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IEEE Journal of Biomedical and Health Informatics

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https://read.qxmd.com/read/30762573/fully-convolutional-networks-for-monocular-retinal-depth-estimation-and-optic-disc-cup-segmentation
#1
Sharath M Shankaranarayana, Keerthi Ram, Kaushik Mitra, Mohanasankar Sivaprakasam
Glaucoma is a serious ocular disorder for which the screening and diagnosis are carried out by the examination of the optic nerve head (ONH). The color fundus image (CFI) is the most common modality used for ocular screening. In CFI, the central region which is the optic disc and the optic cup region within the disc are examined to determine one of the important cues for glaucoma diagnosis called the optic cup-to-disc ratio (CDR). CDR calculation requires accurate segmentation of optic disc and cup. Another important cue for glaucoma progression is the variation of depth in ONH region...
February 14, 2019: IEEE Journal of Biomedical and Health Informatics
https://read.qxmd.com/read/30762572/discovering-the-type-2-diabetes-in-electronic-health-records-using-the-sparse-balanced-support-vector-machine
#2
Michele Bernardini, Luca Romeo, Paolo Misericordia, Emanuele Frontoni
The diagnosis of Type 2 Diabetes (T2D) at an early stage has a key role for an adequate T2D integrated management system and patient's follow-up. Recent years have witnessed an increasing amount of available Electronic Health Record (EHR) data and Machine Learning (ML) techniques have been considerably evolving. However, managing and modeling this amount of information may lead to several challenges such as overfitting, model interpretability and computational cost. Starting from these motivations, we introduced a ML method called Sparse Balanced Support Vector Machine (SB-SVM) for discovering T2D in a novel collected EHR dataset (named FIMMG dataset)...
February 13, 2019: IEEE Journal of Biomedical and Health Informatics
https://read.qxmd.com/read/30762571/artificial-neural-network-for-in-bed-posture-classification-using-bed-sheet-pressure-sensors
#3
Georges Matar, Jean-Marc Lina, Georges Kaddoum
Pressure ulcer prevention is a vital procedure for patients undergoing long-term hospitalization. A human body lying posture (HBLP) monitoring system is essential to reschedule posture change for patients. Video-surveillance, the conventional method of HBLP monitoring, suffers from various limitations, such as subject's privacy, and field-of view obstruction. We propose an autonomous method for classifying the four state-of-art HBLPs in healthy adult subjects: supine, prone, left and right lateral, with no sensors or cables attached on the body and no constraints imposed on the subject...
February 13, 2019: IEEE Journal of Biomedical and Health Informatics
https://read.qxmd.com/read/30763250/detection-and-control-of-unannounced-exercise-in-the-artificial-pancreas-without-additional-physiological-signals
#4
Charrise M Ramkissoon, Athur Bertachi, Aleix Beneyto, Jorge Bondia, Josep Vehi
The purpose of this study was to develop an algorithm that detects aerobic exercise and triggers disturbance rejection actions to prevent exercise-induced hypoglycemia. This approach can provide a solution to poor glycemic control during and after aerobic exercise, a major hindrance in the participation of exercise by patients with type 1 diabetes. This novel exercise-induced hypoglycemia reduction algorithm (EHRA) detects exercise using a threshold on a disturbance term, a parameter estimated from an augmented minimal model using an Unscented Kalman Filter...
February 11, 2019: IEEE Journal of Biomedical and Health Informatics
https://read.qxmd.com/read/30763249/a-novel-mkl-method-for-gbm-prognosis-prediction-by-integrating-histopathological-image-and-multi-omics-data
#5
Ya Zhang, Ao Li, Jie He, Minghui Wang
Glioblastoma Multiforme (GBM) is one of the most malignant brain tumors with very short prognosis expectation. To improve patients' clinical treatment and their life quality after surgery. Researches have developed tremendous in silico models and tools for predicting GBM prognosis based on molecular datasets and have earned great success. However, pathology still plays the most critical role in cancer diagnosis and prognosis in the clinic at present. Recent advancement of storing and processing histopathological images have drawn attention from researches...
