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IEEE Journal of Biomedical and Health Informatics

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https://www.readbyqxmd.com/read/28816683/cardiorespiratory-model-based-data-driven-approach-for-sleep-apnea-detection
#1
Sandeep Gutta, Qi Cheng, Hoa Nguyen, Bruce Benjamin
Obstructive sleep apnea (OSA) is a chronic sleep disorder affecting millions of people worldwide. Individuals with OSA are rarely aware of the condition and are often left untreated, which can lead to some serious health problems. Nowadays, several low-cost wearable health sensors are available that can be used to conveniently and noninvasively collect a wide range of physiological signals. In this paper, we propose a new framework for OSA detection in which we combine the wearable sensor measurement signals with the mathematical models of the cardiorespiratory system...
August 14, 2017: IEEE Journal of Biomedical and Health Informatics
https://www.readbyqxmd.com/read/28796627/automated-breast-ultrasound-lesions-detection-using-convolutional-neural-networks
#2
Moi Hoon Yap, Gerard Pons, Joan Marti, Sergi Ganau, Melcior Sentis, Reyer Zwiggelaar, Adrian K Davison, Robert Marti
Breast lesion detection using ultrasound imaging is considered an important step of Computer-Aided Diagnosis systems. Over the past decade, researchers have demonstrated the possibilities to automate the initial lesion detection. However, the lack of a common dataset impedes research when comparing the performance of such algorithms. This paper proposes the use of deep learning approaches for breast ultrasound lesion detection and investigates three different methods: a Patch-based LeNet, a U-Net, and a transfer learning approach with a pretrained FCN-AlexNet...
August 7, 2017: IEEE Journal of Biomedical and Health Informatics
https://www.readbyqxmd.com/read/28796626/matrix-and-tensor-completion-on-a-human-activity-recognition-framework
#3
Sofia Savvaki, Grigorios Tsagkatakis, Athanasia Panousopoulou, Panagiotis Tsakalides
Sensor-based activity recognition is encountered in innumerable applications of the arena of pervasive healthcare and plays a crucial role in biomedical research. Nonetheless, the frequent situation of unobserved measurements impairs the ability of machine learning algorithms to efficiently extract context from raw streams of data. In this work, we study the problem of accurate estimation of missing multi-modal inertial data and we propose a classification framework that considers the reconstruction of sub-sampled data during the test phase...
August 7, 2017: IEEE Journal of Biomedical and Health Informatics
https://www.readbyqxmd.com/read/28796625/collaborative-ehealth-meets-security-privacy-enhancing-patient-profile-management
#4
Rosa Sanchez-Guerrero, Florina Almenarez, Daniel Diaz-Sanchez, Patricia Arias, Andres Marin
Collaborative healthcare environments offer potential benefits, including enhancing the healthcare quality delivered to patients and reducing costs. As a direct consequence, sharing of Electronic Health Records (EHRs) among healthcare providers has experienced a noteworthy growth in the last years, since it enables physicians to remotely monitor patients' health and enables individuals to manage their own health data more easily. However, these scenarios face significant challenges regarding security and privacy of the extremely sensitive information contained in EHRs...
August 7, 2017: IEEE Journal of Biomedical and Health Informatics
https://www.readbyqxmd.com/read/28783652/a-parametric-computational-analysis-into-galvanic-coupling-intrabody-communication
#5
M Amparo Callejon, P Del Campo, Javier Reina-Tosina, Laura M Roa
Intrabody Communication (IBC) uses the human body tissues as transmission media for electrical signals to interconnect personal health devices in wireless body area networks. The main goal of this work is to conduct a computational analysis covering some bioelectric issues that still have not been fully explained, such as the modeling of the skin-electrode impedance, the differences associated to the use of constant voltage or current excitation modes, or the influence on attenuation of the subject's anthropometrical and bioelectric properties...
