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https://www.readbyqxmd.com/read/28717523/assessment-of-emotional-expressions-after-full-face-transplantation
#1
Çağdaş Topçu, Hilmi Uysal, Ömer Özkan, Özlenen Özkan, Övünç Polat, Merve Bedeloğlu, Arzu Akgül, Ela Naz Döğer, Refik Sever, Nur Ebru Barçın, Kadriye Tombak, Ömer Halil Çolak
We assessed clinical features as well as sensory and motor recoveries in 3 full-face transplantation patients. A frequency analysis was performed on facial surface electromyography data collected during 6 basic emotional expressions and 4 primary facial movements. Motor progress was assessed using the wavelet packet method by comparison against the mean results obtained from 10 healthy subjects. Analyses were conducted on 1 patient at approximately 1 year after face transplantation and at 2 years after transplantation in the remaining 2 patients...
2017: Neural Plasticity
https://www.readbyqxmd.com/read/28713811/reducing-the-impact-of-shoulder-abduction-loading-on-the-classification-of-hand-opening-and-grasping-in-individuals-with-poststroke-flexion-synergy
#2
Yiyun Lan, Jun Yao, Julius P A Dewald
Application of neural machine interface in individuals with chronic hemiparetic stroke is regarded as a great challenge, especially for classification of the hand opening and grasping during a functional upper extremity movement such as reach-to-grasp. The overall accuracy of classifying hand movements, while actively lifting the paretic arm, is subject to a significant reduction compared to the accuracy when the arm is fully supported. Such a reduction is believed to be due to the expression of flexion synergy, which couples shoulder abduction (SABD) with elbow/wrist and finger flexion, and is common in up to 60% of the stroke population...
2017: Frontiers in Bioengineering and Biotechnology
https://www.readbyqxmd.com/read/28711988/emotion-recognition-based-on-eeg-features-in-movie-clips-with-channel-selection
#3
Mehmet Siraç Özerdem, Hasan Polat
Emotion plays an important role in human interaction. People can explain their emotions in terms of word, voice intonation, facial expression, and body language. However, brain-computer interface (BCI) systems have not reached the desired level to interpret emotions. Automatic emotion recognition based on BCI systems has been a topic of great research in the last few decades. Electroencephalogram (EEG) signals are one of the most crucial resources for these systems. The main advantage of using EEG signals is that it reflects real emotion and can easily be processed by computer systems...
July 15, 2017: Brain Informatics
https://www.readbyqxmd.com/read/28708865/a-deep-learning-framework-for-financial-time-series-using-stacked-autoencoders-and-long-short-term-memory
#4
Wei Bao, Jun Yue, Yulei Rao
The application of deep learning approaches to finance has received a great deal of attention from both investors and researchers. This study presents a novel deep learning framework where wavelet transforms (WT), stacked autoencoders (SAEs) and long-short term memory (LSTM) are combined for stock price forecasting. The SAEs for hierarchically extracted deep features is introduced into stock price forecasting for the first time. The deep learning framework comprises three stages. First, the stock price time series is decomposed by WT to eliminate noise...
2017: PloS One
https://www.readbyqxmd.com/read/28706728/effects-of-ageing-and-alzheimer-disease-on-haemodynamic-response-function-a-challenge-for-event-related-fmri
#5
Davud Asemani, Hassan Morsheddost, Mahsa Alizadeh Shalchy
Functional magnetic resonance imaging (fMRI) can generate brain images that show neuronal activity due to sensory, cognitive or motor tasks. Haemodynamic response function (HRF) may be considered as a biomarker to discriminate the Alzheimer disease (AD) from healthy ageing. As blood-oxygenation-level-dependent fMRI signal is much weak and noisy, particularly for the elderly subjects, a robust method is necessary for HRF estimation to efficiently differentiate the AD. After applying minimum description length wavelet as an extra denoising step, deconvolution algorithm is here employed for HRF estimation, substituting the averaging method used in the previous works...
June 2017: Healthcare Technology Letters
https://www.readbyqxmd.com/read/28706561/automatic-detection-of-epilepsy-and-seizure-using-multiclass-sparse-extreme-learning-machine-classification
#6
Yuanfa Wang, Zunchao Li, Lichen Feng, Chuang Zheng, Wenhao Zhang
An automatic detection system for distinguishing normal, ictal, and interictal electroencephalogram (EEG) signals is of great help in clinical practice. This paper presents a three-class classification system based on discrete wavelet transform (DWT) and the nonlinear sparse extreme learning machine (SELM) for epilepsy and epileptic seizure detection. Three-level lifting DWT using Daubechies order 4 wavelet is introduced to decompose EEG signals into delta, theta, alpha, and beta subbands. Considering classification accuracy and computational complexity, the maximum and standard deviation values of each subband are computed to create an eight-dimensional feature vector...
