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IEEE Journal of Biomedical and Health Informatics

Jose Manuel Bote, Joaquin Recas, Francisco Rincon, David Atienza, Roman Hermida
This work presents a new modular and lowcomplexity algorithm for the delineation of the different ECG waves (QRS, P and T peaks, onsets and end). Involving a reduced number of operations per second and having a small memory footprint, this algorithm is intended to perform realtime delineation on resource-constrained embedded systems. The modular design allows the algorithm to automatically adjust the delineation quality in run time to a wide range of modes and sampling rates, from a Ultra-low power mode when no arrhythmia is detected, in which the ECG is sampled at low frequency, to a complete High-accuracy delineation mode in which the ECG is sampled at high frequency and all the ECG fiducial points are detected, in case of arrhythmia...
February 17, 2017: IEEE Journal of Biomedical and Health Informatics
Xiaojin Li, Licong Cui, Shiqiang Tao, Jing Chen, Xiang Zhang, Guo-Qiang Zhang
Automatic identification of sleep stage is an important step in a sleep study. In this paper, we propose a hybrid automatic sleep stage scoring approach, named HyCLASSS, based on single channel electroencephalogram (EEG). HyCLASSS, for the first time, leverages both signal and stage transition features of human sleep for automatic identification of sleep stages. HyCLASSS consists of two parts: a random forest classifier and correction rules. Random forest classifier is trained using thirty EEG signal features, including temporal, frequency and non-linear features...
February 17, 2017: IEEE Journal of Biomedical and Health Informatics
Israr Ibrahim, Sanjay Venkata Oruganti, Ramana Pidaparti
Respiratory diseases such as asthma and acute respiratory distress syndrome as well as acute lung injury involve inflammation at the cellular level. The inflammation process is very complex and is characterized by the emergence of cytokines along with other changes in cellular processes. Due to the complexity of the various constituents that makes up the inflammation dynamics, it is necessary to develop models that can complement experiments to fully understand inflammatory diseases. In this study, we developed a discrete informatics model based on cellular automata (CA) approach to investigate the influence of elastic field (stretch/strain) on the dynamics of inflammation and account for probabilistic adaptation based on statistical interpretation of existing experimental data...
February 15, 2017: IEEE Journal of Biomedical and Health Informatics
Yuh-Jyh Hu, Tien-Hsiung Ku, Yu-Hung Yang, Jia-Ying Shen
Several factors contribute to individual variability in postoperative pain; therefore, individuals consume postoperative analgesics at different rates. Although many statistical studies have analyzed postoperative pain and analgesic consumption, most have identified only the correlation and have not subjected the statistical model to further tests in order to evaluate its predictive accuracy. In this study involving 3052 patients, a multistrategy computational approach was developed for analgesic consumption prediction...
February 13, 2017: IEEE Journal of Biomedical and Health Informatics
Ashok Mondal, Ishan Saxena, Hong Tang, Poulami Banerjee
The main difficulty encountered in interpretation of cardiac sound is interference of noise. The contaminated noise obscures the relevant information which are useful for recognition of heart diseases. The unwanted signals are produced mainly by lungs and surrounding environment. In this paper, a novel heart sound de-noising technique has been introduced based on a combined framework of wavelet packet transform (WPT) and singular value decomposition (SVD). The most informative node of wavelet tree is selected on the criteria of mutual information measurement...
February 13, 2017: IEEE Journal of Biomedical and Health Informatics
Priyanka Mandal, Krishna Tank, Tapas Monday, Chih-Hung Chen, M Jamal Deen
A simple, low-power and wearable health analyzer for early identification and management of some diseases is presented. To achieve this goal, we propose a walking pattern analysis system that uses features such as speed, energy, turn ratio, and bipedal behavior to characterize and classify individuals in distinct walking-ages. A database is constructed from 74 healthy young adults in the age range of 18 to 60 years using the combination of inertial signals from an accelerometer and a gyroscope on a level path including turns...
