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IEEE Journal of Biomedical and Health Informatics

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https://www.readbyqxmd.com/read/30418929/3d-convolutional-neural-networks-for-automatic-detection-of-pulmonary-nodules-in-chest-ct
#1
Aria Pezeshk, Sardar Hamidian, Nicholas Petrick, Berkman Sahiner
Deep 2D convolutional neural networks (CNNs) have been remarkably successful in producing record-breaking results in a variety of computer vision tasks. It is possible to extend CNNs to three dimensions using 3D kernels to make them suitable for volumetric medical imaging data such as CT or MRI, but this increases the processing time as well as the required number of training samples (due to the higher number of parameters that need to be learned). In this work, we address both of these issues for a 3D CNN implementation through the development of a two-stage computer-aided detection system for automatic detection of pulmonary nodules...
November 9, 2018: IEEE Journal of Biomedical and Health Informatics
https://www.readbyqxmd.com/read/30418928/gait-evaluation-using-procrustes-and-euclidean-distance-matrix-analysis
#2
Arif Reza Anwary, Hongnian Yu, Michael Vassallo
Objective assessment of gait is important in the treatment and rehabilitation of patients with different diseases. In this paper, we propose a gait evaluation system using Procrustes and Euclidean distance matrix analysis. We design and develop an android app to collect real time synchronous accelerometer and gyroscope data from two Inertial Measurement Unit (IMU) sensors through Bluetooth connectivity. The data is collected from 12 young (10 for modelling and 2 for validation) and 20 older subjects. We analyse the data collected from real world for stride, step, stance and swing gait features...
November 9, 2018: IEEE Journal of Biomedical and Health Informatics
https://www.readbyqxmd.com/read/30418892/heart-rate-and-heart-rate-variability-from-single-channel-video-and-ica-integration-of-multiple-signals
#3
Riccardo Favilla, Veronica Chiara Zuccala, Giuseppe Coppini
Unobtrusive monitoring of vital signs is relevant for both medical (patient monitoring) and non-medical applications (e.g. stress and fatigue monitoring). In this work, we focus on the use of imaging photoplethysmography (iPPG). High frame rate videos were acquired by using a monochrome camera and an op- tical band-pass filter (560 ± 20 nm). To enhance iPPG signal, we investigated the use of Independent Component Analysis (ICA) pre-processing applied to iPPG signal from different regions of the face. Methodology was tested on 30 healthy volunteers...
November 7, 2018: IEEE Journal of Biomedical and Health Informatics
https://www.readbyqxmd.com/read/30418890/an-adaptive-data-driven-personalized-advisor-for-increasing-physical-activity
#4
Zhiguo Li, Subhro Das, James Codella, Tian Hao, Kun Lin, Chandramouli Maduri, Ching-Hua Chen
In recent years, there has been growing interest in the use of fitness trackers and smartphone applications for promoting physical activity. Most of these applications use accelerometers to measure the level of activity that users engage in and provide descriptive, interactive reports of a user's step counts. While these reports are data-driven and personalized, any recommendations, if provided, are limited to popular health advice. In our work, we develop an approach for providing data-driven and personalized recommendations for intraday activity planning...
November 7, 2018: IEEE Journal of Biomedical and Health Informatics
https://www.readbyqxmd.com/read/30418891/a-lightweight-multi-section-cnn-for-lung-nodule-classification-and-malignancy-estimation
#5
Pranjal Sahu, Dantong Yu, Mallesham Dasari, Fei Hou, Hong Qin
The size and shape of a nodule are the essential indicators of malignancy in lung cancer diagnosis. However, effectively capturing the nodule's structural information from CT scans in a Computer-aided system is a challenging task. Unlike previous models which proposed computationally intensive deep ensemble models or 3D CNN models, we propose a lightweight, multiple view sampling based Multi-section CNN architecture. The model obtains a nodule's cross-sections from multiple view angles and encodes the nodule's volumetric information into a compact representation by aggregating information from its different cross-sections via a view pooling layer...
