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IEEE Journal of Biomedical and Health Informatics

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https://www.readbyqxmd.com/read/27925598/evaluation-of-adherence-to-nutritional-intervention-through-trajectory-analysis
#1
Beatriz Sevilla-Villanueva, Karina Gibert, Miquel Sanchez-Marre, Montserrat Fito, Maria-Isabel Covas
Classical Pre-Post Intervention Studies are often analyzed using traditional statistics. Nevertheless, the nutritional interventions have small effects on the metabolism and traditional statistics are not enough to detect these subtle nutrient effects. Generally, this kind of studies assumes that the participants are adhered to the assigned dietary intervention and directly analyzes its effects over the target parameters. Thus, the evaluation of adherence is generally omitted. Although, sometimes, participants do not effectively adhere to the assigned dietary guidelines...
December 1, 2016: IEEE Journal of Biomedical and Health Informatics
https://www.readbyqxmd.com/read/27925597/tackling-missing-data-in-community-health-studies-using-additive-ls-svm-classifier
#2
Guanjin Wang, Zhaohong Deng, Kup-Sze Choi
Missing data is a common issue in community health and epidemiological studies. Direct removal of samples with missing data can lead to reduced sample size and information bias, which deteriorates the significance of the results. While data imputation methods are available to deal with missing data, they are limited in performance and could introduce noises into the dataset. Instead of data imputation, a novel method based on additive least square support vector machine (LS-SVM) is proposed in this paper for predictive modeling when the input features of the model contain missing data...
December 1, 2016: IEEE Journal of Biomedical and Health Informatics
https://www.readbyqxmd.com/read/27913367/a-new-method-for-self-estimation-of-the-severity-of-obstructive-sleep-apnea-using-easily-available-measurements-and-neural-fuzzy-evaluation-system
#3
Ming-Feng Wu, Wei-Chang Huang, Chia-Feng Juang, Kai-Ming Chang, Chih-Yu Wen, Yu-Hsuan Chen, Ching-Yi Lin, Yi-Chan Chen, Ching-Cheng Lin
This paper proposes a neural fuzzy evaluation system (NFES) with significant variables selected from stepwise regression to predict apnea-hypopnea index (AHI) for evaluating obstructive sleep apnea (OSA). The variables considered are the change statuses of blood pressure (BP) before going to sleep and early in the morning as well as other five easily available measurements (age, body mass index (BMI) etc.) so that users can use the system for self-evaluation of OSA. A total of 150 subjects are reviewed retrospectively and categorized as training (120 subjects) and validation (30 subjects) sets by a five-fold cross validation scheme with stratified sampling based on the OSA severity...
December 1, 2016: IEEE Journal of Biomedical and Health Informatics
https://www.readbyqxmd.com/read/27913366/deepr-a-convolutional-net-for-medical-records
#4
Phuoc Nguyen, Truyen Tran, Nilmini Wickramasinghe, Svetha Venkatesh
Feature engineering remains a major bottleneck when creating predictive systems from electronic medical records. At present, an important missing element is detecting predictive regular clinical motifs from irregular episodic records. We present Deepr (short for Deep record), a new end-to-end deep learning system that learns to extract features from medical records and predicts future risk automatically. Deepr transforms a record into a sequence of discrete elements separated by coded time gaps and hospital transfers...
December 1, 2016: IEEE Journal of Biomedical and Health Informatics
https://www.readbyqxmd.com/read/27913365/portable-mtbi-assessment-using-temporal-and-frequency-analysis-of-speech
#5
Louis Daudet, Nikhil Yadav, Matthew Perez, Christian Poellabauer, Sandra Schneider, Alan Huebner
This paper shows that extraction and analysis of various acoustic features from speech using mobile devices can allow the detection of patterns that could be indicative of neurological trauma. This may pave the way for new types of biomarkers and diagnostic tools. Toward this end, we created a mobile application designed to diagnose mild traumatic brain injuries (mTBI) such as concussions. Using this application, data was collected from youth athletes from 47 high schools and colleges in the the Midwestern United States...