2017: Computational and Mathematical Methods in Medicine
https://www.readbyqxmd.com/read/28705145/texture-based-classification-of-different-single-liver-lesion-based-on-spair-t2w-mri-images
#7
Zhenjiang Li, Yu Mao, Wei Huang, Hongsheng Li, Jian Zhu, Wanhu Li, Baosheng Li
BACKGROUND: To assess the feasibility of texture analysis (TA) based on spectral attenuated inversion-recovery T2 weighted magnetic resonance imaging (SPAIR T2W-MRI) for the classification of hepatic hemangioma (HH), hepatic metastases (HM) and hepatocellular carcinoma (HCC). METHODS: The SPAIR T2W-MRI data of 162 patients with HH (n=55), HM (n=67) and HCC (n=40) were retrospectively analyzed. We used two independent cohorts for training (n = 112 patients) and validation (n = 50 patients)...
July 13, 2017: BMC Medical Imaging
https://www.readbyqxmd.com/read/28704955/day-ahead-pm2-5-concentration-forecasting-using-wt-vmd-based-decomposition-method-and-back-propagation-neural-network-improved-by-differential-evolution
#8
Deyun Wang, Yanling Liu, Hongyuan Luo, Chenqiang Yue, Sheng Cheng
Accurate PM2.5 concentration forecasting is crucial for protecting public health and atmospheric environment. However, the intermittent and unstable nature of PM2.5 concentration series makes its forecasting become a very difficult task. In order to improve the forecast accuracy of PM2.5 concentration, this paper proposes a hybrid model based on wavelet transform (WT), variational mode decomposition (VMD) and back propagation (BP) neural network optimized by differential evolution (DE) algorithm. Firstly, WT is employed to disassemble the PM2...
July 12, 2017: International Journal of Environmental Research and Public Health
https://www.readbyqxmd.com/read/28704495/space-time-analysis-of-pneumonia-hospitalisations-in-the-netherlands
#9
Elisa Benincà, Michiel van Boven, Thomas Hagenaars, Wim van der Hoek
Community acquired pneumonia is a major global public health problem. In the Netherlands there are 40,000-50,000 hospital admissions for pneumonia per year. In the large majority of these hospital admissions the etiologic agent is not determined and a real-time surveillance system is lacking. Localised and temporal increases in hospital admissions for pneumonia are therefore only detected retrospectively and the etiologic agents remain unknown. Here, we perform spatio-temporal analyses of pneumonia hospital admission data in the Netherlands...
2017: PloS One
https://www.readbyqxmd.com/read/28700903/computer-based-classification-of-chromoendoscopy-images-using-homogeneous-texture-descriptors
#10
Hussam Ali, Muhammad Sharif, Mussarat Yasmin, Mubashir Husain Rehmani
Computer-aided analysis of clinical pathologies is a challenging task in the field of medical imaging. Specifically, the detection of abnormal regions in the frames collected during an endoscopic session is difficult. The variations in the conditions of image acquisition, such as field of view or illumination modification, make it more demanding. Therefore, the design of a computer-assisted diagnostic system for the recognition of gastric abnormalities requires features that are robust to scale, rotation, and illumination variations of the images...
July 5, 2017: Computers in Biology and Medicine
https://www.readbyqxmd.com/read/28692997/automated-classification-and-removal-of-eeg-artifacts-with-svm-and-wavelet-ica
#11
Chong Yeh Sai, Norrima Mokhtar, Hamzah Arof, Paul Cumming, Masahiro Iwahashi
Brain electrical activity recordings by electroencephalography (EEG) are often contaminated with signal artifacts. Procedures for automated removal of EEG artifacts are frequently sought for clinical diagnostics and brain computer interface (BCI) applications. In recent years, a combination of independent component analysis (ICA) and discrete wavelet transform (DWT) has been introduced as standard technique for EEG artifact removal. However, in performing the wavelet-ICA procedure, visual inspection or arbitrary thresholding may be required for identifying artifactual components in the EEG signal...