November 6, 2018: IEEE Journal of Biomedical and Health Informatics
https://www.readbyqxmd.com/read/30403644/exploring-joint-ab-lstm-with-embedded-lemmas-for-adverse-drug-reaction-discovery
#6
Sara Santiso, Alicia Perez, Arantza Casillas
This work focuses on the detection of Adverse Drug Reactions (ADRs) in Electronic Health Records (EHRs) written in Spanish. The World Health Organization underlines the importance of reporting ADRs for patients' safety. The fact is that ADRs tend to be under-reported in daily hospital praxis. In this context, automatic solutions based on text mining can help to alleviate the workload of experts. Nevertheless, these solutions pose two challenges: 1) EHRs show high lexical variability, the characterization of the events must be able to deal with unseen words or contexts and 2) ADRs are rare events, hence, the system should be robust against skewed class distribution...
November 5, 2018: IEEE Journal of Biomedical and Health Informatics
https://www.readbyqxmd.com/read/30387757/direct-segmentation-based-full-quantification-for-left-ventricle-via-deep-multi-task-regression-learning-network
#7
Xiuquan Du, Renjun Tang, Susu Yin, Yanping Zhang, Shuo Li
Quantitative analysis of the heart is extremely necessary and significant for detecting and diagnosing heart disease, yet there are still some challenges such as the high variability of cardiac structure and the complexity of temporal dynamics. In this study, we propose a new end-to-end segmentation-based deep multi-task regression learning model (Indices-JSQ) to make a holonomic quantitative analysis of the left ventricle (LV), which contains a segmentation network (Img2Contour) and multi-task regression network (Contour2Indices)...
November 1, 2018: IEEE Journal of Biomedical and Health Informatics
https://www.readbyqxmd.com/read/30387756/simultaneous-cell-detection-and-classification-in-bone-marrow-histology-images
#8
Tzu-Hsi Song, Victor Sanchez, Hesham ElDaly, Nasir Rajpoot
Recently, deep learning frameworks have been shown to be successful and efficient in processing digital histology images for various detection and classification tasks. Among these tasks, cell detection and classification are key steps in many computer-assisted diagnosis systems. Traditionally, cell detection and classification is performed as a sequence of two consecutive steps by using two separate deep learning networks, one for detection and the other for classification. This strategy inevitably increases the computational complexity of the training stage...
October 31, 2018: IEEE Journal of Biomedical and Health Informatics
https://www.readbyqxmd.com/read/30387755/intra-slice-motion-correction-of-intravascular-oct-images-using-deep-features
#9
Atefeh Abdolmanafi, Luc Duong, Farida Cheriet, Nagib S Dahdah
Intra-slice motion correction is an important step for analyzing volume variations and pathological formations from intravascular imaging. Optical Coherence Tomography (OCT) has been recently introduced for intravascular imaging and assessment of coronary artery disease. 2D cross-sectional OCT images of coronary arteries play a crucial role to characterize the internal structure of the tissues. Adjacent images could be compounded, however they might not fully match due to motion, which is a major hurdle for analyzing longitudinally each tissue in 3D...
October 31, 2018: IEEE Journal of Biomedical and Health Informatics
https://www.readbyqxmd.com/read/30387754/effects-of-confidence-based-rejection-on-usability-and-error-in-pattern-recognition-based-myoelectric-control
#10
Jason Robertson, Erik Scheme, Kevin Englehart
Rejection of movements based on the confidence in the classification decision has previously been demonstrated to improve usability of pattern recognition based myoelectric control. To this point, however, the optimal rejection threshold has been determined heuristically, and it is not known how different thresholds affect the trade-off between error mitigation and false rejections in real-time, closed-loop control. To answer this question, 24 able-bodied subjects completed a real-time Fitts' law-style virtual cursor control task using a support vector machine (SVM) classifier...
