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https://www.readbyqxmd.com/read/28529762/enhanced-inter-subject-brain-computer-interface-with-associative-sensorimotor-oscillations
#1
Simanto Saha, Khawza I Ahmed, Raqibul Mostafa, Ahsan H Khandoker, Leontios Hadjileontiadis
Electroencephalography (EEG) captures electrophysiological signatures of cortical events from the scalp with high-dimensional electrode montages. Usually, excessive sources produce outliers and potentially affect the actual event related sources. Besides, EEG manifests inherent inter-subject variability of the brain dynamics, at the resting state and/or under the performance of task(s), caused probably due to the instantaneous fluctuation of psychophysiological states. A wavelet coherence (WC) analysis for optimally selecting associative inter-subject channels is proposed here and is being used to boost performances of motor imagery (MI)-based inter-subject brain computer interface (BCI)...
February 2017: Healthcare Technology Letters
https://www.readbyqxmd.com/read/28529759/high-frequency-based-features-for-low-and-high-retina-haemorrhage-classification
#2
Salim Lahmiri
Haemorrhages (HAs) presence in fundus images is one of the most important indicators of diabetic retinopathy that causes blindness. In this regard, accurate grading of HAs in fundus images is crucial for appropriate medical treatment. The purpose of this Letter is to assess the relative performance of statistical features obtained with three different multi-resolution analysis (MRA) techniques and fed to support vector machine in grading retinal HAs. Considered MRA techniques are the common discrete wavelet transform (DWT), empirical mode decomposition (EMD), and variational mode decomposition (VMD)...
February 2017: Healthcare Technology Letters
https://www.readbyqxmd.com/read/28523350/comparison-of-a-radiomic-biomarker-with-volumetric-analysis-for-decoding-tumour-phenotypes-of-lung-adenocarcinoma-with-different-disease-specific-survival
#3
Mei Yuan, Yu-Dong Zhang, Xue-Hui Pu, Yan Zhong, Hai Li, Jiang-Fen Wu, Tong-Fu Yu
OBJECTIVES: To compare a multi-feature-based radiomic biomarker with volumetric analysis in discriminating lung adenocarcinomas with different disease-specific survival on computed tomography (CT) scans. METHODS: This retrospective study obtained institutional review board approval and was Health Insurance Portability and Accountability Act (HIPAA) compliant. Pathologically confirmed lung adenocarcinoma (n = 431) manifested as subsolid nodules on CT were identified...
May 18, 2017: European Radiology
https://www.readbyqxmd.com/read/28511108/wine-yeasts-identification-by-maldi-tof-ms-optimization-of-the-preanalytical-steps-and-development-of-an-extensible-open-source-platform-for-processing-and-analysis-of-an-in-house-ms-database
#4
Cristina Gutiérrez, M Ángeles Gómez-Flechoso, Ignacio Belda, Javier Ruiz, Nour Kayali, Luis Polo, Antonio Santos
Saccharomyces cerevisiae is the most important yeast species for the production of wine and other beverages. In addition, nowadays, researchers and winemakers are aware of the influence of non-Saccharomyces in wine aroma complexity. Due to the high microbial diversity associated to several agro-food processes, such as winemaking, developing fast and accurate methods for microbial identification is demanded. In this context, MALDI-TOF MS mass fingerprint provides reliable tool for fast biotyping and classification of microorganisms...
May 8, 2017: International Journal of Food Microbiology
https://www.readbyqxmd.com/read/28508259/investigation-of-the-scaling-characteristics-of-landsat-temperature-and-vegetation-data-a-wavelet-based-approach
#5
Maheswaran Rathinasamy, V M Bindhu, Jan Adamowski, Balaji Narasimhan, Rakesh Khosa
An investigation of the scaling characteristics of vegetation and temperature data derived from LANDSAT data was undertaken for a heterogeneous area in Tamil Nadu, India. A wavelet-based multiresolution technique decomposed the data into large-scale mean vegetation and temperature fields and fluctuations in horizontal, diagonal, and vertical directions at hierarchical spatial resolutions. In this approach, the wavelet coefficients were used to investigate whether the normalized difference vegetation index (NDVI) and land surface temperature (LST) fields exhibited self-similar scaling behaviour...
