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IEEE Journal of Biomedical and Health Informatics

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https://www.readbyqxmd.com/read/28333651/automated-ecg-noise-detection-and-classification-system-for-unsupervised-healthcare-monitoring
#1
Udit Satija, Barathram Ramkumar, M Sabarimalai Manikandan
OBJECTIVE: Automatic detection and classification of noises can play a vital role in the development of robust unsupervised electrocardiogram (ECG) analysis systems. This paper proposes a novel unified framework for automatic detection, localization and classification of single and combined ECG noises. METHODS: The proposed framework consists of the modified ensemble empirical mode decomposition (CEEMD), the shortterm temporal feature extraction, and the decision rules based noise detection and classification...
March 22, 2017: IEEE Journal of Biomedical and Health Informatics
https://www.readbyqxmd.com/read/28333650/validation-of-static-and-dynamic-balance-assessment-using-microsoft-kinect-for-young-and-elderly-populations
#2
Moataz Eltoukhy, Christopher Kuenze, Jeonghoon Oh, Joseph Signorile
Reduction in balance is an indicator of fall risk, and therefore, an accurate and cost effective balance assessment tool is essential for prescribing effective postural control strategies. This study established the validity of the Kinect v2 sensor in assessing center of mass (CoM) excursion and velocity during single leg balance and voluntary ankle sway tasks among young and elderly subjects. We compared balance outcome measures (anteroposterior (AP) and mediolateral (ML) CoM excursion and velocity and average sway length) to a traditional three-dimensional motion analysis system...
March 22, 2017: IEEE Journal of Biomedical and Health Informatics
https://www.readbyqxmd.com/read/28333649/multi-scale-rotation-invariant-convolutional-neural-networks-for-lung-texture-classification
#3
Qiangchang Wang, Yuanjie Zheng, Gongping Yang, Weidong Jin, Xinjian Chen, Yilong Yin
We propose a new Multi-scale Rotation-invariant Convolutional Neural Network (MRCNN) model for classifying various lung tissue types on high-resolution computed tomography (HRCT). MRCNN employs Gabor-local binary pattern (Gabor-LBP) which introduces a good property in image analysis - invariance to image scales and rotations. In addition, we offer an approach to deal with the problems caused by imbalanced number of samples between different classes in most of the existing works, accomplished by changing the overlapping size between the adjacent patches...
March 21, 2017: IEEE Journal of Biomedical and Health Informatics
https://www.readbyqxmd.com/read/28333647/automatic-choroidal-layer-segmentation-using-markov-random-field-and-level-set-method
#4
Chuang Wang, Yongmin Li, Ya Xing Wang
The choroid is an important vascular layer that supplies oxygen and nourishment to the retina. The changes in thickness of the choroid have been hypothesised to relate to a number of retinal diseases in the pathophysiology. In this work, an automatic method is proposed for segmenting the choroidal layer from macular images by using the level set framework. The 3D nonlinear anisotropic diffusion filter is used to remove all the OCT imaging artifacts including the speckle noise and to enhance the contrast. The distance regularisation and edge constraint terms are embedded into the level set method to avoid the irregular and small regions and keep information about the boundary between the choroid and sclera...
March 20, 2017: IEEE Journal of Biomedical and Health Informatics
https://www.readbyqxmd.com/read/28328520/a-natural-language-processing-framework-for-assessing-hospital-readmissions-for-patients-with-copd
#5
Ankur Agarwal, Christopher Baechle, Ravi Behara, Xingquan Zhu
With the passage of recent federal legislation many medical institutions are now responsible for reaching target hospital readmission rates. Chronic diseases account for many hospital readmissions and Chronic Obstructive Pulmonary Disease has been recently added to the list of diseases for which the United States government penalizes hospitals incurring excessive readmissions. Though there have been efforts to statistically predict those most in danger of readmission, few have focused primarily on unstructured clinical notes...
March 17, 2017: IEEE Journal of Biomedical and Health Informatics
https://www.readbyqxmd.com/read/28328519/a-framework-for-mixed-type-multi-outcome-prediction-with-applications-in-healthcare
#6
Budhaditya Saha, Sunil Gupta, Dinh Phung, Svetha Venkatesh
Health analysis often involves prediction of multiple outcomes of mixed-type. Existing work is restrictive to either a limited number or specific outcome types. We propose a framework for mixed-type multi-outcome prediction. Our proposed framework proposes a cumulative loss function composed of a specific loss function for each outcome type - as an example, least square (continuous outcome), hinge (binary outcome), poisson (count outcome) and exponential (non-negative outcome). To model these outcomes jointly, we impose a commonality across the prediction parameters through a common matrix-Normal prior...
