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IEEE Journal of Biomedical and Health Informatics

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
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
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
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
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
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
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
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
Wei Chun Ung, Tsukasa Funane, Takushige Katura, Hiroki Sato, Tong Boon Tang, Ahmad Fadzil Mohammad Hani, Masashi Kiguchi
Near-infrared spectroscopy (NIRS), one of the candidates to be used in a neurofeedback system or brain-computer interface (BCI), measures the brain activity by monitoring the changes in cerebral hemoglobin concentration. However, hemodynamic changes in the scalp may affect the NIRS signals. In order to remove the superficial signals when NIRS is used in a neurofeedback system or BCI, real-time processing is necessary. Real-time scalp signal separating (RT-SSS) algorithm, which is capable of separating the scalp-blood signals from NIRS signals obtained in real-time, may thus be applied...
July 4, 2017: IEEE Journal of Biomedical and Health Informatics
Yushan Zheng, Zhiguo Jiang, Haopeng Zhang, Fengying Xie, Yibing Ma, Huaqiang Shi, Yu Zhao
Content-based image retrieval (CBIR) has been widely researched for histopathological images. It is challenging to retrieve contently similar regions from histopathological whole slide images (WSIs) for regions of interest (ROIs) in different size. In this paper, we propose a novel CBIR framework for database that consists of WSIs and size-scalable query ROIs. Each WSI in the database is encoded into a matrix of binary codes. When retrieving, a group of region proposals that have similar size with the query ROI are firstly located in the database through an efficient table-lookup approach...
July 4, 2017: IEEE Journal of Biomedical and Health Informatics
Sebastian Rodrigo Vanrell, Diego Humberto Milone, Hugo Leonardo Rufiner
Unobtrusive activity monitoring can provide valuable information for medical and sports applications. In recent years, human activity recognition has moved to wearable sensors to deal with unconstrained scenarios. Accelerometers are the preferred sensors due to their simplicity and availability. Previous studies have examined several \azul{classic} techniques for extracting features from acceleration signals, including time-domain, time-frequency, frequency-domain, and other heuristic features. Spectral and temporal features are the preferred ones and they are generally computed from acceleration components, leaving the acceleration magnitude potential unexplored...
July 3, 2017: IEEE Journal of Biomedical and Health Informatics
Mohammad Wazid, Ashok Kumar Das, Neeraj Kumar, Mauro Conti, Athanasios V Vasilakos
Implantable medical devices (IMDs) are man-made devices, which can be implanted in the human body to improve the functioning of various organs. The IMDs monitor and treat physiological condition of the human being (for example, monitoring of blood glucose level by insulin pump). The advancement of information and communication technology (ICT) enhances the communication capabilities of IMDs. In healthcare applications, after mutual authentication, a user (for example, doctor) can access the health data from the IMDs implanted in a patient's body...
June 29, 2017: IEEE Journal of Biomedical and Health Informatics
Michael Dorr, Tobias Elze, Wang Hui, Zhong-Lin Lu, Peter John Bex, Luis Andres Lesmes
Visual sensitivity is comprehensively described by the Contrast Sensitivity Function (CSF), but current routine clinical care does not include its assessment because of the time-consuming need to estimate thresholds for a large number of spatial frequencies. The quick CSF method, however, dramatically reduces testing times by using a Bayesian information maximization rule. We evaluate the test-retest variability of a tablet-based quick CSF implementation in a study with 100 subjects who repeatedly assessed their vision with and without optical correction...
June 26, 2017: 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
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