February 9, 2017: IEEE Journal of Biomedical and Health Informatics
Abhishek Vadnerkar, Sabrina Figueiredo, Nancy Mayo, Robert Kearney
A feature of healthy human walking gait is a clearly defined heel-strike at initial contact, known as heel-to-toe gait. However, a common consequence of ageing is the deterioration of this heel first gait towards a flat foot, or 'shuffling' gait. This leads to a shortened stride length, slowed gait speed, and an increased fall risk. Shuffling gait is normally treated by physiotherapy, however, therapist time is limited and training is restricted to a clinical environment. Gait rehabilitation could be expedited with the use of a device that distinguishes between heel-to-toe and shuffling gait and gives feedback to the user...
February 7, 2017: IEEE Journal of Biomedical and Health Informatics
Dongping Du, Hui Yang, Andrew R Ednie, Eric S Bennett
Cardiac ion channels are highly glycosylated membrane proteins, with up to 30% of the protein's mass containing glycans. Heart diseases often accompany individuals with congenital disorders of glycosylation (CDG). However, cardiac dysfunction among CDG patients is not yet fully understood. There is an urgent need to study how aberrant glycosylation impacts cardiac electrical signaling. Our previous works reported that congenitally reduced sialylation achieved through deletion of the sialyltransferase gene, ST3Gal4, leads to altered gating of voltage-gated Na+ and K+ channels (Nav and Kv, respectively)...
February 6, 2017: IEEE Journal of Biomedical and Health Informatics
Xuechen Li, Linlin Shen, Suhuai Luo
Lung cancer is one of the most deadly diseases. It has a high death rate and its incidence rate has been increasing all over the world. Lung cancer appears as a solitary nodule in chest x-ray radiograph (CXR). Therefore, lung nodule detection in CXR could have a significant impact on early detection of lung cancer. Radiologists define a lung nodule in chest x-ray radiographs as "solitary white nodule-like blob". However, the solitary feature has not been employed for lung nodule detection before. In this paper, a solitary feature-based lung nodule detection method was proposed...
January 31, 2017: IEEE Journal of Biomedical and Health Informatics
Chia-Hsiang Wu, Wan-Hua Tsai, Jia-Kuang Liu, Ying-Hui Chen, Yung-Nien Sun
For better treatment outcomes, dentists usually use a set of parameters for orthodontic evaluation. In this study, a new method is proposed to assist dentists in obtaining reliable assessment of these parameters. The proposed method is based on dental panoramic radiographs and can be divided into four stages: image preprocessing, model training, tooth segmentation and assessment of orthodontic parameters. The image is first normalized and enhanced. Then, the model training stage consists of shape and image model training, energy function training and weight training...
January 27, 2017: IEEE Journal of Biomedical and Health Informatics
Saurin Parikh, Damian Ruiz, Hari Kalva, Gerardo Fernandez-Escribano, Velibor Adzic
Efficient storing and retrieval of medical images has direct impact on reducing costs and improving access in cloud based health care services. JPEG 2000 is currently the commonly used compression format for medical images shared using the DICOM standard. However, new formats such as HEVC can provide better compression efficiency compared to JPEG 2000. Furthermore, JPEG 2000 is not suitable for efficiently storing image series and 3D imagery. Using HEVC, a single format can support all forms of medical images...
January 27, 2017: IEEE Journal of Biomedical and Health Informatics
Benedikt Fasel, Jorg Sporri, Julien Chardonnens, Josef Kroll, Erich Muller, Kamiar Aminian
Inertial sensor drift is usually corrected on a single-sensor unit level. When multiple sensor units are used, mutual information from different units can be exploited for drift correction. This study introduces a method for a drift-reduced estimation of three dimensional (3D) segment orientations and joint angles for motion capture of highly dynamic movements as present in many sports. 3D acceleration measured on two adjacent segments is mapped to the connecting joint. Drift is estimated and reduced based on the mapped accelerations' vector orientation differences in the global frame...
January 26, 2017: IEEE Journal of Biomedical and Health Informatics
Yiting Cheng, Yu-Feng Lin, Kuo-Hwa Chiang, Vincent Tseng
Chronic diseases have been among the major concerns in medical fields since they may cause heavy burden on healthcare resources and disturb the quality of life. In this paper, we propose a novel framework for early assessment on chronic diseases by mining sequential risk patterns with time interval information from diagnostic clinical records using sequential rules mining and classification modeling techniques. With a complete workflow, the proposed framework consists of four phases namely data preprocessing, risk pattern mining, classification modeling and post analysis...