December 1, 2016: IEEE Journal of Biomedical and Health Informatics
https://www.readbyqxmd.com/read/27913364/an-intelligible-risk-stratification-model-based-on-pairwise-and-size-constrained-kmeans
#6
Longfei Han, Senlin Luo, Huaiqing Wang, Limin Pan, Xincheng Ma, Tiemei Zhang
Having a system to stratify individuals according to risk is key to clinical disease prevention, this allows individuals identified at different risk tiers benefit from further investigation and intervention. But the same risk score estimated for two different persons doesn't mean they need the same further investigation or represent the similarity health condition between two persons. Meanwhile, users still does not know a prior what most of the risk tiers are, and how many tiers should be found in risk stratification...
November 29, 2016: IEEE Journal of Biomedical and Health Informatics
https://www.readbyqxmd.com/read/27913363/detecting-breathing-and-snoring-episodes-using-a-wireless-tracheal-sensor-a-feasibility-study
#7
Marcel Mlynczak, Ewa Migacz, Maciej Migacz, Wojciech Kukwa
OBJECTIVE: Sleep-disordered breathing is both a clinical and a social problem. This implies the need for convenient solutions to simplify screening and diagnosis. The aim of the study was to investigate the sensitivity and specificity of a novel wireless system in detecting breathing and snoring episodes during sleep. METHODS: A wireless acoustic sensor was elaborated and implemented. Segmentation (based on spectral thresholding and heuristics) and classification of all breathing episodes during recording were implemented through a mobile application...
November 29, 2016: IEEE Journal of Biomedical and Health Informatics
https://www.readbyqxmd.com/read/27898386/mixture-model-clustering-of-pathological-gait-patterns
#8
Elham Dolatabadi, Avril Mansfield, Kara Patterson, Babak Taati, Alex Mihailidis
This study applies mixture-model clustering to spatiotemporal gait parameters in order to characterize the pathological gait pattern and to generate a composite measure indicative of overall gait performance. Gait data from sixty-eight adults with stroke (age: 61.513.6 years) and twenty healthy adults (age: 28.87.1 years) were used in this study. Participants performed 3 passes across a GAITRite mat at different time points following stroke (poststroke adults only). Mixture-model clustering grouped participants' gait patterns based on their spatiotemporal gait features including symmetry, speed, and variability...
November 24, 2016: IEEE Journal of Biomedical and Health Informatics
https://www.readbyqxmd.com/read/27898385/measuring-the-consumption-of-individual-solid-and-liquid-bites-using-a-table-embedded-scale-during-unrestricted-eating
#9
Ryan S Mattfeld, Eric R Muth, Adam Hoover
The universal eating monitor (UEM) is a table-embedded scale used to measure grams consumed over time while a person eats. It has been used in laboratory settings to test the effects of anorectic drugs and behavior manipulations such as slowing eating, and to study relationships between demographics and body weight. However, its use requires restricted conditions on the foods consumed and behaviors allowed during eating in order to simplify analysis of the scale data. Individual bites can only be measured when the only interaction with the scale is to carefully remove a single bite of food, consume it fully, and wait a minimum amount of time before the next bite...
November 24, 2016: IEEE Journal of Biomedical and Health Informatics
https://www.readbyqxmd.com/read/27893403/real-time-robust-heart-rate-estimation-from-wrist-type-ppg-signals-using-multiple-reference-adaptive-noise-cancellation
#10
Sayeed Chowdhury, Rakib Hyder, Md Samzid Hafiz, Mohammad Ariful Haque
No abstract text is available yet for this article.