May 16, 2017: International Journal of Biometeorology
https://www.readbyqxmd.com/read/28499157/identify-temporal-trend-of-air-temperature-and-its-impact-on-forest-stream-flow-in-lower-mississippi-river-alluvial-valley-using-wavelet-analysis
#6
Ying Ouyang, Prem B Parajuli, Yide Li, Theodor D Leininger, Gary Feng
Characterization of stream flow is essential to water resource management, water supply planning, environmental protection, and ecological restoration; while air temperature variation due to climate change can exacerbate stream flow and add instability to the flow. In this study, the wavelet analysis technique was employed to identify temporal trend of air temperature and its impact upon forest stream flows in Lower Mississippi River Alluvial Valley (LMRAV). Four surface water monitoring stations, which locate near the headwater areas with very few land use disturbances and the long-term data records (60-90 years) in the LMRAV, were selected to obtain stream discharge and air temperature data...
May 9, 2017: Journal of Environmental Management
https://www.readbyqxmd.com/read/28499122/emd-dwt-based-transform-domain-feature-reduction-approach-for-quantitative-multi-class-classification-of-breast-lesions
#7
Sharmin R Ara, Syed Khairul Bashar, Farzana Alam, Md Kamrul Hasan
Using a large set of ultrasound features does not necessarily ensure improved quantitative classification of breast tumors; rather, it often degrades the performance of a classifier. In this paper, we propose an effective feature reduction approach in the transform domain for improved multi-class classification of breast tumors. Feature transformation methods, such as empirical mode decomposition (EMD) and discrete wavelet transform (DWT), followed by a filter- or wrapper-based subset selection scheme are used to extract a set of non-redundant and more potential transform domain features through decorrelation of an optimally ordered sequence of N ultrasonic bi-modal (i...
April 24, 2017: Ultrasonics
https://www.readbyqxmd.com/read/28494371/improving-surface-emg-burst-detection-in-infrahyoid-muscles-during-swallowing-using-digital-filters-and-discrete-wavelet-analysis
#8
Sebastian Restrepo-Agudelo, Sebastian Roldan-Vasco, Lina Ramirez-Arbelaez, Santiago Cadavid-Arboleda, Estefania Perez-Giraldo, Andres Orozco-Duque
The visual inspection is a widely used method for evaluating the surface electromyographic signal (sEMG) during deglutition, a process highly dependent of the examiners expertise. It is desirable to have a less subjective and automated technique to improve the onset detection in swallowing related muscles, which have a low signal-to-noise ratio. In this work, we acquired sEMG measured in infrahyoid muscles with high baseline noise of ten healthy adults during water swallowing tasks. Two methods were applied to find the combination of cutoff frequencies that achieve the most accurate onset detection: discrete wavelet decomposition based method and fixed steps variations of low and high cutoff frequencies of a digital bandpass filter...
May 3, 2017: Journal of Electromyography and Kinesiology
https://www.readbyqxmd.com/read/28489019/heart-sound-classification-from-unsegmented-phonocardiograms
#9
Philip Langley, Alan Murray
Objective Most algorithms for automated analysis of phonocardiograms (PCG) require segmentation of the signal into the characteristic heart sounds. The aim was to assess the feasibility for accurate classification of heart sounds on short, unsegmented recordings. Approach PCG segments of 5 second duration from the PhysioNet/Computing in Cardiology Challenge database were analysed. Initially the 5 second segment at the start of each recording (seg 1) was analysed. Segments were zero-mean but otherwise had no pre-processing or segmentation...
May 10, 2017: Physiological Measurement
https://www.readbyqxmd.com/read/28484720/eeg-based-computer-aided-diagnosis-of-autism-spectrum-disorder-using-wavelet-entropy-and-ann
#10
Ridha Djemal, Khalil AlSharabi, Sutrisno Ibrahim, Abdullah Alsuwailem
Autism spectrum disorder (ASD) is a type of neurodevelopmental disorder with core impairments in the social relationships, communication, imagination, or flexibility of thought and restricted repertoire of activity and interest. In this work, a new computer aided diagnosis (CAD) of autism ‎based on electroencephalography (EEG) signal analysis is investigated. The proposed method is based on discrete wavelet transform (DWT), entropy (En), and artificial neural network (ANN). DWT is used to decompose EEG signals into approximation and details coefficients to obtain EEG subbands...
