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Medical & Biological Engineering & Computing

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https://www.readbyqxmd.com/read/29043535/towards-multilevel-mental-stress-assessment-using-svm-with-ecoc-an-eeg-approach
#1
Fares Al-Shargie, Tong Boon Tang, Nasreen Badruddin, Masashi Kiguchi
Mental stress has been identified as one of the major contributing factors that leads to various diseases such as heart attack, depression, and stroke. To avoid this, stress quantification is important for clinical intervention and disease prevention. This study aims to investigate the feasibility of exploiting electroencephalography (EEG) signals to discriminate between different stress levels. We propose a new assessment protocol whereby the stress level is represented by the complexity of mental arithmetic (MA) task for example, at three levels of difficulty, and the stressors are time pressure and negative feedback...
October 18, 2017: Medical & Biological Engineering & Computing
https://www.readbyqxmd.com/read/29034407/novel-mahalanobis-based-feature-selection-improves-one-class-classification-of-early-hepatocellular-carcinoma
#2
Ricardo de Lima Thomaz, Pedro Cunha Carneiro, João Eliton Bonin, Túlio Augusto Alves Macedo, Ana Claudia Patrocinio, Alcimar Barbosa Soares
Detection of early hepatocellular carcinoma (HCC) is responsible for increasing survival rates in up to 40%. One-class classifiers can be used for modeling early HCC in multidetector computed tomography (MDCT), but demand the specific knowledge pertaining to the set of features that best describes the target class. Although the literature outlines several features for characterizing liver lesions, it is unclear which is most relevant for describing early HCC. In this paper, we introduce an unconstrained GA feature selection algorithm based on a multi-objective Mahalanobis fitness function to improve the classification performance for early HCC...
October 16, 2017: Medical & Biological Engineering & Computing
https://www.readbyqxmd.com/read/29027087/the-effect-of-using-a-dielectric-matching-medium-in-focused-microwave-radiometry-an-anatomically-detailed-head-model-study
#3
Maria Koutsoupidou, Evangelos Groumpas, Irene S Karanasiou, Maria Christopoulou, Konstantina Nikita, Nikolaos Uzunoglu
Microwave radiometry is a passive technique used to measure in-depth temperature distributions inside the human body, potentially useful in clinical applications. Experimental data imply that it may provide the capability of detecting in-depth local variations of temperature and/or conductivity of excitable tissues at microwave frequencies. Specifically, microwave radiometry may allow the real-time monitoring of brain temperature and/or conductivity changes, associated with local brain activation. In this paper, recent results of our ongoing research regarding the capabilities of focused microwave radiometry for brain intracranial applications are presented...
October 13, 2017: Medical & Biological Engineering & Computing
https://www.readbyqxmd.com/read/28948522/methodological-framework-for-heart-rate-variability-analysis-during-exercise-application-to-running-and-cycling-stress-testing
#4
David Hernando, Alberto Hernando, Jose A Casajús, Pablo Laguna, Nuria Garatachea, Raquel Bailón
Standard methodologies of heart rate variability analysis and physiological interpretation as a marker of autonomic nervous system condition have been largely published at rest, but not so much during exercise. A methodological framework for heart rate variability (HRV) analysis during exercise is proposed, which deals with the non-stationary nature of HRV during exercise, includes respiratory information, and identifies and corrects spectral components related to cardiolocomotor coupling (CC). This is applied to 23 male subjects who underwent different tests: maximal and submaximal, running and cycling; where the ECG, respiratory frequency and oxygen consumption were simultaneously recorded...
September 26, 2017: Medical & Biological Engineering & Computing
https://www.readbyqxmd.com/read/28948480/delineation-of-the-ischemic-stroke-lesion-based-on-watershed-and-relative-fuzzy-connectedness-in-brain-mri
#5
Asit Subudhi, Subhranshu Jena, Sukanta Sabut
Precise segmentation of stroke lesions from brain magnetic resonance (MR) images poses a challenging task in automated diagnosis. In this paper, we proposed a new method called watershed-based lesion segmentation algorithm (WLSA), which is a novel intensity-based segmentation technique used to delineate infarct lesion in diffusion-weighted imaging (DWI) MR images of the brain. The algorithm was tested on a series of 142 real-time images collected from different stroke patients reported at IMS and SUM Hospital...
