journal
MENU ▼
Read by QxMD icon Read
search

IEEE Journal of Biomedical and Health Informatics

journal
https://www.readbyqxmd.com/read/30222588/classifier-personalization-for-activity-recognition-using-wrist-accelerometers
#1
Andrea Mannini, Stephen Intille
Inter-subject variability in accelerometer-based activity recognition may significantly affect classification accuracy, limiting a reliable extension of methods to new users. In this work we propose an approach for personalizing classification rules to a single person. We demonstrate that the method improves activity detection from wrist-worn accelerometer data on a four-class recognition problem of interest to the exercise science community, where classes are ambulation, cycling, sedentary, and other. We extend a previously published activity classification method based on support vector machines so that it estimates classification uncertainty...
September 12, 2018: IEEE Journal of Biomedical and Health Informatics
https://www.readbyqxmd.com/read/30207969/weakly-supervised-learning-of-metric-aggregations-for-deformable-image-registration
#2
Enzo Ferrante, Puneet Kumar Dokania, Rafael Marini Silva, Nikos Paragios
Deformable registration has been one of the pillars of biomedical image computing. Conventional approaches refer to the definition of a similarity criterion that, once endowed with a deformation model and a smoothness constraint, determines the optimal transformation to align two given images. The definition of this metric function is among the most critical aspects of the registration process. We argue that incorporating semantic information (in the form of anatomical segmentation maps) into the registration process will further improve the accuracy of the results...
September 10, 2018: IEEE Journal of Biomedical and Health Informatics
https://www.readbyqxmd.com/read/30207968/opencliper-an-opencl-based-c-framework-for-overhead-reduced-medical-image-processing-and-reconstruction-on-heterogeneous-devices
#3
Federico Simmross-Wattenberg, Manuel Rodriguez-Cayetano, Javier Royuela-Del-Val, Elena Martin-Gonzalez, Elisa Moya-Saez, Marcos Martin-Fernandez, Carlos Alberola-Lopez
Medical image processing is often limited by the computational cost of the involved algorithms. Whereas dedicated computing devices (GPUs in particular) exist and do provide significant efficiency boosts, they have an extra cost of use in terms of housekeeping tasks (device selection and initialization, data streaming, synchronization with the CPU and others), which may hinder developers from using them. This paper describes an OpenCL-based framework that is capable of handling dedicated computing devices seamlessly and that allows the developer to concentrate on image processing tasks...
September 10, 2018: IEEE Journal of Biomedical and Health Informatics
https://www.readbyqxmd.com/read/30207967/glycemic-control-of-people-with-type-1-diabetes-based-on-probabilistic-constraints
#4
Souransu Nandi, Tarun Singh
The objective of the paper is to develop an open loop insulin infusion profile which is capable of controlling the blood glucose level of people with Type 1 diabetes in the presence of broad uncertainties such as inter-patient variability and unknown meal quantity. For illustrative purposes, the Bergman model in conjunction with a gut-dynamics model is chosen to represent the human glucose-insulin dynamics. A recently developed sampling based uncertainty quantification approach is used to determine the statistics (mean and variance) of the evolving states in the model...
September 10, 2018: IEEE Journal of Biomedical and Health Informatics
https://www.readbyqxmd.com/read/30207966/multimodal-medical-image-fusion-based-on-fuzzy-discrimination-with-structural-patch-decomposition
#5
Yong Yang, Jiahua Wu, Shuying Huang, Yuming Fang, Pan Lin, Yue Que
Multimodal medical image fusion, emerging as a hot topic, aims to fuse images with complementary multi-source information. In this paper, we propose a novel multimodal medical image fusion method based on structural patch decomposition (SPD) and fuzzy logic technology. First, the SPD method is employed to extract two salient features for fusion discrimination. Next, two novel fusion decision maps called an incomplete fusion map and supplemental fusion map are constructed from salient features. In this step, the supplemental map is constructed by our defined two different fuzzy logic systems...
September 10, 2018: IEEE Journal of Biomedical and Health Informatics
https://www.readbyqxmd.com/read/30188842/functional-connectivity-analysis-of-cerebellum-using-spatially-constrained-spectral-clustering
#6
Vasileios C Pezoulas, Kostas Michalopoulos, Manousos Klados, Sifis Micheloyannis, Nikolaos Bourbakis, Michalis Zervakis