March 16, 2017: IEEE Journal of Biomedical and Health Informatics
https://www.readbyqxmd.com/read/28320682/deformable-registration-based-super-resolution-for-isotropic-reconstruction-of-4d-mri-volumes
#7
Geetha Soujanya Chilla, Cher Heng Tan, Chueh Loo Poh
Multi-plane super-resolution (SR) has been widely employed for resolution improvement of MR images. However, this has mostly been limited to MRI acquisitions with rigid motion. In cases of non-rigid motion, volumes are usually pre-registered using deformable registration methods before SR reconstruction. The pre-registered images are then used as input for the SR reconstruction. Since deformable registration involves smoothening of the inputs, using pre-registered inputs could lead to loss in information in SR reconstructions...
March 13, 2017: IEEE Journal of Biomedical and Health Informatics
https://www.readbyqxmd.com/read/28333648/mobile-stride-length-estimation-with-deep-convolutional-neural-networks
#8
Julius Hannink, Thomas Kautz, Cristian Pasluosta, Jens Barth, Samuel Schulein, Karl-Gunter Gassmann, Jochen Klucken, Bjoern Eskofier
OBJECTIVE: Accurate estimation of spatial gait characteristics is critical to assess motor impairments resulting from neurological or musculoskeletal disease. Currently, however, methodological constraints limit clinical applicability of state-ofthe- art double integration approaches to gait patterns with a clear zero-velocity phase. METHODS: We describe a novel approach to stride length estimation that uses deep convolutional neural networks to map stride-specific inertial sensor data to the resulting stride length...
March 9, 2017: IEEE Journal of Biomedical and Health Informatics
https://www.readbyqxmd.com/read/28287995/parameters-extracted-from-arterial-pulse-waves-as-markers-of-atherosclerotic-changes-performance-and-repeatability
#9
Mikko Peltokangas, Anca A Telembeci, Jarmo Verho, Ville M Mattila, Pekka Romsi, Antti Vehkaoja, Jukka Lekkala, Niku Oksala
Arterial diseases are significant and increasing cause of mortality and morbidity. In this study, we analyze and compare the discrimination capability of different arterial pulse wave (PW) based indices, both earlier proposed and novel ones, for describing the vascular health. The repeatability of the indices is also evaluated. Both volume PWs and dynamic pressure PWs are recorded by using photoplethysmographic and electromechanical film (EMFi) sensors connected to a wireless body sensor network. The study population consists of 82 subjects, 30 atherosclerotic patients and 52 control subjects...
March 8, 2017: IEEE Journal of Biomedical and Health Informatics
https://www.readbyqxmd.com/read/28287994/ensemble-empirical-mode-decomposition-with-principal-component-analysis-a-novel-approach-for-extracting-respiratory-rate-and-heart-rate-from-photoplethysmographic-signal
#10
Mohammod Abdul Motin, Chandan Karmakar, Marimuthu Palaniswami
The photoplethysmographic (PPG) signal measures the local variations of blood volume in tissues, reflecting the peripheral pulse modulated by cardiac activity, respiration and other physiological effects. Therefore, PPG can be used to extract the vital cardiorespiratory signals like heart rate (HR), and respiratory rate (RR) and this will reduce the number of sensors connected to the patient's body for recording these vital signs. In this paper, we propose an algorithm based on ensemble empirical mode decomposition with principal component analysis (EEMD-PCA) as a novel approach to estimate HR and RR simultaneously from PPG signal...
March 7, 2017: IEEE Journal of Biomedical and Health Informatics
https://www.readbyqxmd.com/read/28278487/meal-detection-and-carbohydrate-estimation-using-continuous-glucose-sensor-data
#11
Sediqeh Samadi, Kamuran Turksoy, Iman Hajizadeh, Jianyuan Feng, Mert Sevil, Ali Cinar
A meal detection and meal size estimation algorithm is developed for use in artificial pancreas (AP) control systems for people with type 1 diabetes. The algorithm detects the consumption of a meal and estimates its carbohydrate (CHO) amount to determine the appropriate dose of insulin bolus for a meal. It can be used in AP systems without manual meal announcements, or as a safety feature for people who may forget entering meal information manually. Using qualitative representation of the filtered continuous glucose monitor signal, a time period labeled as meal flag is identified...
March 3, 2017: IEEE Journal of Biomedical and Health Informatics
https://www.readbyqxmd.com/read/28278486/differences-between-gait-on-stairs-and-flat-surfaces-in-relation-to-fall-risk-and-future-falls
#12
Kejia Wang, Kim Delbaere, Matthew Brodie, Nigel Lovell, Lauren Kark, Stephen Lord, Stephen Redmond
We used body-worn inertial sensors to quantify differences in semi-free-living gait between stairs and on normal flat ground in older adults, and investigated the utility of assessing gait on these terrains for predicting the occurrence of multiple falls. 82 community-dwelling older adults wore two inertial sensors, on the lower back and the right ankle, during several bouts of walking on flat surfaces and up and down stairs, in between rests and activities of daily living. Derived from the vertical acceleration at the lower back, step rate was calculated from the signal's fundamental frequency...