November 23, 2016: IEEE Journal of Biomedical and Health Informatics
https://www.readbyqxmd.com/read/27893402/a-convolutional-neural-network-for-automatic-characterization-of-plaque-composition-in-carotid-ultrasound
#11
Karim Lekadir, Alfiia Galimzianova, Angels Betriu, Maria Del Mar Vila, Laura Igual, Daniel Rubin, Elvira Fernandez, Petia Radeva, Sandy Napel
Characterization of carotid plaque composition, more specifically the amount of lipid core, fibrous tissue, and calcified tissue, is an important task for the identification of plaques that are prone to rupture, and thus for early risk estima-tion of cardiovascular and cerebrovascular events. Due to its low costs and wide availability, carotid ultrasound has the potential to become the modality of choice for plaque characterization in clinical practice. However, its significant image noise, coupled with the small size of the plaques and their complex appearance, makes it difficult for automated techniques to discriminate be-tween the different plaque constituents...
November 22, 2016: IEEE Journal of Biomedical and Health Informatics
https://www.readbyqxmd.com/read/27893401/heartbeat-classification-using-abstract-features-from-the-abductive-interpretation-of-the-ecg
#12
Tomas Teijeiro, Paulo Felix, Jesus Presedo, Daniel Castro
: This paper aims to prove that automatic beat classification on ECG signals can be effectively solved with a pure knowledge-based approach, using an appropriate set of abstract features obtained from the interpretation of the physiological processes underlying the signal. METHODS: A set of qualitative morphological and rhythm features are obtained for each heartbeat as a result of the abductive interpretation of the ECG. Then, a QRS clustering algorithm is applied in order to reduce the effect of possible errors in the interpretation...
November 21, 2016: IEEE Journal of Biomedical and Health Informatics
https://www.readbyqxmd.com/read/27849550/generalized-feature-extraction-for-wrist-pulse-analysis-from-1-d-time-series-to-2-d-matrix
#13
Dimin Wang, David Zhang, Guangming Lu
Traditional Chinese Pulse Diagnosis (TCPD), known as an empirical science, depends on the subjective experience. Inconsistent diagnostic results may be obtained among different practitioners. A scientific way of studying the pulse should be to analyze the objectified wrist pulse waveforms. In recent years, many pulse acquisition platforms have been developed with the advances in sensor and computer technology. And the pulse diagnosis using pattern recognition theories is also increasingly attracting attentions...
November 11, 2016: IEEE Journal of Biomedical and Health Informatics
https://www.readbyqxmd.com/read/27845680/emg-torque-relation-in-chronic-stroke-a-novel-emg-complexity-representation-with-a-linear-electrode-array
#14
Xu Zhang, Dongqing Wang, Zaiyang Yu, Xiang Chen, Sheng Li, Ping Zhou
This study examines the electromyogram (EMG) - torque relation for chronic stroke survivors using a novel EMG complexity representation. Ten stroke subjects performed a series of submaximal isometric elbow flexion tasks using their affected and contralateral arms, respectively, while a 20-channel linear electrode array was used to record surface EMG from the biceps brachii muscles. The sample entropy (SampEn) of surface EMG signals was calculated with both global and local tolerance schemes. A regression analysis was performed between SampEn of each channel's surface EMG and elbow flexion torque...
November 8, 2016: IEEE Journal of Biomedical and Health Informatics
https://www.readbyqxmd.com/read/27834661/an-extended-bayesian-framework-for-atrial-and-ventricular-activity-separation-in-atrial-fibrillation
#15
Ebadollah Kheirati Roonizi, Roberto Sassi
An extended nonlinear Bayesian filtering framework is introduced for the analysis of Atrial Fibrillation (AF), in particular with single channel electrocardiographical (ECG) recordings. It is suitable for simultaneously tracking the fundamental frequency of atrial fibrillatory waves (f-waves), and separating signals, linked to atrial and ventricular activity, during AF. In this framework, high-power ECG components, i.e. Q, R, S and T waves, are modeled using sum of Gaussian functions. The atrial activity dynamical model is instead based on a trigonometrical function, with a fundamental frequency (the inverse of the dominant atrial cycle length), and its harmonics...