October 31, 2018: IEEE Journal of Biomedical and Health Informatics
https://www.readbyqxmd.com/read/30387753/large-scale-multi-class-image-based-cell-classification-with-deep-learning
#11
Nan Meng, Edmund Lam, Kevin Kin Man Tsia, Hayden Kwok-Hay So
Recent advances in ultra-high-throughput optical microscopy have enabled a new generation of cell classification methodologies using image-based cell phenotypes alone. In contrast to the current single-cell analysis techniques that rely solely on slow and costly genetic/epigenetic analyses, these image-based classification methods allow morphological profiling and screening of thousands or even millions of single cells at a fraction of the cost. Furthermore, they have demonstrated the statistical significance required for understanding the role of cell heterogeneity in diverse biological applications, ranging from cancer screening to drug candidate identification/validation processes...
October 31, 2018: IEEE Journal of Biomedical and Health Informatics
https://www.readbyqxmd.com/read/30387752/towards-emotionally-adaptive-virtual-reality-for-mental-health-applications
#12
Sergi Bermudez I Badia, Luis Velez Quintero, Monica S Cameirao, Alice Chirico, Stefano Triberti, Pietro Cipresso, Andrea Gaggioli
Here we introduce the design and preliminary validation of a general-purpose architecture for affective-driven procedural content generation in Virtual Reality (VR) applications in mental health and wellbeing. The architecture supports seven commercial physiological sensing technologies and can be deployed in immersive and non-immersive VR systems. To demonstrate the concept, we developed the "The Emotional Labyrinth", a non-linear scenario in which navigation in a procedurally-generated 3D maze is entirely decided by the user, and whose features are dynamically adapted according to a set of emotional states...
October 31, 2018: IEEE Journal of Biomedical and Health Informatics
https://www.readbyqxmd.com/read/30387751/wearable-motion-based-heart-rate-at-rest-a-workplace-evaluation
#13
Javier Hernandez, Daniel McDuff, Karen S Quigley, Pattie Maes, Rosalind W Picard
This work studies the feasibility of using low-cost motion sensors to provide opportunistic heart rate assessments from ballistocardiographic signals during restful periods of daily life. Three wearable devices were used to capture peripheral motions at specific body locations (head, wrist and trouser pocket) of 15 participants during five regular workdays each. Three methods were implemented to extract heart rate from motion data and their performance was compared to those obtained with an FDA-cleared device...
October 29, 2018: IEEE Journal of Biomedical and Health Informatics
https://www.readbyqxmd.com/read/30371397/a-machine-learning-approach-for-detection-and-quantification-of-qrs-fragmentation
#14
Griet Goovaerts, Sibasankar Padhy, Bert Vandenberk, Carolina Varon, Rik Willems, Sabine Van Huffel
OBJECTIVE: Fragmented QRS (fQRS) is an accessible biomarker and indication of myocardial scarring that can be detected from the electrocardiogram (ECG). Nowadays, fQRS scoring is done on a visual basis, which is time-consuming and leads to subjective results. This study proposes an automated method to detect and quantify fQRS in a continuous way using features extracted from Variational Mode Decomposition (VMD) and Phase-Rectified Signal Averaging (PRSA). METHODS: In the proposed framework, QRS complexes in the ECG signals were first segmented using VMD...
October 29, 2018: IEEE Journal of Biomedical and Health Informatics
https://www.readbyqxmd.com/read/30369459/multifractal-analysis-of-uterine-electromyography-signals-for-the-assessment-of-progression-of-pregnancy-in-term-conditions
#15
Punitha Namadurai, Vardhini Padmanabhan, Ramakrishnan Swaminathan
OBJECTIVES: The objectives of this work are to examine the source of multifractality in uterine Electromyography (EMG) signals and to study the progression of pregnancy in the term (gestation period > 37 weeks) conditions using Multifractal Detrending Moving Average (MFDMA) algorithm. METHODS: The signals for the study, considered from an online database, are obtained from the surface of abdomen during the second (T1) and third trimester (T2). The existence of multifractality is tested using Hurst and scaling exponents...