February 11, 2019: IEEE Journal of Biomedical and Health Informatics
https://read.qxmd.com/read/30763248/motion-sensor-based-assessment-of-parkinson-s-disease-motor-symptoms-during-leg-agility-tests-results-from-levodopa-challenge
#6
Somayeh Aghanavesi, Filip Bergquist, Dag Nyholm, Marina Senek, Mevludin Memedi
Parkinson's disease (PD) is a degenerative, progressive disorder of the central nervous system that mainly affects motor control. The aim of this study was to develop data-driven methods and test their clinimetric properties to detect and quantify PD motor states using motion sensor data from leg agility tests. Nineteen PD patients were recruited in a levodopa single dose challenge study. PD patients performed leg agility tasks while wearing motion sensors on their lower extremities. Clinical evaluation of video recordings was performed by three movement disorder specialists who used four items from the motor section of the Unified PD Rating Scale (UPDRS), the treatment response scale (TRS) and a dyskinesia score...
February 8, 2019: IEEE Journal of Biomedical and Health Informatics
https://read.qxmd.com/read/30716057/estimating-sit-to-stand-dynamics-using-a-single-depth-camera
#7
Robert Peter Matthew, Sarah Seko, Jeannie Bailey, Ruzena Bajcsy, Jeffrey Lotz
Kinematic and dynamic analysis of human motion allows for an assessment of a patient's functional ability, providing insight beyond static imaging or subjective surveys. While advanced modelling methods and sensor systems are utilised by biomechanics laboratories, there remains a need for a clinically deployable system a to analyse patient motion in a fast, affordable, and accurate manner. This paper presents and validates a method for performing inverse dynamics with a single depth camera, including estimates of body momenta and joint torques...
February 4, 2019: IEEE Journal of Biomedical and Health Informatics
https://read.qxmd.com/read/30716056/hierarchical-rough-to-fine-model-for-infant-age-prediction-based-on-cortical-features
#8
Dan Hu, Zhengwang Wu, Weili Lin, Gang Li, Dinggang Shen
Prediction of the chronological age based on neuroimaging data is important for brain development analysis and brain disease diagnosis. Although many researches have been conducted for age prediction of older children and adults, little work has been dedicated to infants. To this end, this paper focuses on predicting infant age from birth to 2 years old using brain MR images, as well as identifying some related biomarkers. However, brain development during infancy is too rapid and heterogeneous to be accurately modeled by the conventional regression models...
February 1, 2019: IEEE Journal of Biomedical and Health Informatics
https://read.qxmd.com/read/30716055/implicit-irregularity-detection-using-unsupervised-learning-on-daily-behaviors
#9
Cuijuan Shang, Chih-Yung Chang, Guilin Chen, Shenghui Zhao, Jiazao Lin
The irregularity detection of daily behaviors for the elderly is an important issue in homecare. Plenty of mechanisms have been developed to detect the health condition of the elderly based on the explicit irregularity of several biomedical parameters or some specific behaviors. However, few researches focus on detecting the implicit irregularity involving the combination of diverse behaviors, which can assess the cognitive and physical wellbeing of elders but cannot be directly identified based on sensor data...
February 1, 2019: IEEE Journal of Biomedical and Health Informatics
https://read.qxmd.com/read/30716054/investigating-the-role-of-model-based-and-model-free-imaging-biomarkers-as-early-predictors-of-neoadjuvant-breast-cancer-therapy-outcome
#10
Eleftherios Kontopodis, Maria Venianaki, George Manikis, Katerina Nikiforaki, Ovidio Salvetti, Efrosini Papadaki, Georgios Papadakis, Apostolos Karantanas, Kostas Marias
Imaging biomarkers (IBs) play a critical role in the clinical management of breast cancer (BRCA) patients throughout the cancer continuum for screening, diagnosis and therapy assessment especially in the neoadjuvant setting. However, certain model-based IBs suffer from significant variability due to the complex workflows involved in their computation, whereas model-free IBs have not been properly studied regarding clinical outcome. In the present study, IBs from 35 BRCA patients who received neoadjuvant chemotherapy (NAC) were extracted from dynamic contrast enhanced MR imaging (DCE-MRI) data with two different approaches, a model-free approach based on pattern recognition (PR), and a model-based one using pharmacokinetic compartmental modeling...