The human cerebellum contains almost fifty percent of the neurons in the brain, although its volume does not exceed ten percent of the total brain volume. The goal of this study is to derive the functional network of the cerebellum during resting-state and then compare the ensuing group networks between males and females. Towards this direction, a spatially constrained version of the classic spectral clustering algorithm is proposed and then compared against conventional spectral graph theory approaches, such as, spectral clustering, and N-cut, on synthetic data as well as on resting-state fMRI data obtained from the Human Connectome Project (HCP)...
September 6, 2018: IEEE Journal of Biomedical and Health Informatics
https://www.readbyqxmd.com/read/30188841/robust-methods-for-real-time-diabetic-foot-ulcer-detection-and-localization-on-mobile-devices
#7
Manu Goyal, Neil Reeves, Satyan Rajbhandari, Moi Hoon Yap
Current practice for Diabetic Foot Ulcers (DFU) screening involves detection and localization by podiatrists. Existing automated solutions either focus on segmentation or classification. In this work, we design deep learning methods for real-time DFU localization. To produce a robust deep learning model, we collected an extensive database of 1775 images of DFU. Two medical experts produced the ground truths of this dataset by outlining the region of interest of DFU with an annotator software. Using 5-fold cross-validation, overall, Faster R-CNN with InceptionV2 model using two-tier transfer learning achieved a mean average precision of 91...
September 6, 2018: IEEE Journal of Biomedical and Health Informatics
https://www.readbyqxmd.com/read/30183649/parkinson-s-disease-diagnosis-via-joint-learning-from-multiple-modalities-and-relations
#8
Haijun Lei, Zhongwei Huang, Feng Zhou, Ahmed Elazab, Ee-Leng Tan, Hancong Li, Jing Qin, Baiying Lei
Parkinson's disease (PD) is a neurodegenerative progressive disease that mainly affects the motor systems of patients. To slow this disease deterioration, early and accurate diagnosis of PD is an effective way, which alleviates mental and physical sufferings by clinical intervention. In this paper, we propose a joint regression and classification framework for PD diagnosis via magnetic resonance and diffusion tensor imaging data. Specifically, we devise a unified multi-task feature selection model to explore multiple relationships among features, samples, and clinical scores...
September 3, 2018: IEEE Journal of Biomedical and Health Informatics
https://www.readbyqxmd.com/read/30183648/modeling-of-noisy-acceleration-signals-from-quasi-periodic-movements-for-drift-free-position-estimation
#9
Miroslav Zivanovic, Nora Millor, Marisol Gomez
OBJECTIVE: We present a novel approach to drift-free position estimation from noisy acceleration signals which often arise from quasi-periodic small-amplitude body movements. In contrast to the existing methods, this data-driven strategy is designed to properly describe time-variant harmonic structures in single-channel acceleration signals for low signal-to-noise ratios. METHODS: It comprises three processing steps: (1) short-time modeling of acceleration dynamics (instantaneous harmonic amplitudes and phases) in the analysis frame, (2) analytical integration which yields short-time position, and (3) overlap-add recombination for full length position synthesis...
September 3, 2018: IEEE Journal of Biomedical and Health Informatics
https://www.readbyqxmd.com/read/30176615/decision-support-system-for-seizure-onset-zone-localization-based-on-channel-ranking-and-high-frequency-eeg-activity
#10
Stefan L Sumsky, Sabato Santaniello
Interictal high frequency oscillations (HFO) are a promising biomarker that can help define the seizure onset zone (SOZ) and predict the surgical outcome after the epilepsy surgery. The utility of HFO in planning the surgery, though, is un-clear. Reasons include the variability of the HFO across patients and brain regions and the influence of the sleep-wake cycle, which causes large fluctuations in the ratio be-tween the HFO observed in SOZ and non-SOZ regions. To cope with these limitations, a rank-based solution is pro-posed to identify the SOZ by using the HFO in multi-channel intracranial EEG...
August 30, 2018: IEEE Journal of Biomedical and Health Informatics
https://www.readbyqxmd.com/read/30176611/revealing-clusters-of-connected-pathways-through-multisource-data-integration-in-huntington-s-disease-and-spastic-ataxia
#11
Andrea Kakouri, Christiana Christodoulou, Margarita Zachariou, Anastasios Oulas, George Minadakis, Christiana Demetriou, Christina Votsi, Eleni Papanicolaou-Zamba, Christodoulou Kyproula, George Spyrou
The advancement of scientific and medical research over the past years has generated a wealth of experimental data from multiple technologies, including genomics, transcriptomics, proteomics and other forms of -omics data, which are available for a number of diseases. The integration of such multisource data is a key component towards the success of precision medicine. In this work we are investigating a multisource data integration method developed by our group, regarding its ability to drive to clusters of connected pathways under two different approaches: (a) a disease-centric approach, where we want to integrate data around a disease, and (b) a gene-centric approach, where we want to integrate data around a gene...
