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IEEE Journal of Biomedical and Health Informatics

Euijoon Ahn, Jinman Kim, Lei Bi, Ashnil Kumar, Changyang Li, Michael Fulham, Dagan Feng
The segmentation of skin lesions in dermoscopic images is a fundamental step in automated computer-aided diagnosis (CAD) of melanoma. Conventional segmentation methods, however, have difficulties when the lesion borders are indistinct and when contrast between the lesion and the surrounding skin is low. They also perform poorly when there is a heterogeneous background or a lesion that touches the image boundaries; this then results in under- and over-segmentation of the skin lesion. We suggest that saliency detection using the reconstruction errors derived from a sparse representation model coupled with a novel background detection can more accurately discriminate the lesion from surrounding regions...
January 16, 2017: IEEE Journal of Biomedical and Health Informatics
Wei-Yang Lin, Shu-Fu Chou, Chia-Ling Tsai, Shih-Jen Chen
Indocyanine green (ICG) angiography is an imaging method for Doctors to observe choroidal abnormalities in human eyes. The ICG angiograms typically exhibit inhomogeneous illumination which poses serious difficulties for the development of computer-aided diagnostic tools. In this paper, we propose a novel illumination normalization method to alleviate the inhomogeneous illumination in ICG video angiograms. In particular, we first align the viewpoint of the input ICG video angiogram using an image registration method...
January 16, 2017: IEEE Journal of Biomedical and Health Informatics
Mostafa Shahin, Beena Ahmed, Sana Tmar-Ben Hamida, Fathima Mulaffer, Martin Glos, Thomas Penzel
Effective sleep analysis is hampered by the lack of automated tools catering for disordered sleep patterns and cumbersome monitoring hardware. In this paper, we apply deep learning on a set of 57 EEG features extracted from a maximum of two EEG channels to accurately differentiate between patients with insomnia or controls with no sleep complaints. We investigated two different approaches to achieve this. The first approach used EEG data from the whole sleep recording irrespective of the sleep stage (stage-independent classification), while the second used only EEG data from insomnia-impacted specific sleep stages (stage-dependent classification)...
January 9, 2017: IEEE Journal of Biomedical and Health Informatics
Daniel Kelly, Kevin Curran, Brian Caulfield
OBJECTIVE: Current methods of assessing the affect a patients' health has on their daily life are extremely limited. The aim of this work is to develop a sensor based approach to health status measurement in order to objectively measure health status. METHODS: Techniques to generate human behaviour profiles, derived from smartphone accelerometer and gyroscope sensors, are proposed. Experiments, using SVM regression models, are then conducted in order to evaluate the use of the proposed behaviour profiles as a predictor of health status...
January 9, 2017: IEEE Journal of Biomedical and Health Informatics
Chen Zhao, Shiqin Jiang, Yanhua Wu, Junjie Zhu, Dafang Zhou, Birgit Hailer, Dietrich Gronemeyer, Peter Van Leeuwen
We present a new approach of integrated maximum current density (IMCD) for the noninvasive detection of myocardial infarction (MI) using magnetocardiography (MCG) data acquired from a superconducting quantum interference device (SQUID) system. In this study, we investigated the relationship of the maximum current density (MCD) in the current density map and the underlying equivalent current dipole (ECD) based on a novel method of reconstructing the ECD in the extremum circle of the magnetic field map. The performance of IMCD and the integrated ECD (IECD) approaches were also evaluated by using 61-channel MCG data from 39 healthy subjects and 102 patients with ST elevation myocardial infarction (STEMI)...
January 9, 2017: IEEE Journal of Biomedical and Health Informatics
Daniele Ravi, Charence Wong, Fani Deligianni, Melissa Berthelot, Javier Andreu Perez, Benny Lo, Guang-Zhong Yang
With a massive influx of multimodality data, the role of data analytics in health informatics has grown rapidly in the last decade. This has also prompted increasing interests in the generation of analytical, data driven models based on machine learning in health informatics. Deep learning, a technique with its foundation in artificial neural networks, is emerging in recent years as a powerful tool for machine learning, promising to reshape the future of artificial intelligence. Rapid improvements in computational power, fast data storage and parallelization have also contributed to the rapid uptake of the technology in addition to its predictive power and ability to generate automatically optimized high-level features and semantic interpretation from the input data...