January 31, 2019: IEEE Journal of Biomedical and Health Informatics
https://read.qxmd.com/read/30714937/gesgnext-gene-expression-signature-extraction-and-meta-analysis-on-gene-expression-omnibus
#11
Shankai Yan, Ka-Chun Wong
The Gene Expression Omnibus (GEO) repository harbours an exponentially increasing number of gene expression studies. The expression data, as well as the related metadata, provides an abundant resource for knowledge discovery. Each study in GEO focuses on the gene expression perturbation of a specific subject (e.g. gene, drug, and disease). The identification of those subjects and the associations among them are beneficial for further in-depth studies. However, they cannot be directly inferred from the studies...
January 30, 2019: IEEE Journal of Biomedical and Health Informatics
https://read.qxmd.com/read/30714936/patient-specific-prediction-of-abdominal-aortic-aneurysm-expansion-using-bayesian-calibration
#12
Liangliang Zhang, Zhenxiang Jiang, Jongeun Choi, Chae Young Lim, Tapabrata Maiti, Seungik Baek
Translating recent advances in abdominal aortic aneurysm (AAA) growth and remodeling (G&R) knowledge into a predictive, patient-specific clinical treatment tool requires a major paradigm shift in computational modelling. The objectives of this study are to develop a prediction framework that 1) first calibrates the physical AAA G\&R model using patient-specific serial computed tomography (CT) scan images, 2) predicts the expansion of an AAA in the future, and 3) quantifies the associated uncertainty in the prediction...
January 30, 2019: IEEE Journal of Biomedical and Health Informatics
https://read.qxmd.com/read/30714935/understanding-patients-behavior-vision-based-analysis-of-seizure-disorders
#13
David Ahmedt Aristizabal, Simon Denman, Kien Nguyen, Sridha Sridharan, Sasha Dionisio, Clinton Fookes
A substantial proportion of patients with functional neurological disorders (FND) are being incorrectly diagnosed with epilepsy because their semiology resembles that of epileptic seizures (ES). Misdiagnosis may lead to unnecessary treatment and its associated complications. Diagnostic errors often result from an over-reliance on specific clinical features. Furthermore, the lack of electrophysiological changes in patients with FND can also be seen in some forms of epilepsy, making diagnosis extremely challenging...
January 29, 2019: IEEE Journal of Biomedical and Health Informatics
https://read.qxmd.com/read/30703051/dermoscopy-image-analysis-overview-and-future-directions
#14
M Emre Celebi, Noel Codella, Allan Halpern
Dermoscopy is a non-invasive skin imaging technique that permits visualization of features of pigmented melanocytic neoplasms that are not discernable by examination with the naked eye. While studies on the automated analysis of dermoscopy images date back to the late 1990s, because of various factors (lack of publicly available datasets, open-source software, computational power, etc.), the field progressed rather slowly in its first two decades. With the release of a large public dataset by the International Skin Imaging Collaboration (ISIC) in 2016, development of open-source software for convolutional neural networks, and the availability of inexpensive graphics processing units, dermoscopy image analysis has recently become a very active research field...
January 28, 2019: IEEE Journal of Biomedical and Health Informatics
https://read.qxmd.com/read/30703050/performance-analysis-of-gyroscope-and-accelerometer-sensors-for-seismocardiography-based-wearable-pre-ejection-period-estimation
#15
Md Mobashir Hasan Shandhi, Beren Semiz, Sinan Hersek, Nazli Goller, Farrokh Ayazi, Omer Inan
OBJECTIVE: Systolic time intervals such as the pre-ejection period (PEP) are important parameters for assessing cardiac contractility that can be measured non-invasively using seismocardiography (SCG). Recent studies have shown that specific points on accelerometer and gyroscope based SCG signals can be used for PEP estimation. However, the complex morphology and inter-subject variation of the SCG signal can make this assumption very challenging and increase the root mean squared error (RMSE) when these techniques are used to develop a global model...
