng Yang, Qi Guo
The automatic detection of epileptic seizures from electroencephalography (EEG) signals is crucial for the localization and classification of epileptic seizure activity. However, seizure processes are typically dynamic and nonstationary, and thus, distinguishing rhythmic discharges from nonstationary processes is one of the challenging problems. In this paper, an adaptive and localized time-frequency representation in EEG signals is proposed by means of multiscale radial basis functions (MRBF) and a modified particle swarm optimization (MPSO) to improve both time and frequency resolution simultaneously, which is a novel MRBF-MPSO framework of the time-frequency feature extraction for epileptic EEG signals...
March 2018: IEEE Journal of Biomedical and Health Informatics
Fabian Prasser, Florian Kohlmayer, Helmut Spengler, Klaus A Kuhn
The sharing of sensitive personal health data is an important aspect of biomedical research. Methods of data de-identification are often used in this process to trade the granularity of data off against privacy risks. However, traditional approaches, such as HIPAA safe harbor or -anonymization, often fail to provide data with sufficient quality. Alternatively, data can be de-identified only to a degree which still allows us to use it as required, e.g., to carry out specific analyses. Controlled environments, which restrict the ways recipients can interact with the data, can then be used to cope with residual risks...
March 2018: IEEE Journal of Biomedical and Health Informatics
Julius Hannink, Thomas Kautz, Cristian F Pasluosta, Jens Barth, Samuel Schulein, Karl-Gunter GaBmann, Jochen Klucken, Bjoern M Eskofier
OBJECTIVE: Accurate estimation of spatial gait characteristics is critical to assess motor impairments resulting from neurological or musculoskeletal disease. Currently, however, methodological constraints limit clinical applicability of state-of-the-art double integration approaches to gait patterns with a clear zero-velocity phase. METHODS: We describe a novel approach to stride length estimation that uses deep convolutional neural networks to map stride-specific inertial sensor data to the resulting stride length...
March 2018: IEEE Journal of Biomedical and Health Informatics
Ankur Agarwal, Christopher Baechle, Ravi Behara, Xingquan Zhu
With the passage of recent federal legislation, many medical institutions are now responsible for reaching target hospital readmission rates. Chronic diseases account for many hospital readmissions and chronic obstructive pulmonary disease has been recently added to the list of diseases for which the United States government penalizes hospitals incurring excessive readmissions. Though there have been efforts to statistically predict those most in danger of readmission, a few have focused primarily on unstructured clinical notes...
March 2018: IEEE Journal of Biomedical and Health Informatics
Hamdi Dibeklioglu, Zakia Hammal, Jeffrey F Cohn
Depression is one of the most common psychiatric disorders worldwide, with over 350 million people affected. Current methods to screen for and assess depression depend almost entirely on clinical interviews and self-report scales. While useful, such measures lack objective, systematic, and efficient ways of incorporating behavioral observations that are strong indicators of depression presence and severity. Using dynamics of facial and head movement and vocalization, we trained classifiers to detect three levels of depression severity...
March 2018: IEEE Journal of Biomedical and Health Informatics
Jongchan Lee, Zahra Ghasemi, Chang-Sei Kim, Hao-Min Cheng, Chen-Huan Chen, Shih-Hsien Sung, Ramakrishna Mukkamala, Jin-Oh Hahn
We investigated the relationship between carotid artery blood pressure (BP) and distal pulse volume waveforms (PVRs) via subject-specific mathematical modeling. We conceived three physical models to define the relationship: a tube-load model augmented with a gain (TLG), Voigt (TLV), and standard linear solid (TLS) models. We compared these models using PVRs measured via BP cuffs at an upper arm and an ankle as well as carotid artery tonometry waveform collected from 133 subjects. At both upper arm and ankle, PVR was related to carotid artery tonometry by TLV and TLS models better than by TLG model; when root-mean-squared over all the subjects, the systolic and diastolic BP errors between measured carotid artery tonometry waveform and the one estimated from distal PVR reduced from 4...