August 2, 2017: IEEE Journal of Biomedical and Health Informatics
https://www.readbyqxmd.com/read/28783651/automatic-recognition-of-activities-of-daily-living-utilizing-insole-based-and-wrist-worn-wearable-sensors
#6
Nagaraj Hegde, Matthew Bries, Tracy Swibas, Edward Melanson, Edward Sazonov
Automatic recognition of activities of daily living (ADL) is an important component in understanding of energy balance, quality of life and other areas of health and well-being. In our previous work, we had proposed an insole based activity monitor - SmartStep, designed to be socially acceptable and comfortable. The goals of the current study were: first, validation of SmartStep in recognition of a broad set of ADL; second, comparison of the SmartStep to a wrist sensor and testing these in combination; third, evaluation of SmartStep accuracy in measuring wear non-compliance and a novel activity class (driving); fourth, performing the validation in free living against a well-studied criterion measure (ActivPAL, PAL Technologies); and fifth, quantitative evaluation of the perceived comfort of SmartStep...
August 1, 2017: IEEE Journal of Biomedical and Health Informatics
https://www.readbyqxmd.com/read/28783650/automatic-polyp-detection-via-a-novel-unified-bottom-up-and-top-down-saliency-approach
#7
Yixuan Yuan, Dengwang Li, Max Q-H Meng
In this paper, we propose a novel automatic computer-aided method to detect polyps for colonoscopy videos. To find the perceptually and semantically meaningful salient polyp regions, we first segment images into multilevel superpixels. Each level corresponds to different sizes of superpixels. Rather than adopting hand-designed features to describe these superpixels in images, we employ sparse autoencoder (SAE) to learn discriminative features in an unsupervised way. Then a novel unified bottom-up and top-down saliency method is proposed to detect polyps...
July 31, 2017: IEEE Journal of Biomedical and Health Informatics
https://www.readbyqxmd.com/read/28783649/variational-mode-extraction-a-new-efficient-method-to-derive-respiratory-signals-from-ecg
#8
Mojtaba Nazari, Sayed Mahmoud Sakhaei
ECG-derived respiratory (EDR) signal is an effective and inexpensive method to monitor the respiration. Previous studies have shown that the empirical mode decomposition (EMD) techniques can satisfactorily extract the EDR signal, however their performances are degraded at the presence of noise. On the other hand, Variational Mode Decomposition (VMD) performs good robustness against noise. In applications such as EDR extraction, where a specific mode is in interest, VMD imposes unnecessary computational cost...
July 31, 2017: IEEE Journal of Biomedical and Health Informatics
https://www.readbyqxmd.com/read/28767376/ehdc-an-energy-harvesting-modeling-and-profiling-platform-for-body-sensor-networks
#9
Dawei Fan, Luis Lopez Ruiz, Jiaqi Gong, John Lach
Energy harvesting is a promising solution to the limited battery lifetimes of body sensor nodes. Self-powered sensor systems capable of quasi-perpetual operation enable the possibility of truly continuous monitoring of patients beyond the clinic. However the discontinuous and dynamic characteristics of harvesting in real-world scenarios - and their implications for the design and operation of self-powered systems - are not yet well understood. This paper presents a mobile Energy Harvesting and Data Collection (EHDC) platform designed to provide a deeper understanding of energy harvesting dynamics...
July 31, 2017: IEEE Journal of Biomedical and Health Informatics
https://www.readbyqxmd.com/read/28767375/a-trust-model-for-ubiquitous-healthcare-environment-on-the-basis-of-adaptable-fuzzy-probabilistic-inference-system
#10
Georgia Athanasiou, George C Anastasopoulos, Eleni Tiritidou, Dimitrios Lymberopoulos
Trust is considered to be a determinant on psychologist selection which can ensure patient satisfaction. Hence, trust concept is essential to be introduced into Ubiquitous Healthcare (UH) environment oriented on patients with anxiety disorders. This is accomplished by Trust Model estimating psychologists' trustworthiness, a priory to service delivery, with the use of patient's and his/her acquaintances testimonies, i.e. Personal Interaction Experience (PIE) and Reputation (R). In this paper, Trust Model is proposed to be materialized via an Adaptable Cloud Inference System (ACIS) that performs Trust Value (TV) estimation...