December 29, 2016: IEEE Journal of Biomedical and Health Informatics
Daniele Ravi, Charence Wong, Benny Lo, Guang-Zhong Yang
The increasing popularity of wearable devices in recent years means that a diverse range of physiological and functional data can now be captured continuously for applications in sports, wellbeing, and healthcare. This wealth of information requires efficient methods of classification and analysis where deep learning is a promising technique for large-scale data analytics. Whilst deep learning has been successful in implementations that utilize high performance computing platforms, its use on low-power wearable devices is limited by resource constraints...
December 23, 2016: IEEE Journal of Biomedical and Health Informatics
Andreas Neocleous, Kypros Nicolaides, Christos Schizas
The objective of this work is to introduce a noninvasive diagnosis procedure for aneuploidy and minimize the social and financial cost of prenatal diagnosis tests that are performed for fetal aneuploidies in an early stage of pregnancy. We propose a method using artificial neural networks trained with data from singleton pregnancy cases, while undergoing first trimester screening. Three different datasets1 with a total of 122362 euploid and 967 aneuploid cases were used in this study. The data for each case contained markers collected from the mother and the fetus...
December 22, 2016: IEEE Journal of Biomedical and Health Informatics
Jun Ying, Joyita Dutta, Ning Guo, Chenhui Hu, Dan Zhou, Arkadiusz Sitek, Quanzheng Li
This study aims to develop an automatic classifier based on deep learning for exacerbation frequency in patients with chronic obstructive pulmonary disease (COPD). A threelayer deep belief network (DBN) with two hidden layers and one visible layer was employed to develop classification models and the models' robustness to exacerbation was analyzed. Subjects from the COPDGene cohort were labeled with exacerbation frequency, defined as the number of exacerbation events per year. 10,300 subjects with 361 features each were included in the analysis...
December 21, 2016: IEEE Journal of Biomedical and Health Informatics
Cheikh Latyr Fall, Gabriel Gagnon-Turcotte, J F Dube, Jean Simon Gagne, Yanick Delisle, Alexandre Campeau-Lecours, Clement Gosselin, Benoit Gosselin
Assistive Technology (AT) tools and appliances are being more and more widely used and developed worldwide to improve the autonomy of people living with disabilities and ease the interaction with their environments. This paper describes an intuitive and wireless surface electromyography (sEMG) based body-machine interface for AT tools. Spinal cord injuries (SCIs) at C5-C8 levels affect patients' arms, forearms, hands and fingers control. Thus, using classical AT control interfaces (keypads, joysticks, etc.) is often difficult or impossible...
December 21, 2016: IEEE Journal of Biomedical and Health Informatics
Lihua Ruan, M Pubuduni Imali Dias, Elaine Wong
The Smart Body Area Network (SmartBAN) is a recently proposed system for wireless body area networks (WBANs). Compared to conventional WBANs, it is designed to support lower system complexity and ultra-low power consumption. In a SmartBAN, the sensors' access is scheduled upon receiving a beacon on the data channel at the beginning of each working cycle, termed inter-beacon interval (IBI). As network performance including delay and energy consumption is highly dependent on the length of IBI, we present, in this paper, an Optimal IBI frame for SmartBAN...
December 20, 2016: IEEE Journal of Biomedical and Health Informatics
Muhammad Farooq, Edward Sazonov
Several methods have been proposed for automatic and objective monitoring of food intake, but their performance suffers in the presence of speech and motion artifacts. This work presents a novel sensor system and algorithms for detection and characterization of chewing bouts from a piezoelectric strain sensor placed on the temporalis muscle. The proposed data acquisition device was incorporated into the temple of eyeglasses. The system was tested by ten participants in two part experiments, one under controlled laboratory conditions and the other in unrestricted free-living...
December 14, 2016: IEEE Journal of Biomedical and Health Informatics
Toufik Guettari, Dan Istrate, Jerome Boudy, Badr-Eddine Benkelfet, Betty Fumel, Jean-Christophe Daviet
This paper presents the design and a first evaluation of a new monitoring system based on contactless sensors to estimate sleep quality. This sensor produces thermal signals which have been used, at first, to detect a human presence in the bed and then to estimate sleep quality. To distinguish between different sleep phases, we have used methods of signal processing in order to extract the necessary features for learning an adapted statistical model. The existing monitoring systems use sensors attached to the bed or worn by the person...