March 2018: IEEE Journal of Biomedical and Health Informatics
Jose Manuel Bote, Joaquin Recas, Francisco Rincon, David Atienza, Roman Hermida
This work presents a new modular and low-complexity algorithm for the delineation of the different ECG waves (QRS, P and T peaks, onsets, and end). Involving a reduced number of operations per second and having a small memory footprint, this algorithm is intended to perform real-time delineation on resource-constrained embedded systems. The modular design allows the algorithm to automatically adjust the delineation quality in runtime to a wide range of modes and sampling rates, from a ultralow-power mode when no arrhythmia is detected, in which the ECG is sampled at low frequency, to a complete high-accuracy delineation mode, in which the ECG is sampled at high frequency and all the ECG fiducial points are detected, in the case of arrhythmia...
March 2018: IEEE Journal of Biomedical and Health Informatics
Xiaojin Li, Licong Cui, Shiqiang Tao, Jing Chen, Xiang Zhang, Guo-Qiang Zhang
Automatic identification of sleep stage is an important step in a sleep study. In this paper, we propose a hybrid automatic sleep stage scoring approach, named HyCLASSS, based on single channel electroencephalogram (EEG). HyCLASSS, for the first time, leverages both signal and stage transition features of human sleep for automatic identification of sleep stages. HyCLASSS consists of two parts: A random forest classifier and correction rules. Random forest classifier is trained using 30 EEG signal features, including temporal, frequency, and nonlinear features...
March 2018: IEEE Journal of Biomedical and Health Informatics
Priyanka Mandal, Krishna Tank, Tapas Mondal, Chih-Hung Chen, M Jamal Deen
A simple, low-power and wearable health analyzer for early identification and management of some diseases is presented. To achieve this goal, we propose a walking pattern analysis system that uses features, such as speed, energy, turn ratio, and bipedal behavior to characterize and classify individuals in distinct walking-ages. A database is constructed from 74 healthy young adults in the age range from 18 to 60 years using the combination of inertial signals from an accelerometer and a gyroscope on a level path including turns...
March 2018: IEEE Journal of Biomedical and Health Informatics
Dongping Du, Hui Yang, Andrew R Ednie, Eric S Bennett
Cardiac ion channels are highly glycosylated membrane proteins with up to 30% of the protein's mass containing glycans. Heart diseases often accompany individuals with congenital disorders of glycosylation (CDG). However, cardiac dysfunction among CDG patients is not yet fully understood. There is an urgent need to study how aberrant glycosylation impacts cardiac electrical signaling. Our previous works reported that congenitally reduced sialylation achieved through deletion of the sialyltransferase gene, ST3Gal4, leads to altered gating of voltage-gated Na+ and K+ channels ( and , respectively)...
March 2018: IEEE Journal of Biomedical and Health Informatics
Xuechen Li, Linlin Shen, Suhuai Luo
Lung cancer is one of the most deadly diseases. It has a high death rate and its incidence rate has been increasing all over the world. Lung cancer appears as a solitary nodule in chest x-ray radiograph (CXR). Therefore, lung nodule detection in CXR could have a significant impact on early detection of lung cancer. Radiologists define a lung nodule in CXR as "solitary white nodule-like blob." However, the solitary feature has not been employed for lung nodule detection before. In this paper, a solitary feature-based lung nodule detection method was proposed...
March 2018: IEEE Journal of Biomedical and Health Informatics
Chia-Hsiang Wu, Wan-Hua Tsai, Ying-Hui Chen, Jia-Kuang Liu, Yung-Nien Sun
For better treatment outcomes, dentists usually use a set of parameters for orthodontic evaluation. In this study, a new method is proposed to assist dentists in obtaining reliable assessment of these parameters. The proposed method is based on dental panoramic radiographs and can be divided into four stages: image preprocessing, model training, tooth segmentation, and assessment of orthodontic parameters. The image is first normalized and enhanced. Then, the model training stage consists of shape and image model training, energy function training, and weight training...
March 2018: IEEE Journal of Biomedical and Health Informatics
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