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IEEE Journal of Biomedical and Health Informatics

Nahed Jalloul, Fabienne Poree, Geoffrey Viardot, Phillipe L'Hostis, Guy Carrault
In this paper, we perform complex network analysis on a connectivity dataset retrieved from a monitoring system in order to classify simple daily activities. The monitoring system is composed of a set of wearable sensing modules positioned on the subject's body and the connectivity data consists of the correlation between each pair of modules. A number of network measures are then computed followed by the application of statistical significance and feature selection methods. These methods were implemented for the purpose of reducing the total number of modules in the monitoring system required to provide accurate activity classification...
October 12, 2017: IEEE Journal of Biomedical and Health Informatics
Poonam Zham, Sridhar Arjunan, Sanjay Raghav, Dinesh Kant Kumar
BACKGROUND: Change of handwriting can be an early marker for severity of Parkinson's disease but suffers from poor sensitivity and specificity due to inter-subject variations. AIM: This study has investigated the group-difference in the dynamic features during sketching of spiral between PD and control subjects with the aim of developing an accurate method for diagnosing PD patients. METHOD: Dynamic handwriting features were computed for 206 specimens collected from 62 Subjects (31 Parkinson's and 31 Controls)...
October 11, 2017: IEEE Journal of Biomedical and Health Informatics
Fang Chen, Zhe Zhao, Jia Liu, Cong Gao, Xiuyun Su, Jingxin Zhao, Peifu Tang, Hongen Liao
Intramedullary (IM) nail implantation is currently the standard treatment for femoral intertrochanteric fractures. However, individual differences in femur cavity bring a challenge in designing well-matched IM nails and cause difficulties in IM nail implantation. Therefore, there is an intense need to analyze femur cavities to predict difficulties in IM nail implantation to assist the design of IM nails. This study proposed a method to automatically identify subtypes of femur cavities that exhibit differences in potential difficulties in nail implantation by clustering the morphological features of femur models...
October 11, 2017: IEEE Journal of Biomedical and Health Informatics
Ola Ahmad, Herve Lombaert, Stefan Parent, Hubert Labelle, Farida Cheriet
OBJECTIVE: This paper aims at evaluating the effect of spinal surgery on the torso shape appearance of adolescent patients. Current methods that assess the surgical outcome on the trunk shape are limited to its global asymmetry or rely on unreliable manual measurements. METHOD: We introduce a novel framework to evaluate pre- to post-operative local asymmetry changes using a spectral representation of the torso shape, more specifically, the Laplacian spectrum (eigenvalues and eigenvectors) of a graph...
October 10, 2017: IEEE Journal of Biomedical and Health Informatics
Sina Reulecke, Sonia Charleston Villalobos, Andreas Voss, Ramon Gonzalez-Camarena, Jesus Antonio Gonzalez-Hermosillo, Jatziri Gaitan, Guadalupe Hernandez-Pacheco, Rico Schroeder, Tomas Aljama-Corrales
The effect of an orthostatic stress on cardiovascular and respiratory complexity was investigated to detect impaired autonomic regulation in patients with vasovagal syncope (VVS). Sixteen female patients and 12 age-matched healthy female subjects were enrolled in a passive 70° head-up tilt test. Also, 12 age-matched healthy male subjects were enrolled to study gender differences. Analysis was performed dynamically using various short-term (5 min) windows shifted by 1 min as well as by 20 min of orthostatic phase (OP) to evaluate local and global complexity...
October 9, 2017: IEEE Journal of Biomedical and Health Informatics
Jerome Thevenot, Miguel Bordallo Lopez, Abdenour Hadid
Automatic medical diagnosis is an emerging center of interest in computer vision as it provides unobtrusive objective information on a patient's condition. The face, as a mirror of health status, can reveal symptomatic indications of specific diseases. Thus, the detection of facial abnormalities or atypical features is at upmost importance when it comes to medical diagnostics. This survey aims to give an overview of the recent developments in medical diagnostics from facial images based on computer vision methods...