2017: BioMed Research International
https://www.readbyqxmd.com/read/28484325/element-analysis-a-wavelet-based-method-for-analysing-time-localized-events-in-noisy-time-series
#11
Jonathan M Lilly
A method is derived for the quantitative analysis of signals that are composed of superpositions of isolated, time-localized 'events'. Here, these events are taken to be well represented as rescaled and phase-rotated versions of generalized Morse wavelets, a broad family of continuous analytic functions. Analysing a signal composed of replicates of such a function using another Morse wavelet allows one to directly estimate the properties of events from the values of the wavelet transform at its own maxima. The distribution of events in general power-law noise is determined in order to establish significance based on an expected false detection rate...
April 2017: Proceedings. Mathematical, Physical, and Engineering Sciences
https://www.readbyqxmd.com/read/28463198/accelerating-over-relaxed-and-monotone-fast-iterative-shrinkage-thresholding-algorithms-with-line-search-for-sparse-reconstructions
#12
Marcelo V Zibetti, Elias Helou, Daniel Pipa
Recently, especially-crafted unidimensional optimization has been successfully used as line search to accelerate the over-relaxed and monotone fast iterative shrinkage-threshold algorithm (OMFISTA) for computed tomography. In this paper, we extend the use of fast line search to the monotone fast iterative shrinkage-threshold algorithm (MFISTA) and some of its variants. Line search can accelerate the FISTA family considering typical synthesis priors, such as the `1-norm of wavelet coefficients, as well as analysis priors, such as anisotropic total variation...
April 28, 2017: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
https://www.readbyqxmd.com/read/28441634/spatial-enhancement-of-ecg-using-diagnostic-similarity-score-based-lead-selective-multi-scale-linear-model
#13
Jiss J Nallikuzhy, S Dandapat
In this work, a new patient-specific approach to enhance the spatial resolution of ECG is proposed and evaluated. The proposed model transforms a three-lead ECG into a standard twelve-lead ECG thereby enhancing its spatial resolution. The three leads used for prediction are obtained from the standard twelve-lead ECG. The proposed model takes advantage of the improved inter-lead correlation in wavelet domain. Since the model is patient-specific, it also selects the optimal predictor leads for a given patient using a lead selection algorithm...
April 11, 2017: Computers in Biology and Medicine
https://www.readbyqxmd.com/read/28440270/refined-genetic-maps-reveal-sexual-dimorphism-in-human-meiotic-recombination-at-multiple-scales
#14
Claude Bhérer, Christopher L Campbell, Adam Auton
In humans, males have lower recombination rates than females over the majority of the genome, but the opposite is usually true near the telomeres. These broad-scale differences have been known for decades, yet little is known about differences at the fine scale. By combining data sets, we have collected recombination events from over 100,000 meioses and have constructed sex-specific genetic maps at a previously unachievable resolution. Here we show that, although a substantial fraction of the genome shows some degree of sexually dimorphic recombination, the vast majority of hotspots are shared between the sexes, with only a small number of putative sex-specific hotspots...
April 25, 2017: Nature Communications
https://www.readbyqxmd.com/read/28436399/identification-of-patients-with-preeclampsia-from-normal-subjects-using-wavelet-based-spectral-analysis-of-heart-rate-variability
#15
A Hossen, A Barhoum, D Jaju, V Gowri, K Al-Hashmi, M O Hassan, L Al-Kharusi
BACKGROUND: The spectral analysis of the heart rate variability (HRV) shows a decrease in the power of the high frequency (HF) component in preeclamptic pregnancy compared with normal pregnancy; such a decrease is associated with an increase in the low frequency (LF) and the very low frequency (VLF) power. The physiological interpretation is that preeclamptic pregnancy is associated with a facilitation of sympathetic regulation and an attenuation of parasympathetic influence of HR compared with non-pregnancy and normal pregnancy...
April 14, 2017: Technology and Health Care: Official Journal of the European Society for Engineering and Medicine
https://www.readbyqxmd.com/read/28432822/focal-liver-lesions-segmentation-and-classification-in-nonenhanced-t2-weighted-mri
#16
Ilias Gatos, Stavros Tsantis, Maria Karamesini, Stavros Spiliopoulos, Dimitris Karnabatidis, John D Hazle, George C Kagadis
PURPOSE: To automatically segment and classify focal liver lesions (FLLs) on nonenhanced T2-weighted magnetic resonance imaging (MRI) scans using a computer-aided diagnosis (CAD) algorithm. METHODS: 71 FLLs (30 benign lesions, 19 hepatocellular carcinomas, and 22 metastases) on T2-weighted MRI scans were delineated by the proposed CAD scheme. The FLL segmentation procedure involved wavelet multiscale analysis to extract accurate edge information and mean intensity values for consecutive edges computed using horizontal and vertical analysis that were fed into the subsequent fuzzy C-means algorithm for final FLL border extraction...
