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IEEE Journal of Biomedical and Health Informatics

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https://www.readbyqxmd.com/read/28922133/aligning-event-logs-to-task-time-matrix-clinical-pathways-in-bpmn-for-variance-analysis
#1
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
https://www.readbyqxmd.com/read/28922132/design-of-secure-and-lightweight-authentication-protocol-for-wearable-devices-environment
#2
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
https://www.readbyqxmd.com/read/28922131/riemann-liouvelle-fractional-integral-based-empirical-mode-decomposition-for-ecg-denoising
#3
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
https://www.readbyqxmd.com/read/28910781/weighted-performance-metrics-for-automatic-neonatal-seizure-detection-using-multi-scored-eeg-data
#4
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
https://www.readbyqxmd.com/read/28880196/estimation-of-pulse-transit-time-from-radial-pressure-waveform-alone-by-artificial-neural-network
#5
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
https://www.readbyqxmd.com/read/28858819/machine-learning-approaches-on-diagnostic-term-encoding-with-the-icd-for-clinical-documentation
#6
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
https://www.readbyqxmd.com/read/28858818/optimal-classification-of-respiratory-patterns-from-manual-analyses-using-expectation-maximization
#7
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
https://www.readbyqxmd.com/read/28841560/segmentation-and-measurement-of-chronic-wounds-for-bioprinting
#8
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
https://www.readbyqxmd.com/read/28829323/channel-deviation-based-power-control-in-body-area-networks
#9
Son Dinh Van, Simon L Cotton, David B Smith
Internet enabled body area networks (BANs) will form a core part of future remote health monitoring and Ambient Assisted Living (AAL) technology. In BAN applications, due to the dynamic nature of human activity, the off-body BAN channel can be prone to deep fading caused by body shadowing and multipath fading. Using this knowledge, we present some novel practical adaptive power control protocols based on the channel deviation to simultaneously prolong the lifetime of wearable devices and reduce outage probability...
August 18, 2017: IEEE Journal of Biomedical and Health Informatics
https://www.readbyqxmd.com/read/28829322/success-rate-and-technical-quality-of-home-polysomnography-with-self-applicable-electrode-set-in-subjects-with-possible-sleep-bruxism
#10
Tomi Miettinen, Katja Myllymaa, Susanna Westeren-Punnonen, Jari Ahlberg, Taina Hukkanen, Juha Toyras, Reijo Lappalainen, Esa Mervaala, Kirsi Sipila, Sami Myllymaa
Using sleep laboratory polysomnography (PSG) is restricted for the diagnosis of only the most severe sleep disorders due to its low availability and high cost. Home PSG is more affordable, but applying conventional electroencephalography (EEG) electrodes increases its overall complexity and lowers the availability. Simple, self-administered single-channel EEG monitors on the other hand suffer from poor reliability. In this study, we aimed to quantify the reliability of self-administrated home PSG recordings conducted with a newly designed ambulatory electrode set (AES) that enables multi-channel EEG, electrooculography, electromyography and electrocardiography recordings...
August 18, 2017: IEEE Journal of Biomedical and Health Informatics
https://www.readbyqxmd.com/read/28829321/designing-of-ground-truth-annotated-dbt-tu-ju-breast-thermogram-database-towards-early-abnormality-prediction
#11
Mrinal Kanti Bhowmik, Usha Rani Gogoi, Gautam Majumdar, Debotosh Bhattacharjee, Dhritiman Datta, Anjan Kumar Ghosh
The advancement of research in a specific area of clinical diagnosis crucially depends on the availability and quality of the radiology and other test related databases accompanied by ground truth and additional necessary medical findings. The paper describes the creation of the Department of Biotechnology-Tripura University-Jadavpur University (DBT-TU-JU) breast thermogram database. The objective of creating the DBT-TU-JU database is to provide a breast thermogram database that is annotated with the ground truth images of the suspicious regions...
August 17, 2017: IEEE Journal of Biomedical and Health Informatics
https://www.readbyqxmd.com/read/28816683/cardiorespiratory-model-based-data-driven-approach-for-sleep-apnea-detection
#12
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
#13
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
#14
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
#15
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
#16
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
#17
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
#18
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
#19
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
#20
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
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