October 5, 2017: IEEE Journal of Biomedical and Health Informatics
Jose Maria Perez-Macias, Mirja Tenhunen, Alpo Varri, Sari-Leena Himanen, Jari Viik
Snoring (SN) is an early sign of upper airway dysfunction, and it is strongly associated with obstructive sleep apnea (OSA). SN detection is important to monitor SN objectively and to improve the diagnostic sensitivity of sleep-disordered breathing (SDB). In this study, an automatic snore detection method using an Emfit (Electromechanical film transducer) signal is presented. Representative polysomnographs of normal breathing (NB) and SN periods from 30 subjects were selected. Individual SN events were identified using source separation applying nonnegative matrix factorization deconvolution (NMFD)...
September 28, 2017: IEEE Journal of Biomedical and Health Informatics
S Lekha, Suchetha M
Non-invasive diabetes prediction has been gaining prominence over the last decade. Among many human serums evaluated, human breath emerges as a promising option with acetone levels in breath exhibiting a good correlation to blood glucose levels. Such correlation establishes acetone as an acceptable bio-marker for diabetes. The most common data analysis strategies to analyze the bio-markers in breath for disease detection use feature extraction and classification algorithms. However snags such as computational cost and lack of optimal feature selection on application to real time signals reduce the efficiency of such analysis...
September 28, 2017: IEEE Journal of Biomedical and Health Informatics
Hui Yan, Pieter Van Gorp, Uzay Kaymak, Xudong Lu, Lei Ji, Choo Chiap Chiau, Hendriks H M Korsten, Huilong Duan
Clinical pathways (CPs) are popular healthcare management tools to standardise care and ensure quality. Analysing CP compliance levels and variances is known to be useful for training and CP redesign purposes. Flexible semantics of the Business Process Model and Notation (BPMN) language has been shown to be useful for the modeling and analysis of complex protocols. However, in practical cases one may want to exploit that CPs often have the form of Task-Time matrices. This paper presents a new method parsing complex BPMN models and aligning traces to the models heuristically...
September 18, 2017: IEEE Journal of Biomedical and Health Informatics
Ashok Kumar Das, Mohammad Wazid, Neeraj Kumar, Muhammad Khurram Khan, Kim-Kwang Raymond Choo, YoungHo Park
Wearable devices are used in various applications to collect information including step information, sleeping cycles, workout statistics, and health related information. Due to the nature and richness of the data collected by such devices, it is important to ensure the security of the collected data. This paper presents a new lightweight authentication scheme suitable for wearable device deployment. The scheme allows a user to mutually authenticate his/her wearable device(s) and the mobile terminal (e.g., Android and iOS device) and establish a session key among these devices (worn and carried by the same user) for secure communication between the wearable device and the mobile terminal...
September 18, 2017: IEEE Journal of Biomedical and Health Informatics
Shweta Jain, Varun Bajaj, Anil Kumar
Electrocardiograph (ECG) denoising is the most important step in diagnosis of heart related diseases, as the diagnosis gets influenced with noises. In this paper, a new method for ECG denoising is proposed, which incorporates empirical mode decomposition algorithm and Riemann Liouvelle (RL) fractional integral filtering. In the proposed method, noisy ECG signal is decomposed into its intrinsic mode functions (IMFs); from which noisy IMFs are identified by proposed noisy-IMFs identification methodology. RL fractional integral filtering is applied on noisy IMFs to get denoised IMFs; ECG signal is reconstructed with denoised IMFs and remaining signal dominant IMFs to obtain noise-free ECG signal...
September 18, 2017: IEEE Journal of Biomedical and Health Informatics
A H Ansari, P J Cherian, A Caicedo, K Jansen, A Dereymaeker, L De Wispelaere, C Dielman, J Vervisch, P Govaert, M De Vos, G Naulaers, S Van Huffel
In neonatal intensive care units, there is a need for around the clock monitoring of EEG, especially for recognizing seizures. An automated seizure detector with an acceptable performance can partly fill this need. In order to develop a detector, an extensive dataset labeled by experts is needed. However, accurately defining neonatal seizures on EEG is a challenge, especially when seizure discharges do not meet exact definitions of repetitiveness or evolution in amplitude and frequency. When several readers score seizures independently, disagreement can be high...