January 28, 2019: IEEE Journal of Biomedical and Health Informatics
https://read.qxmd.com/read/30703049/human-emotion-characterization-by-heart-rate-variability-analysis-guided-by-respiration
#16
Maria Teresa Valderas Yamuza, Juan Bolea, Michele Orini, Pablo Laguna, Carlos Orrite, Montserrat Vallverdu, Raquel Bailon
Developing a tool which identifies emotions based on their effect on cardiac activity may have a potential impact on clinical practice, since it may help in the diagnosing of psycho-neural illnesses. In this study, a method based on the analysis of heart rate variability (HRV) guided by respiration is proposed. The method was based on redefining the high frequency (HF) band, not only to be centered at the respiratory frequency, but also to have a bandwidth dependent on the respiratory spectrum. The method was first tested using simulated HRV signals, yielding the minimum estimation errors as compared to classical and respiratory frequency centered at HF band based definitions, independently of the values of the sympathovagal ratio...
January 28, 2019: IEEE Journal of Biomedical and Health Informatics
https://read.qxmd.com/read/30676990/modeling-of-heart-rate-variability-and-respiratory-muscle-activity-in-organophosphate-poisoned-patients
#17
Maria Bernarda Salazar Sanchez, A M Hernandez, Miguel Angel Manyanas, Cesar Cortes Daza
We propose an extended model of cardiovascular regulation to assess heart rate variability in patients poisoned with organophosphate during their treatment with mechanical ventilation. The model was modified to fit a population of twenty-one (21) patients poisoned with organophosphorus compounds and undergoing mechanical ventilation. The extended model incorporated the respiratory muscle activity measured by surface electromyography for quantifying the vagal - sympathetic engagement during spontaneous breathing test...
January 23, 2019: IEEE Journal of Biomedical and Health Informatics
https://read.qxmd.com/read/30676989/deep-learning-current-and-emerging-applications-in-medicine-and-technology
#18
Altug Akay, Henry Hess
Machine learning is enabling researchers to analyze and understand increasingly complex physical and biological phenomena in traditional fields such as biology, medicine, and engineering and emerging fields like synthetic biology, automated chemical synthesis, and bio-manufacturing. These fields require new paradigms towards understanding increasingly complex data and converting such data into medical products and services for patients. The move towards deep learning and complex modeling is an attempt to bridge the gap between acquiring massive quantities of complex data, and converting such data into practical insights...
January 23, 2019: IEEE Journal of Biomedical and Health Informatics
https://read.qxmd.com/read/30676988/improving-prediction-performance-using-hierarchical-analysis-of-real-time-data-a-sepsis-case-study
#19
Franco van Wyk, Anahita Khojandi, Rishikesan Kamaleswaran
This paper presents a novel method for hierarchical analysis of machine learning algorithms to improve predictions of at risk patients, thus further enabling prompt therapy. Specifically, we develop a multi-layer machine learning approach to analyze continuous, high-frequency data. We illustrate the capabilities of this approach for early identification of patients at risk of sepsis, a potentially life-threatening complication of an infection, using high-frequency (minute-by-minute) physiological data collected from bedside monitors...
January 23, 2019: IEEE Journal of Biomedical and Health Informatics
https://read.qxmd.com/read/30676986/gaussian-processes-for-personalized-interpretable-volatility-metrics-in-the-step-down-ward
#20
Glen Wright Colopy, Stephen Roberts, David A Clifton
Patients in a hospital step-down unit require a level of care that is between that of the intensive care unit (ICU) and that of the general ward. While many patients remain physiologically stabilized, others will suffer clinical emergencies and be readmitted to the ICU, with a subsequent high risk of mortality. Had the associated physiological deterioration been detected early, the emergency may have been less severe or avoided entirely. Current clinical monitoring is largely heuristic, requiring manual calculation of risk scores and the use of heuristic decision criteria...
January 22, 2019: IEEE Journal of Biomedical and Health Informatics
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