September 26, 2017: Medical & Biological Engineering & Computing
https://www.readbyqxmd.com/read/28933043/quantifying-the-effect-of-uncertainty-in-input-parameters-in-a-simplified-bidomain-model-of-partial-thickness-ischaemia
#6
Barbara M Johnston, Sam Coveney, Eugene T Y Chang, Peter R Johnston, Richard H Clayton
Reduced blood flow in the coronary arteries can lead to damaged heart tissue (myocardial ischaemia). Although one method for detecting myocardial ischaemia involves changes in the ST segment of the electrocardiogram, the relationship between these changes and subendocardial ischaemia is not fully understood. In this study, we modelled ST-segment epicardial potentials in a slab model of cardiac ventricular tissue, with a central ischaemic region, using the bidomain model, which considers conduction longitudinal, transverse and normal to the cardiac fibres...
September 20, 2017: Medical & Biological Engineering & Computing
https://www.readbyqxmd.com/read/28913821/erratum-to-foreword-to-the-special-issue-electroporation-for-biomedical-applications
#7
Günther Zeck, Damijan Miklavčič
No abstract text is available yet for this article.
September 15, 2017: Medical & Biological Engineering & Computing
https://www.readbyqxmd.com/read/28905236/diagnosis-of-coronary-artery-disease-using-an-efficient-hash-table-based-closed-frequent-itemsets-mining
#8
Ramesh Dhanaseelan, M Jeya Sutha
This paper proposes an efficient hash table based closed frequent itemsets (HCFI) mining algorithm to envisage coronary artery disease early. HCFI algorithm generates closed frequent itemsets efficiently by performing intersection operation on transaction id's of itemset without considering the name of item/itemset. The employed hash table reduces search efficiency to O(1) or constant time. HCFI algorithm is applied on the UCI (University of California, Irvine) Cleveland dataset, a biological database of cardiovascular disease to generate closed frequent itemsets on the dataset...
September 14, 2017: Medical & Biological Engineering & Computing
https://www.readbyqxmd.com/read/28900873/numerical-analysis-of-intracochlear-mechanical-auditory-stimulation-using-piezoelectric-bending-actuators
#9
Daniel Schurzig, Sebastian Schwarzendahl, Jörg Wallaschek, Wouter J van Drunen, Thomas S Rau, Thomas Lenarz, Omid Majdani
Cochlear implantation can restore a certain degree of auditory impression of patients suffering from profound hearing loss or deafness. Furthermore, studies have shown that in case of residual hearing, patients benefit from the use of a hearing aid in addition to the cochlear implant. The presented studies aim at the improvement of this electromechanical stimulation (EMS) approach by substituting the external hearing aid by an internal stimulus provided by miniaturized piezoelectric actuators. Finite element analyses are performed in order to derive fundamental guidelines for the actuator layout aiming at maximal mechanical stimuli...
September 13, 2017: Medical & Biological Engineering & Computing
https://www.readbyqxmd.com/read/28891042/a-novel-and-reliable-computational-intelligence-system-for-breast-cancer-detection
#10
Amin Zadeh Shirazi, Seyyed Javad Seyyed Mahdavi Chabok, Zahra Mohammadi
Cancer is the second important morbidity and mortality factor among women and the most incident type is breast cancer. This paper suggests a hybrid computational intelligence model based on unsupervised and supervised learning techniques, i.e., self-organizing map (SOM) and complex-valued neural network (CVNN), for reliable detection of breast cancer. The dataset used in this paper consists of 822 patients with five features (patient's breast mass shape, margin, density, patient's age, and Breast Imaging Reporting and Data System assessment)...