July 28, 2017: IEEE Journal of Biomedical and Health Informatics
https://www.readbyqxmd.com/read/28749360/multi-hypergraph-learning-for-incomplete-multi-modality-data
#11
Mingxia Liu, Yue Gao, Pew-Thian Yap, Dinggang Shen
Multi-modality data convey complementary information that can be used to improve the accuracy of prediction models in disease diagnosis. However, effectively integrating multi-modality data remains a challenging problem especially when the data are incomplete. For instance, more than half of the subjects in the Alzheimer's Disease Neuroimaging Initiative (ADNI) database have no fluorodeoxyglucose positron emission tomography (FDG-PET) and cerebrospinal fluid (CSF) data. Currently, there are two commonly-used strategies to handle the problem of incomplete data: 1) discard samples having missing features, and 2) impute those missing values via specific techniques...
July 26, 2017: IEEE Journal of Biomedical and Health Informatics
https://www.readbyqxmd.com/read/28749359/systolic-time-interval-estimation-using-continuous-wave-radar-with-on-body-antennas
#12
Dilpreet Buxi, Evelien Hermeling, Marco Mercuri, Fabian Beutel, Roberto Garcia van der Westen, Tom Torfs, Jean-Michel Redoute, Mehmet Rasit Yuce
The estimation of systolic time interval (STI)s is done using continuous wave (CW) radar at 2.45GHz with an on-body antenna. MOTIVATION: In the state of the art, typically bioimpedance, heart sounds and / or ultrasound is used to measure STIs. All three methods suffer from insufficient accuracy of STI estimation due to various reasons. CW radar is investigated for its ability to overcome the deficiencies in the state of the art. METHODS: Ten healthy male subjects aged 25-45 were asked to lie down at a 30 degree incline...
July 25, 2017: IEEE Journal of Biomedical and Health Informatics
https://www.readbyqxmd.com/read/28715343/deep-belief-networks-for-electroencephalography-a-review-of-recent-contributions-and-future-outlooks
#13
Faezeh Movahedi, James L Coyle, Ervin Sejdic
Deep learning, a relatively new branch of machine learning, has been investigated for use in a variety of biomedical applications. Deep learning algorithms have been used to analyze different physiological signals and gain a better understanding of human physiology for automated diagnosis of abnormal conditions. In this manuscript, we provide an overview of deep learning approaches with a focus on deep belief networks in electroencephalography applications. We investigate the state of- the-art algorithms for deep belief networks and then cover the application of these algorithms and their performances in electroencephalographic applications...
July 14, 2017: IEEE Journal of Biomedical and Health Informatics
https://www.readbyqxmd.com/read/28715341/an-automatic-detection-system-of-lung-nodule-based-on-multi-group-patch-based-deep-learning-network
#14
Hongyang Jiang, He Ma, Wei Qian, Mengdi Gao, Yan Li
High-efficiency lung nodule detection dramatically contributes to the risk assessment of lung cancer. It is a significant and challenging task to quickly locate the exact positions of lung nodules. Extensive work has been done by researchers around this domain for approximately two decades. However, previous computer aided detection (CADe) schemes are mostly intricate and time-consuming since they may require more image processing modules, such as the computed tomography (CT) image transformation, the lung nodule segmentation and the feature extraction, to construct a whole CADe system...
July 14, 2017: IEEE Journal of Biomedical and Health Informatics
https://www.readbyqxmd.com/read/28715342/quantitative-evaluation-of-rehabilitation-effect-on-peripheral-circulation-of-diabetic-foot
#15
Yao-Kuang Huang, Chang-Cheng Chang, Pin-Xing Lin, Bor-Shyh Lin
Diabetics may encounter different foot problems, which easily lead to infection, ulcers, and increased risk of amputation due to nerve or vascular injury. In order to reduce the risk of amputation, Buerger's exercise is one of the frequently used rehabilitation to improve the blood circulation in the lower limbs. However, it is difficult to evaluate the rehabilitation efficiency of Buerger's exercise objectively. In this study, a novel non-invasively optical system was developed to non-invasively monitor the change of the foot blood circulation before and after long-term Buerger's exercise...