August 30, 2018: IEEE Journal of Biomedical and Health Informatics
https://www.readbyqxmd.com/read/30176614/respiratory-waveform-estimation-from-multiple-accelerometers-an-optimal-sensor-number-and-placement-analysis
#12
Ailton Luiz Dias Siqueira Junior, Amanda Franco Spirandeli, Raimes Moraes, Vicente Zarzoso
Respiratory patterns are commonly measured to monitor and diagnose cardiovascular, metabolic and sleep disorders. Electronic devices such as masks used to record respiratory waveforms usually require medical staff support and obstruct the patients breathing, causing discomfort. New techniques are being investigated to overcome such limitations. An emerging approach involves accelerometers to estimate the respiratory waveform based on chest motion. However, most existing techniques employ a single accelerometer placed on an arbitrary thorax position...
August 29, 2018: IEEE Journal of Biomedical and Health Informatics
https://www.readbyqxmd.com/read/30176613/multimodal-ambulatory-sleep-detection-using-lstm-recurrent-neural-networks
#13
Akane Sano, Weixuan Chen, Daniel Lopez Martinez, Sara Taylor, Rosalind W Picard
Unobtrusive and accurate ambulatory methods are needed to monitor long-term sleep patterns for improving health. Previously developed ambulatory sleep detection methods rely either in whole or in part on self-reported diary data as ground truth, which is a problem since people often do not fill them out accurately. This paper presents an algorithm that uses multimodal data from smartphones and wearable technologies to detect sleep/wake state and sleep onset/offset using a type of recurrent neural network with long-short-term memory (LSTM) cells for synthesizing temporal information...
August 29, 2018: IEEE Journal of Biomedical and Health Informatics
https://www.readbyqxmd.com/read/30176612/towards-zero-re-training-for-long-term-hand-gesture-recognition-via-ultrasound-sensing
#14
Xingchen Yang, Dalin Zhou, Yu Zhou, Youjia Huang, Honghai Liu
While myoelectric pattern recognition is a prevailing way for gesture recognition, the inherent nonstationarity of electromyography signals hinders its long-term application. This study aims to prove a hypothesis that morphological information of muscle contraction detected by ultrasound image is potentially suitable for long-term use. A set of ultrasound-based algorithms are proposed to realize robust hand gesture recognition over multiple days, with user training only at the first day. A markerless calibration algorithm is first presented to position the ultrasound probe during donning and doffing; an algorithm combining speeded-up robust features (SURF) and bag-of-features (BoF) model being immune to ultrasound probe shift and rotation is then introduced; a self-enhancing classification method is next adopted to update classification model automatically by incorporating useful knowledge from testing data; finally the performance of long-term hand gesture recognition with zero re-training is validated by a six-day experiment of six healthy subjects, whose outcomes strongly support the hypothesis with about 94% of gesture recognition accuracy for each testing day...
August 28, 2018: IEEE Journal of Biomedical and Health Informatics
https://www.readbyqxmd.com/read/30137018/multimodal-assessment-of-parkinson-s-disease-a-deep-learning-approach
#15
Juan Camilo Vasquez-Correa, Tomas Arias-Vergara, J R Orozco-Arroyave, Bjoern Michael Eskofier, Jochen Klucken, Elmar Noth
Parkinson's disease is a neurodegenerative disorder characterized by a variety of motor symptoms. Particularly, difficulties to start/stop movements have been observed in patients. From a technical/diagnostic point of view, these movement changes can be assessed by modeling the transitions between voiced and unvoiced segments in speech, the movement when the patient starts or stops a new stroke in handwriting, or the movement when the patient starts or stops the walking process. This study proposes a methodology to model such difficulties to start or to stop movements considering information from speech, handwriting, and gait...
August 23, 2018: IEEE Journal of Biomedical and Health Informatics
https://www.readbyqxmd.com/read/30113902/anatomical-structure-segmentation-in-ultrasound-volumes-using-cross-frame-belief-propagating-iterative-random-walks
#16
Debarghya China, Alfredo Illanes, Prabal Poudel, Michael Friebe, Pabitra Mitra, Debdoot Sheet
Ultrasound (US) is a widely used as a low-cost alternative to computed tomography (CT) or magnetic resonance (MRI) and primarily for preliminary imaging. Since speckle intensity in US images is inherently stochastic, readers are often challenged in their ability to identify the pathological regions in a volume of a large number of images. This paper introduces a generalized approach for volumetric segmentation of structures in US images and volumes. We employ an iterative random walks (IRW) solver, random forest (RF) learning model, and a gradient vector flow (GVF) based inter-frame belief propagation technique for achieving cross-frame volumetric segmentation...