September 11, 2017: IEEE Journal of Biomedical and Health Informatics
Hanguang Xiao, Mark Butlin, Isabella Tan, Ahmad Qasem, Alberto Avolio
OBJECTIVE: To validate the feasibility of the estimation of pulse transit time (PTT) by artificial neural network (ANN) from radial pressure waveform alone. METHODS: A cascade ANN with ten-fold cross validation was applied to invasively and simultaneously recorded aortic and radial pressure waveforms during rest and nitroglycerin infusion (n=62) for the estimation of mean and beat-to-beat PTT. The results of the ANN models were compared to a multiple linear regression (LR) model when the features of radial arterial pressure waveform in time and frequency domains were used as the predictors of the models...
September 1, 2017: IEEE Journal of Biomedical and Health Informatics
Aitziber Atutxa, Alicia Perez, Arantza Casillas
This work focuses on data mining applied to the clinical documentation domain. Diagnostic Terms (DTs) are used as keywords to retrieve valuable information from Electronic Health Records (EHRs). Indeed, they are encoded manually by experts following the International Classification of Diseases (ICD). The goal of this work is to explore the aid of text mining on DT encoding. From the machine learning (ML) perspective, this is a high-dimensional classification task, as it comprises thousands of codes. This work delves into a robust representation of the instances to improve ML results...
August 24, 2017: IEEE Journal of Biomedical and Health Informatics
Carlos A Robles-Rubio, Karen A Brown, Robert E Kearney
Manual scoring (MS) of cardiorespiratory signals is the gold standard method for the analysis of respiratory data in sleep laboratories. In MS, trained, expert scorers characterize respiratory patterns by scrolling through a data record and visually identifying patterns. However, MS is limited by high intra- and inter-scorer variability and subjectivity. A strategy to mitigate this is to analyze the same respiratory data multiple times and generate a consensus. This consensus is generally determined by a majority vote (MV), where the most frequent pattern is selected as the true pattern...
August 24, 2017: IEEE Journal of Biomedical and Health Informatics
Peyman Gholami, Mohammad Ali Ahmadi-Pajouh, Nabiollah Abolftahi, Ghassan Hamarneh, Mohammad Kayvanrad
To provide a proof-of-concept tool for segmenting chronic wounds and transmitting the results as instructions and coordinates to a bioprinter robot and thus facilitate the treatment of chronic wounds.
August 23, 2017: IEEE Journal of Biomedical and Health Informatics
Fan Pan, Peiyu He, Chengyu Liu, Taiyong Li, Alan Murray, Dingchang Zheng
Korotkoff sounds are known to change their characteristics during blood pressure (BP) measurement, resulting in some uncertainties for systolic and diastolic pressure (SBP and DBP) determinations. The aim of this study was to assess the variation of Korotkoff sounds during BP measurement by examining all stethoscope sounds associated with each heartbeat from above systole to below diastole during linear cuff deflation. Three repeat BP measurements were taken from 140 healthy subjects (age 21 to 73 years; 62 female and 78 male) by a trained observer, giving 420 measurements...
November 2017: IEEE Journal of Biomedical and Health Informatics
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 paper, we study the problem of accurate estimation of missing multimodal inertial data and we propose a classification framework that considers the reconstruction of subsampled data during the test phase...
November 2017: IEEE Journal of Biomedical and Health Informatics
Rosa Sanchez-Guerrero, Florina Almenarez Mendoza, Daniel Diaz-Sanchez, Patricia Arias Cabarcos, Andres Marin Lopez
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...
November 2017: IEEE Journal of Biomedical and Health Informatics
Hamed Danandeh Hesar, Maryam Mohebbi
Model-based Bayesian frameworks have a common problem in processing electrocardiogram (ECG) signals with sudden morphological changes. This situation often happens in the case of arrhythmias where ECGs do not obey the predefined state models. To solve this problem, in this paper, a model-based Bayesian denoising framework is proposed using marginalized particle-extended Kalman filter (MP-EKF), variational mode decomposition, and a novel fuzzy-based adaptive particle weighting strategy. This strategy helps MP-EKF to perform well even when the morphology of signal does not comply with the predefined dynamic model...
November 2017: IEEE Journal of Biomedical and Health Informatics
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