July 13, 2017: IEEE Journal of Biomedical and Health Informatics
https://www.readbyqxmd.com/read/28708565/real-time-adaptation-to-time-varying-constraints-for-medical-video-communications
#16
Zinonas C Antoniou, Andreas S Panayides, Marios Pantziaris, Anthony G Constantinides, Constantinos S Pattichis, Marios S Pattichis
The wider adoption of mobile Health (mHealth) video communication systems in standard clinical practice requires real-time control to provide for adequate levels of clinical video quality to support reliable diagnosis. The latter can only be achieved with real-time adaptation to time-varying wireless networks' state to guarantee clinically acceptable performance throughout the streaming session, while conforming to device capabilities for supporting real-time encoding.
July 12, 2017: IEEE Journal of Biomedical and Health Informatics
https://www.readbyqxmd.com/read/28693000/a-new-active-contours-approach-for-finger-extensor-tendon-segmentation-in-ultrasound-images-using-prior-knowledge-and-phase-symmetry
#17
Nelson Martins, Saad Sultan, Diana Veiga, Manuel Ferreira, Filipa Teixeira, Miguel Coimbra
This work proposes a new approach for the segmentation of the extensor tendon in ultrasound images of the second metacarpophalangeal joint (MCPJ). The MCPJ is known to be frequently involved in early stages of rheumatic diseases like rheumatoid arthritis. The early detection and follow up of these diseases is important to start and adapt the treatments properly and, in that way, preventing irreversible damage of the joints. This work relies on an active contours framework, preceded by a phase symmetry preprocessing and with prior knowledge energies, to automatically identify the extensor tendon...
July 5, 2017: IEEE Journal of Biomedical and Health Informatics
https://www.readbyqxmd.com/read/28692999/optic-disk-detection-in-fundus-image-based-on-structured-learning
#18
Zhun Fan, Yibiao Rong, Xieye Cai, Jiewei Lu, Wenji Li, Huibiao Lin, Xinjian Chen
Automated optic disk (OD) detection plays an important role in developing a computer aided system for eye diseases. In this paper, we propose an algorithm for OD detection based on structured learning. A classifier model is trained based on structured learning. Then we use the model to achieve the edge map of OD. Thresholding is performed on the edge map thus a binary image of the OD is obtained. Finally, circle Hough transform is carried out to approximate the boundary of OD by a circle. The proposed algorithm has been evaluated on three public datasets and obtained promising results...
July 5, 2017: IEEE Journal of Biomedical and Health Informatics
https://www.readbyqxmd.com/read/28692998/a-new-ensemble-classification-system-for-fracture-zone-prediction-using-imbalanced-micro-ct-bone-morphometrical-data
#19
Vasileios Ch Korfiatis, Simone Tassani, George K Matsopoulos
Trabecular bone fractures constitute a major health issue for the modern societies, with the currently established prediction methods of fracture risk, such as Bone Mineral Density (BMD), resulting in errors up to 40%. Fracture-zone prediction based on bone's micro-structure has been recently proposed as an alternative prediction method of fracture risk. In this paper, a Classification System (CS) for the automatic fracture-zone prediction based on an Ensemble of Imbalanced Learning methods is proposed, following the observation that the percentage of the actual fractured bone area is significantly smaller than the intact bone in the case of a fracture event...
July 4, 2017: IEEE Journal of Biomedical and Health Informatics
https://www.readbyqxmd.com/read/28692997/automated-classification-and-removal-of-eeg-artifacts-with-svm-and-wavelet-ica
#20
Chong Yeh Sai, Norrima Mokhtar, Hamzah Arof, Paul Cumming, Masahiro Iwahashi
Brain electrical activity recordings by electroencephalography (EEG) are often contaminated with signal artifacts. Procedures for automated removal of EEG artifacts are frequently sought for clinical diagnostics and brain computer interface (BCI) applications. In recent years, a combination of independent component analysis (ICA) and discrete wavelet transform (DWT) has been introduced as standard technique for EEG artifact removal. However, in performing the wavelet-ICA procedure, visual inspection or arbitrary thresholding may be required for identifying artifactual components in the EEG signal...
July 4, 2017: IEEE Journal of Biomedical and Health Informatics
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