August 15, 2018: IEEE Journal of Biomedical and Health Informatics
https://www.readbyqxmd.com/read/30113903/convolutional-neural-network-with-shape-prior-applied-to-cardiac-mri-segmentation
#17
Clement Zotti, Zhiming Luo, Alain Lalande, Pierre-Marc Jodoin
In this paper, we present a novel convolutional neural network (CNN) architecture to segment images from a series of short-axis cardiac magnetic resonance slices (CMRI). The proposed model is an extension of the U-net that embeds a cardiac shape prior and involves a loss function tailored to the cardiac anatomy. Our system takes as input raw MR images, requires no manual preprocessing or image cropping and is trained to segment the endocardium and epicardium of the left ventricle, the endocardium of the right ventricle, as well as the center of the left ventricle...
August 14, 2018: IEEE Journal of Biomedical and Health Informatics
https://www.readbyqxmd.com/read/30106745/imu-based-classification-of-parkinson-s-disease-from-gait-a-sensitivity-analysis-on-sensor-location-and-feature-selection
#18
Carlotta Caramia, Diego Torricelli, Maurizio Schmid, Adriana Munoz, Jose Gonzalez, Francisco Grandas, Jose Pons
Inertial Measurement Units (IMUs) have a long-lasting popularity in a variety of industrial applications, from navigation systems, to guidance and robotics. Their use in clinical practice is now becoming more common thanks to miniaturization and the ability to integrate on-board computational and decision-support features. IMU-based gait analysis is a paradigm of this evolving process, and in this study its use for the assessment of Parkinson's Disease (PD) is comprehensively analyzed. Data coming from 25 individuals with different levels of PD symptoms severity and an equal number of age-matched healthy individuals were included into a set of 6 different machine learning (ML) techniques, processing 18 different configurations of gait parameters taken from 8 IMU sensors...
August 13, 2018: IEEE Journal of Biomedical and Health Informatics
https://www.readbyqxmd.com/read/30106744/finger-to-heart-f2h-authentication-for-wireless-implantable-medical-devices
#19
Guanglou Zheng, Wencheng Yang, Craig Valli, Li Qiao, Rajan Shankaran, Mehmet A Orgun, Subhas Chandra Mukhopadhyay
Any proposal to provide security for Implantable Medical Devices (IMDs), such as cardiac pacemakers and defibrillators, has to achieve a trade-off between security and accessibility for doctors to gain access to an IMD, especially in an emergency scenario. In this paper, we propose a Finger-to-Heart (F2H) IMD authentication scheme to address this trade-off between security and accessibility. This scheme utilizes a patient's fingerprint to perform authentication for gaining access to the IMD. Doctors can gain access to the IMD and perform emergency treatment by scanning the patient's finger tip instead of asking the patient for passwords/security tokens thereby achieving the necessary trade-off...
August 10, 2018: IEEE Journal of Biomedical and Health Informatics
https://www.readbyqxmd.com/read/30106702/monitoring-health-status-in-long-term-care-through-the-use-of-ambient-technologies-and-serious-games
#20
Andrea Wilkinson, Tiffany Tong, Atefeh Zare, Marc Kanik, Mark Chignell
New technologies, such as serious games and ambient activities, are being developed to address problems of under-stimulation, anxiety, and agitation in millions of people living with dementia in long term care homes. Frequent interactions with instrumented versions of these technologies may not only be beneficial for long term care residents, but may also provide a valuable new set of multifaceted data relating to health status of residents over time. In this paper, we develop a model of health monitoring in healthcare environments and we report on two studies that show how medically relevant data can be collected from elderly residents and emergency department patients in an unobtrusive way...
August 9, 2018: IEEE Journal of Biomedical and Health Informatics
journal
journal
47867
1
2
Fetch more papers »
Fetching more papers... Fetching...
Read by QxMD. Sign in or create an account to discover new knowledge that matter to you.
Remove bar
Read by QxMD icon Read
×

Search Tips

Use Boolean operators: AND/OR

diabetic AND foot
diabetes OR diabetic

Exclude a word using the 'minus' sign

Virchow -triad

Use Parentheses

water AND (cup OR glass)

Add an asterisk (*) at end of a word to include word stems

Neuro* will search for Neurology, Neuroscientist, Neurological, and so on

Use quotes to search for an exact phrase

"primary prevention of cancer"
(heart or cardiac or cardio*) AND arrest -"American Heart Association"