November 4, 2016: IEEE Journal of Biomedical and Health Informatics
https://www.readbyqxmd.com/read/27834660/private-and-efficient-query-processing-on-outsourced-genomic-databases
#16
Reza Ghasemi, Md Momin Al Aziz, Noman Mohammed, Massoud Hadian Dehkordi, Xiaoqian Jiang
Applications of genomic studies are spreading rapidly in many domains of science and technology such as healthcare, biomedical research, direct-to-consumer services, and legal and forensic. However, there are a number of obstacles that make it hard to access and process a big genomic database for these applications. First, sequencing genomic sequence is a timeconsuming and expensive process. Second, it requires large-scale computation and storage systems to processes genomic sequences. Third, genomic databases are often owned by different organizations and thus not available for public usage...
November 4, 2016: IEEE Journal of Biomedical and Health Informatics
https://www.readbyqxmd.com/read/27834659/a-novel-chewing-detection-system-based-on-ppg-audio-and-accelerometry
#17
Vasileios Papapanagiotou, Christos Diou, Lingchuan Zhou, Janet van den Boer, Monica Mars, Anastasios Delopoulos
In the context of dietary management, accurate monitoring of eating habits is receiving increased attention. Wearable sensors, combined with the connectivity and processing of modern smart phones, can be used to robustly extract objective, and real-time measurements of human behaviour. In particular, for the task of chewing detection, several approaches based on an in-ear microphone can be found in the literature, while other types of sensors have also been reported, such as strain sensors. In this work, performed in the context of the SPLENDID project, we propose to combine an in-ear microphone with a photoplethysmography (PPG) sensor placed in the ear concha, in a new high accuracy and low sampling rate prototype chewing detection system...
November 4, 2016: IEEE Journal of Biomedical and Health Informatics
https://www.readbyqxmd.com/read/27834657/segmentation-of-white-blood-cells-image-using-adaptive-location-and-iteration
#18
Yuehua Liu, Feilong Cao, Jianwei Zhao, Jianjun Chu
Segmentation of white blood cells (WBCs) image is meaningful but challenging due to the complex internal characteristics of the cells and external factors, such as illumination and different microscopic views. This paper addresses two problems of the segmentation: WBC location and sub-image segmentation. To locate WBCs, a method that uses multiple windows obtained by scoring multi-scale cues to extract a rectangular region is proposed. In this manner, the location window not only can cover the whole WBC completely, but also achieve adaptive adjustment...
November 4, 2016: IEEE Journal of Biomedical and Health Informatics
https://www.readbyqxmd.com/read/27834656/automated-detection-of-atrial-fibrillation-based-on-time-frequency-analysis-of-seismocardiagrams
#19
Tero Hurnanen, Eero Lehtonen, Mojtaba Jafari Tadi, Tom Kuusela, Tuomas Kiviniemi, Antti Saraste, Tuija Vasankari, Juhani Airaksinen, Tero Koivisto, Mikko Pankaala
In this paper, a novel method to detect atrial fibrillation from a seismocardiogram (SCG) is presented. The proposed method is based on linear classification of the spectral entropy and a heart rate variability index computed from the SCG. The performance of the developed algorithm is demonstrated on data gathered from 13 patients in clinical setting. After motion artefact removal, in total 119 minutes of AFib data and 126 minutes of sinus rhythm data were considered for automated atrial fibrillation detection...
November 4, 2016: IEEE Journal of Biomedical and Health Informatics
https://www.readbyqxmd.com/read/27834658/single-channel-sparse-nonnegative-blind-source-separation-method-for-automatic-3d-delineation-of-lung-tumor-in-pet-images
#20
Ivica Kopriva, Wei Ju, Bin Zhang, Fei Shi, Dehui Xiang, Kai Yu, Ximing Wang, Ulas Bagci, Xinjian Chen
In this paper, we propose a novel method for single-channel blind separation of non-overlapped sources and, to the best of our knowledge, apply it for the first time to automatic segmentation of lung tumors in Positron Emission Tomography (PET) images. Our approach first converts 3D PET image into a pseudo multichannel image. Afterwards, regularization free sparseness constrained nonnegative matrix factorization is used to separate tumor from other tissues. By using complexity based criterion, we select tumor component as the one with minimal complexity...
November 3, 2016: IEEE Journal of Biomedical and Health Informatics
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