December 14, 2016: IEEE Journal of Biomedical and Health Informatics
Milos Jordanski, Milos Radovic, Zarko Milosevic, Nenad Filipovic, Zoran Obradovic
Computer simulations based on the finite element method (FEM) represent powerful tools for modeling blood flow through arteries. However, due to its computational complexity, this approach may be inappropriate when results are needed quickly. In order to reduce computational time, in this paper we proposed an alternative machine learning based approach for calculation of wall shear stress (WSS) distribution, which may play an important role in mechanisms related to initiation and development of atherosclerosis...
December 14, 2016: IEEE Journal of Biomedical and Health Informatics
Abhishek Srivastava, Nithin Sankar, Baibhab Chatterjee, Devarshi Das, Meraj Ahmad, Rakesh Kukkundoor, Vivek Saraf, Ananthpadmanabhan Jayachandran, Dinesh Sharma, Maryam Baghini
This paper presents a low power integrated wireless telemetry system (Bio-WiTel) for healthcare applications in 401- 406 MHz frequency band of Medical Device RadioCommunication (MedRadio) spectrum. In this work, necessary design considerations for telemetry system for short range (upto 3 m) communication of bio-signals are presented. These considerations greatly help in taking important design decisions, which eventually lead to a simple, low power, robust and reliable wireless system implementation. Transmitter (TX) and receiver (RX) of Bio-WiTel system have been fabricated in 180 nm mixed mode CMOS technology...
December 14, 2016: IEEE Journal of Biomedical and Health Informatics
Philipp Muller, Marc-Andre Begin, Thomas Schauer, Thomas Seel
Due to their relative ease of handling and low cost, inertial measurement unit (IMU)-based joint angle measurements are used for a widespread range of applications. These include sports performance, gait analysis and rehabilitation (e.g. Parkinson's disease monitoring or post-stroke assessment). However, a major downside of current algorithms, recomposing human kinematics from IMU data, is that they require calibration motions and/or the careful alignment of the IMUs with respect to the body segments. In this article, we propose a new method, which is alignment-free and self-calibrating using arbitrary movements of the user and an initial zero reference arm pose...
December 14, 2016: IEEE Journal of Biomedical and Health Informatics
Adnan Vilic, Karsten Hoppe, John Petersen, Troels Kjaer, Helge Sorensen
This paper presents a novel data-driven approach to graphical presentation of text-based electronic health records (EHR) while maintaining all textual information. We have developed the Patient Condition Timeline (PCT) tool, which creates a timeline representation of a patients' physiological condition during admission. PCT is based on electronical monitoring of vital signs and then combining these into Early Warning Scores (EWS). Hereafter, techniques from Natural Language Processing (NLP) are applied on existing EHR to extract all entries...
December 9, 2016: IEEE Journal of Biomedical and Health Informatics
Diana Tobon Vallejo, Tiago Falk
Advances in low-cost portable electrocardiogram (ECG) devices have opened doors for numerous new applications, including fitness tracking, remote health, and peak athletic performance monitoring, to name a few. Many such devices, however, have been shown to be highly contaminated by movement and/or muscle contraction artifacts, which in turn, can lead to erroneous heart rate and heart rate variability (HRV) analyses. Here, we propose a new denoising method based on adaptive spectrotemporal filtering for ECG enhancement...
December 9, 2016: IEEE Journal of Biomedical and Health Informatics
Pegah Kharazmi, Mohammed I AlJasser, Harvey Lui, Z Jane Wang, Tim K Lee
Blood vessels are important biomarkers in skin lesions both diagnostically and clinically. Detection and quantification of cutaneous blood vessels provide critical information towards lesion diagnosis and assessment. In this paper, a novel framework for detection and segmentation of cutaneous vasculature from dermoscopy images is presented and the further extracted vascular features are explored for skin cancer classification. Given a dermoscopy image, we segment vascular structures of the lesion by first decomposing the image using independent component analysis into melanin and hemoglobin components...
December 8, 2016: IEEE Journal of Biomedical and Health Informatics
Julius Hannink, Thomas Kautz, Cristian Pasluosta, Karl-Gunter Gassmann, Jochen Klucken, Bjoern Eskofier
Measurement of stride-related, biomechanical parameters is the common rationale for objective gait impairment scoring. State-of-the-art double integration approaches to extract these parameters from inertial sensor data are, however, limited in their clinical applicability due to the underlying assumptions. To overcome this, we present a method to translate the abstract information provided by wearable sensors to context-related expert features based on deep convolutional neural networks. Regarding mobile gait analysis, this enables integration-free and data-driven extraction of a set of eight spatio-temporal stride parameters...
December 8, 2016: IEEE Journal of Biomedical and Health Informatics
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