October 25, 2018: IEEE Journal of Biomedical and Health Informatics
https://www.readbyqxmd.com/read/30369458/a-bayesian-multiple-kernel-learning-algorithm-for-ssvep-bci-detection
#16
Vangelis P Oikonomou, Spiros Nikolopoulos, Ioannis Kompatsiaris
Our work deals with the classification of Steady State Visual Evoked Potentials (SSVEP) which isa multiclass classification problem addressed in SSVEPbased Brain Computer Interfaces (BCIs). In particular, our method named MultiLRM MKL, uses multiple linear regression models under a Sparse Bayesian Learning (SBL)framework to discriminate between the SSVEP classes. The regression coefficients of each model are learned using the Variational Bayesian (VB) framework and the kernel trick is adopted not only for reducing the computational cost of our method, but also for enabling the combination of different kernel spaces...
October 25, 2018: IEEE Journal of Biomedical and Health Informatics
https://www.readbyqxmd.com/read/30369457/a-machine-learning-framework-for-automatic-and-continuous-mmn-detection-with-preliminary-results-for-coma-outcome-prediction
#17
Narges Armanfard, Majid Komeili, James P Reilly, John Connoly
Mismatch Negativity (MMN) is a component of the event-related potential (ERP) that is elicited through an odd-ball paradigm. The existence of the MMN in a coma patient has a good correlation with coma emergence; however, this component can be difficult to detect. Previously, MMN detection was based on visual inspection of the averaged ERPs by a skilled clinician, a process which is expensive and not always feasible in practice. In this paper we propose a practical machine learning (ML) based approach for detection of the MMN component, thus improving the accuracy of prediction of emergence from coma...
October 24, 2018: IEEE Journal of Biomedical and Health Informatics
https://www.readbyqxmd.com/read/30369456/design-of-a-clinical-decision-support-system-for-predicting-erectile-dysfunction-in-men-using-nhird-dataset
#18
Yung-Fu Chen, Chih-Sheng Lin, Chun-Fu Hong, D J Lee, Changming Sun, Hsuan-Hung Lin
Erectile dysfunction (ED) affects millions of men worldwide. Men with ED generally complain failure to attain or maintain an adequate erection during sexual activity. The prevalence of ED is strongly correlated with age, affecting about 40% of men at age 40 and nearly 70% at age 70. A variety of chronic diseases, including diabetes, ischemic heart disease, congestive heart failure, hypertension, depression, chronic renal failure, obstructive sleep apnea, prostate disease, gout, and sleep disorder, were reported to be associated with ED...
October 23, 2018: IEEE Journal of Biomedical and Health Informatics
https://www.readbyqxmd.com/read/30346297/efficient-mining-template-of-predictive-temporal-clinical-event-patterns-from-patient-electronic-medical-records
#19
Jianqiang Li, Xiyue Tan, Xi Xu, Fei Wang
Exploring the temporal relationship among events in patient Electronic Medical Records (EMR) is an important problem in biomedical informatics and the results can reveal patients' impending disease conditions. In this paper, we investigate the problem of mining patterns from a sequence of point events, i.e., we only have the information on when the event happens but no duration or numerical value available. We propose a whole pipeline, including event preprocessing, pattern mining and outcome analysis to mine the patterns and evaluate their effectiveness and discriminative power...
October 22, 2018: IEEE Journal of Biomedical and Health Informatics
https://www.readbyqxmd.com/read/30346296/computational-cardiology
#20
Lambros Athanasiou, Farhad Rikhtegar Nezami, Elazer R Edelman
Computational cardiology is the scientific field devoted to the development of methodologies that enhance our mechanistic understanding, diagnosis and treatment of cardiovascular disease. In this regard, the field embraces the extraordinary pace of discovery in imaging, computational modeling and cardiovascular informatics at the intersection of atherogenesis and vascular biology. This article highlights existing methods, practices, and computational models and proposes new strategies to support a multidisciplinary effort in this space...
October 19, 2018: IEEE Journal of Biomedical and Health Informatics
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