April 22, 2017: Medical Physics
https://www.readbyqxmd.com/read/28432621/comparison-of-air-pollution-in-shanghai-and-lanzhou-based-on-wavelet-transform
#17
Yana Su, Yongzhong Sha, Guangyu Zhai, Shengliang Zong, Jiehua Jia
For a long-period comparative analysis of air pollution in coastal and inland cities, we analyzed the continuous Morlet wavelet transform on the time series of a 5274-day air pollution index in Shanghai and Lanzhou during 15 years and studied the multi-scale variation characteristic, main cycle, and impact factor of the air pollution time series. The analysis showed that (1) air pollution in the two cities was non-stationary and nonlinear, had multiple timescales, and exhibited the characteristics of high in winter and spring and low in summer and autumn...
April 21, 2017: Environmental Science and Pollution Research International
https://www.readbyqxmd.com/read/28429754/real-time-detection-of-tsunami-ionospheric-disturbances-with-a-stand-alone-gnss-receiver-a-preliminary-feasibility-demonstration
#18
Giorgio Savastano, Attila Komjathy, Olga Verkhoglyadova, Augusto Mazzoni, Mattia Crespi, Yong Wei, Anthony J Mannucci
It is well known that tsunamis can produce gravity waves that propagate up to the ionosphere generating disturbed electron densities in the E and F regions. These ionospheric disturbances can be studied in detail using ionospheric total electron content (TEC) measurements collected by continuously operating ground-based receivers from the Global Navigation Satellite Systems (GNSS). Here, we present results using a new approach, named VARION (Variometric Approach for Real-Time Ionosphere Observation), and estimate slant TEC (sTEC) variations in a real-time scenario...
April 21, 2017: Scientific Reports
https://www.readbyqxmd.com/read/28428848/modeling-activity-patterns-of-wildlife-using-time-series-analysis
#19
Jindong Zhang, Vanessa Hull, Zhiyun Ouyang, Liang He, Thomas Connor, Hongbo Yang, Jinyan Huang, Shiqiang Zhou, Zejun Zhang, Caiquan Zhou, Hemin Zhang, Jianguo Liu
The study of wildlife activity patterns is an effective approach to understanding fundamental ecological and evolutionary processes. However, traditional statistical approaches used to conduct quantitative analysis have thus far had limited success in revealing underlying mechanisms driving activity patterns. Here, we combine wavelet analysis, a type of frequency-based time-series analysis, with high-resolution activity data from accelerometers embedded in GPS collars to explore the effects of internal states (e...
April 2017: Ecology and Evolution
https://www.readbyqxmd.com/read/28426817/evaluation-of-machine-learning-algorithms-and-structural-features-for-optimal-mri-based-diagnostic-prediction-in-psychosis
#20
Raymond Salvador, Joaquim Radua, Erick J Canales-Rodríguez, Aleix Solanes, Salvador Sarró, José M Goikolea, Alicia Valiente, Gemma C Monté, María Del Carmen Natividad, Amalia Guerrero-Pedraza, Noemí Moro, Paloma Fernández-Corcuera, Benedikt L Amann, Teresa Maristany, Eduard Vieta, Peter J McKenna, Edith Pomarol-Clotet
A relatively large number of studies have investigated the power of structural magnetic resonance imaging (sMRI) data to discriminate patients with schizophrenia from healthy controls. However, very few of them have also included patients with bipolar disorder, allowing the clinically relevant discrimination between both psychotic diagnostics. To assess the efficacy of sMRI data for diagnostic prediction in psychosis we objectively evaluated the discriminative power of a wide range of commonly used machine learning algorithms (ridge, lasso, elastic net and L0 norm regularized logistic regressions, a support vector classifier, regularized discriminant analysis, random forests and a Gaussian process classifier) on main sMRI features including grey and white matter voxel-based morphometry (VBM), vertex-based cortical thickness and volume, region of interest volumetric measures and wavelet-based morphometry (WBM) maps...
2017: PloS One
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