September 11, 2017: Medical & Biological Engineering & Computing
https://www.readbyqxmd.com/read/28891000/clinical-application-of-modified-bag-of-features-coupled-with-hybrid-neural-based-classifier-in-dengue-fever-classification-using-gene-expression-data
#11
Sankhadeep Chatterjee, Nilanjan Dey, Fuqian Shi, Amira S Ashour, Simon James Fong, Soumya Sen
Dengue fever detection and classification have a vital role due to the recent outbreaks of different kinds of dengue fever. Recently, the advancement in the microarray technology can be employed for such classification process. Several studies have established that the gene selection phase takes a significant role in the classifier performance. Subsequently, the current study focused on detecting two different variations, namely, dengue fever (DF) and dengue hemorrhagic fever (DHF). A modified bag-of-features method has been proposed to select the most promising genes in the classification process...
September 11, 2017: Medical & Biological Engineering & Computing
https://www.readbyqxmd.com/read/28864847/a-monocentric-centerline-extraction-method-for-ring-like-blood-vessels
#12
Fengjun Zhao, Feifei Sun, Yuqing Hou, Yanrong Chen, Dongmei Chen, Xin Cao, Huangjian Yi, Bin Wang, Xiaowei He, Jimin Liang
Centerline is generally used to measure topological and morphological parameters of blood vessels, which is pivotal for the quantitative analysis of vascular diseases. However, previous centerline extraction methods have two drawbacks on complex blood vessels, represented as the failure on ring-like structures and the existing of multi-voxel width. In this paper, we propose a monocentric centerline extraction method for ring-like blood vessels, which consists of three components. First, multiple centerlines are generated from the seed points that are chosen by randomly sprinkling points on blood vessel data...
September 2, 2017: Medical & Biological Engineering & Computing
https://www.readbyqxmd.com/read/28864838/an-improved-multi-objective-optimization-based-cica-method-with-data-driver-temporal-reference-for-group-fmri-data-analysis
#13
Yuhu Shi, Weiming Zeng, Xiaoyan Tang, Wei Kong, Jun Yin
Group independent component analysis (GICA) has been successfully applied to study multi-subject functional magnetic resonance imaging (fMRI) data, and the group independent component (GIC) represents the commonality of all subjects in the group. However, some studies show that the performance of GICA can be improved by incorporating a priori information, which is not always considered when looking for GICs in existing GICA methods. In this paper, we propose an improved multi-objective optimization-based constrained independent component analysis (CICA) method to take advantage of the temporal a priori information extracted from all subjects in the group by incorporating it into the computational process of GICA for group fMRI data analysis...
September 2, 2017: Medical & Biological Engineering & Computing
https://www.readbyqxmd.com/read/28849546/a-new-monte-carlo-code-for-light-transport-in-biological-tissue
#14
Eugenio Torres-García, Rigoberto Oros-Pantoja, Liliana Aranda-Lara, Patricia Vieyra-Reyes
The aim of this work was to develop an event-by-event Monte Carlo code for light transport (called MCLTmx) to identify and quantify ballistic, diffuse, and absorbed photons, as well as their interaction coordinates inside the biological tissue. The mean free path length was computed between two interactions for scattering or absorption processes, and if necessary scatter angles were calculated, until the photon disappeared or went out of region of interest. A three-layer array (air-tissue-air) was used, forming a semi-infinite sandwich...
August 29, 2017: Medical & Biological Engineering & Computing
https://www.readbyqxmd.com/read/28849317/knee-cartilage-segmentation-and-thickness-computation-from-ultrasound-images
#15
Amir Faisal, Siew-Cheok Ng, Siew-Li Goh, Khin Wee Lai
Quantitative thickness computation of knee cartilage in ultrasound images requires segmentation of a monotonous hypoechoic band between the soft tissue-cartilage interface and the cartilage-bone interface. Speckle noise and intensity bias captured in the ultrasound images often complicates the segmentation task. This paper presents knee cartilage segmentation using locally statistical level set method (LSLSM) and thickness computation using normal distance. Comparison on several level set methods in the attempt of segmenting the knee cartilage shows that LSLSM yields a more satisfactory result...