March 3, 2017: IEEE Journal of Biomedical and Health Informatics
https://www.readbyqxmd.com/read/28278485/dynamic-multimodal-measurement-of-depression-severity-using-deep-autoencoding
#13
Hamdi Dibeklioglu, Zakia Hammal, Jeffrey F Cohn
Depression is one of the most common psychiatric disorders worldwide, with over 350 million people affected. Current methods to screen for and assess depression depend almost entirely on clinical interviews and self-report scales. While useful, such measures lack objective, systematic, and efficient ways of incorporating behavioral observations that are strong indicators of depression presence and severity. Using dynamics of facial and head movement and vocalization, we trained classifiers to detect three levels of depression severity...
March 2, 2017: IEEE Journal of Biomedical and Health Informatics
https://www.readbyqxmd.com/read/28287993/prediction-of-adverse-events-in-patients-undergoing-major-cardiovascular-procedures
#14
Bobak Mortazavi, Nihar Desai, Jing Zhang, Andreas Coppi, Frederick Warner, Harlan Krumholz, Sahand Negahban
Electronic health records (EHR) provide opportunities to leverage vast arrays of data to help prevent adverse events, improve patient outcomes, and reduce hospital costs. This work develops a postoperative complications prediction system by extracting data from the EHR and creating features. The analytic engine then provides model accuracy, calibration, feature ranking, and personalized feature responses. This allows clinicians to interpret the likelihood of an adverse event occurring, general causes for these events, and the contributing factors for each specific patient...
March 1, 2017: IEEE Journal of Biomedical and Health Informatics
https://www.readbyqxmd.com/read/28237937/investigation-of-viscoelasticity-in-the-relationship-between-carotid-artery-blood-pressure-and-distal-pulse-volume-waveforms
#15
Jong Chan Lee, Zahra Ghasemi, Chang-Sei Kim, Hao-Min Cheng, Chen-Huan Chen, Shih-Hsien Sung, Ramakrishna Mukkamala, Jin-Oh Hahn
We investigated the relationship between carotid artery blood pressure (BP) and distal pulse volume waveforms (PVRs) via subject-specific mathematical modeling. We conceived three physical models to define the relationship: a tube-load model augmented with a gain (TLG), Voigt (TLV) and standard linear solid (TLS) models. We compared these models using PVRs measured via BP cuffs at an upper arm and an ankle as well as carotid artery tonometry waveform collected from 133 subjects. At both upper arm and ankle, PVR was related to carotid artery tonometry by TLV and TLS models better than by TLG model; when root-mean-squared over all the subjects, the systolic and diastolic BP errors between measured carotid artery tonometry waveform and the one estimated from distal PVR reduced from 4...
February 22, 2017: IEEE Journal of Biomedical and Health Informatics
https://www.readbyqxmd.com/read/28237936/onset-and-offset-estimation-of-the-neural-inspiratory-time-in-surface-diaphragm-electromyography-a-pilot-study-in-healthy-subjects
#16
Luis Estrada, Abel Torres-Cebrian, Leonardo Sarlabous, Raimon Jane
This study evaluates the onset and offset of neural inspiratory time estimated from surface diaphragm electromyographic (EMGdi) recordings. EMGdi and airflow signals were recorded in ten healthy subjects according to two respiratory protocols based on respiratory rate (RR) increments, from 15 to 40 breaths per minute (bpm), and fractional inspiratory time (Ti/Ttot) decrements, from 0.54 to 0.18. The analysis of diaphragm electromyographic (EMGdi) signal amplitude is an alternative approach for the quantification of neural respiratory drive (NRD)...
February 22, 2017: IEEE Journal of Biomedical and Health Informatics
https://www.readbyqxmd.com/read/28237935/biopad-leveraging-off-the-shelf-video-games-for-stress-self-regulation
#17
Zelun Wang, Avinash Parnandi, Ricardo Gutierrez-Osuna
This paper presents an approach to use commercial videogames for biofeedback training. It consists of intercepting signals from the game controller and adapting them in real-time based on physiological measurements from the player. We present three sample implementations and a case study for teaching stress self-regulation via an immersive car racing game. We use a crossover gaming device to manipulate controller signals, and a respiratory sensor to monitor the players' breathing rate. We then alter the speed of the car to encourage slow, deep breathing, in this way allowing players to reduce their arousal while playing the game...
February 20, 2017: IEEE Journal of Biomedical and Health Informatics
https://www.readbyqxmd.com/read/28222005/a-modular-low-complexity-ecg-delineation-algorithm-for-real-time-embedded-systems
#18
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
https://www.readbyqxmd.com/read/28222004/hyclasss-a-hybrid-classifier-for-automatic-sleep-stage-scoring
#19
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
https://www.readbyqxmd.com/read/28212103/simulation-of-healing-threshold-in-strain-induced-inflammation-through-a-discrete-informatics-model
#20
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
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