January 25, 2017: IEEE Journal of Biomedical and Health Informatics
Alhassan Khedr, Glenn Gulak
Sharing the medical records of individuals among healthcare providers and researchers around the world can accelerate advances in medical research. While the idea seems increasingly practical due to cloud data services, maintaining patient privacy is of paramount importance. Standard encryption algorithms help protect sensitive data from outside attackers but they cannot be used to compute on this sensitive data while being encrypted. Homomorphic Encryption (HE) presents a very useful tool that can compute on encrypted data without the need to decrypt it...
January 23, 2017: IEEE Journal of Biomedical and Health Informatics
Jun Shi, Xiao Zheng, Yan Li, Qi Zhang, Shihui Ying
The accurate diagnosis of Alzheimer's disease (AD) and its early stage, i.e. mild cognitive impairment (MCI), is essential for timely treatment and possible delay of AD. Fusion of multimodal neuroimaging data, such as magnetic resonance imaging (MRI) and positron emission tomography (PET), has shown its effectiveness for AD diagnosis. The deep polynomial networks (DPN) is a recently proposed deep learning algorithm, which performs well on both large-scale and small-size datasets. In this study, a multimodal stacked DPN (MM-SDPN) algorithm, which MM-SDPN consists of two-stage SDPNs, is proposed to fuse and learn feature representation from multimodal neuroimaging data for AD diagnosis...
January 19, 2017: IEEE Journal of Biomedical and Health Informatics
Pranav Deshpande, M Sabarimalai Manikandan
Accurate determination of glottal instants and electroglottographic (EGG) parameters is most important in voice pathology analysis including multiple voice disorders: neurological, functional, and laryngeal diseases. In this paper, we present a new effective method for reliable detection of glottal instants and EGG parameters from an EGG signal composed of voiced and non-voice segments. In the first stage, we present an adaptive variational mode decomposition (aVMD) based algorithm for suppressing low-frequency artifacts and additive high-frequency noises...
January 17, 2017: IEEE Journal of Biomedical and Health Informatics
Mario Merone, Claudio Pedone, Giuseppe Capasso, Raffaele Antonelli Incalzi, Paolo Soda
Chronic Obstructive Pulmonary Disease (COPD) is a preventable, treatable and slowly progressive disease, whose course is aggravated by a periodic worsening of symptoms and lung function lasting for several days. The development of home telemonitoring systems has made possible to collect symptoms and physiological data in electronic records, boosting the development of decision support systems (DSSs). Current DSSs work with physiological measurements collected by means of several measuring and communication devices as well as with symptoms gathered by questionnaires submitted to COPD subjects...
January 17, 2017: IEEE Journal of Biomedical and Health Informatics
Avan Suinesiaputra, Pierre Ablin, Xenia Alba, Martino Alessandrini, Jack Allen, W Bai, Serkan Cimen, Peter Claes, Brett R Cowan, Jan D'hooge, Nicolas Duchateau, Jan Ehrhardt, Alejandro F Frangi, Ali Gooya, Vicente Grau, Karim Lekadir, Allen Lu, Anirban Mukhopadhyay, Ilkay Oksuz, Xavier Pennec, Marco Pereanez, Catarina Pinto, Paolo Piras, Marc-Michel Rohe, Daniel Rueckert, Maxime Sermesant, Kaleem Siddiqi, Mahdi Tabassian, Luciano Teresi, Sotirios A Tsaftaris, Matthias Wilms, Alistair A Young, Xingyu Zhang, Pau Medrano-Gracia
Statistical shape modeling is a powerful tool for visualizing and quantifying geometric and functional patterns of the heart. After myocardial infarction (MI), the left ventricle typically remodels in response to physiological challenges. Several methods have been proposed in the literature to describe statistical shape changes. Which method best characterizes left ventricular remodeling after MI is an open research question. A better descriptor of remodeling is expected to provide a more accurate evaluation of disease status in MI patients...
January 17, 2017: 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
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