August 29, 2017: Medical & Biological Engineering & Computing
https://www.readbyqxmd.com/read/28849304/apnea-and-heart-rate-detection-from-tracheal-body-sounds-for-the-diagnosis-of-sleep-related-breathing-disorders
#16
Christoph Kalkbrenner, Manuel Eichenlaub, Stefan Rüdiger, Cornelia Kropf-Sanchen, Wolfgang Rottbauer, Rainer Brucher
Sleep apnea is one of the most common sleep disorders. Here, patients suffer from multiple breathing pauses longer than 10 s during the night which are referred to as apneas. The standard method for the diagnosis of sleep apnea is the attended cardiorespiratory polysomnography (PSG). However, this method is expensive and the extensive recording equipment can have a significant impact on sleep quality falsifying the results. To overcome these problems, a comfortable and novel system for sleep monitoring based on the recording of tracheal sounds and movement data is developed...
August 29, 2017: Medical & Biological Engineering & Computing
https://www.readbyqxmd.com/read/28840445/mr-image-reconstruction-via-guided-filter
#17
Heyan Huang, Hang Yang, Kang Wang
Magnetic resonance imaging (MRI) reconstruction from the smallest possible set of Fourier samples has been a difficult problem in medical imaging field. In our paper, we present a new approach based on a guided filter for efficient MRI recovery algorithm. The guided filter is an edge-preserving smoothing operator and has better behaviors near edges than the bilateral filter. Our reconstruction method is consist of two steps. First, we propose two cost functions which could be computed efficiently and thus obtain two different images...
August 25, 2017: Medical & Biological Engineering & Computing
https://www.readbyqxmd.com/read/28840490/usability-of-computerized-lung-auscultation-sound-software-class-for-learning-pulmonary-auscultation
#18
Ana Machado, Ana Oliveira, Cristina Jácome, Marco Pereira, José Moreira, João Rodrigues, José Aparício, Luis M T Jesus, Alda Marques
The mastering of pulmonary auscultation requires complex acoustic skills. Computer-assisted learning tools (CALTs) have potential to enhance the learning of these skills; however, few have been developed for this purpose and do not integrate all the required features. Thus, this study aimed to assess the usability of a new CALT for learning pulmonary auscultation. Computerized Lung Auscultation-Sound Software (CLASS) usability was assessed by eight physiotherapy students using computer screen recordings, think-aloud reports, and facial expressions...
August 24, 2017: Medical & Biological Engineering & Computing
https://www.readbyqxmd.com/read/28840461/simultaneous-bold-detection-and-incomplete-fmri-data-reconstruction
#19
Saideh Ferdowsi, Vahid Abolghasemi
The problem of simultaneous blood oxygenation level dependent (BOLD) detection and data completion is addressed in this paper. It is assumed that a set of fMRI data with significant number of missing samples is available and the aim is to recover those samples with least possible quality degradation. At the same time, BOLD should be detected. We propose a new cost function comprising both BOLD detection and data reconstruction terms. A solution based on singular value thresholding and sparsity-inducing approach is proposed...
August 24, 2017: Medical & Biological Engineering & Computing
https://www.readbyqxmd.com/read/28840451/early-prediction-of-cardiac-resynchronization-therapy-response-by-non-invasive-electrocardiogram-markers
#20
Nuria Ortigosa, Víctor Pérez-Roselló, Víctor Donoso, Joaquín Osca, Luis Martínez-Dolz, Carmen Fernández, Antonio Galbis
Cardiac resynchronization therapy (CRT) is an effective treatment for those patients with severe heart failure. Regrettably, there are about one third of CRT "non-responders", i.e. patients who have undergone this form of device therapy but do not respond to it, which adversely affects the utility and cost-effectiveness of CRT. In this paper, we assess the ability of a novel surface ECG marker to predict CRT response. We performed a retrospective exploratory study of the ECG previous to CRT implantation in 43 consecutive patients with ischemic (17) or non-ischemic (26) cardiomyopathy...
August 24, 2017: Medical & Biological Engineering & Computing
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