July 4, 2017: IEEE Journal of Biomedical and Health Informatics
https://www.readbyqxmd.com/read/28692986/heavy-tailed-noise-suppression-and-derivative-wavelet-scalogram-for-detecting-dna-copy-number-aberrations
#12
Nha Nguyen, An Vo, Haibin Sun, Heng Huang
Most existing array comparative genomic hybridization (array CGH) data processing methods and evaluation models assumed that the probability density function of noise in array CGH is a Gaussian distribution. However, in practice such noise distribution is peaky and heavy-tailed. A more accurate and sufficient model of noise in array CGH data is necessary and beneficial to the detection of DNA copy number variations. We analyze the real array CGH data from different platforms and show that the distribution of noise in array CGH data is fitted very well by generalized Gaussian distribution (GGD)...
July 6, 2017: IEEE/ACM Transactions on Computational Biology and Bioinformatics
https://www.readbyqxmd.com/read/28692691/extracting-time-frequency-feature-of-single-channel-vastus-medialis-emg-signals-for-knee-exercise-pattern-recognition
#13
Yi Zhang, Peiyang Li, Xuyang Zhu, Steven W Su, Qing Guo, Peng Xu, Dezhong Yao
The EMG signal indicates the electrophysiological response to daily living of activities, particularly to lower-limb knee exercises. Literature reports have shown numerous benefits of the Wavelet analysis in EMG feature extraction for pattern recognition. However, its application to typical knee exercises when using only a single EMG channel is limited. In this study, three types of knee exercises, i.e., flexion of the leg up (standing), hip extension from a sitting position (sitting) and gait (walking) are investigated from 14 healthy untrained subjects, while EMG signals from the muscle group of vastus medialis and the goniometer on the knee joint of the detected leg are synchronously monitored and recorded...
2017: PloS One
https://www.readbyqxmd.com/read/28691485/single-drop-raman-imaging-exposes-the-trace-contaminants-in-milk
#14
Zong Tan, Tingting Lou, Zhixuan Huang, Jing Zong, Kexin Xu, Qifeng Li, Da Chen
Better milk safety control can offer important means to promote public health. However, few technologies can detect different types of contaminants in milk simultaneously. In this regard, the present work proposes a single-drop Raman imaging (SDRI) strategy for semi-quantitation of multiple hazardous factors in milk solutions. By developing SDRI strategy that incorporates coffee-ring effect (a natural phenomenon often presents in a condensed circle pattern after a drop evaporated) for sample pretreatment and discrete wavelet transform for spectra processing, the method serves well to expose typical hazardous molecular species in milk products, such as melamine, sodium thiocyanate and lincomycin hydrochloride, with little sample preparation...
July 10, 2017: Journal of Agricultural and Food Chemistry
https://www.readbyqxmd.com/read/28685918/a-long-term-bci-study-with-ecog-recordings-in-freely-moving-rats
#15
Thomas Costecalde, Tetiana Aksenova, Napoleon Torres-Martinez, Andriy Eliseyev, Corinne Mestais, Cecile Moro, Alim Louis Benabid
BACKGROUND: Brain Computer Interface (BCI) studies are performed in an increasing number of applications. Questions are raised about electrodes, data processing and effectors. Experiments are needed to solve these issues. OBJECTIVE: To develop a simple BCI set-up to easier studies for improving the mathematical tools to process the ECoG to control an effector. METHOD: We designed a simple BCI using transcranial electrodes (17 screws, three mechanically linked to create a common reference, 14 used as recording electrodes) to record Electro-Cortico-Graphic (ECoG) neuronal activities in rodents...
July 6, 2017: Neuromodulation: Journal of the International Neuromodulation Society
https://www.readbyqxmd.com/read/28683095/computer-algorithms-for-automated-detection-and-analysis-of-local-ca2-releases-in-spontaneously-beating-cardiac-pacemaker-cells
#16
Alexander V Maltsev, Sean P Parsons, Mary S Kim, Kenta Tsutsui, Michael D Stern, Edward G Lakatta, Victor A Maltsev, Oliver Monfredi
Local Ca2+ Releases (LCRs) are crucial events involved in cardiac pacemaker cell function. However, specific algorithms for automatic LCR detection and analysis have not been developed in live, spontaneously beating pacemaker cells. In the present study we measured LCRs using a high-speed 2D-camera in spontaneously contracting sinoatrial (SA) node cells isolated from rabbit and guinea pig and developed a new algorithm capable of detecting and analyzing the LCRs spatially in two-dimensions, and in time. Our algorithm tracks points along the midline of the contracting cell...
2017: PloS One
https://www.readbyqxmd.com/read/28682261/towards-on-demand-deep-brain-stimulation-using-online-parkinson-s-disease-prediction-driven-by-dynamic-detection
#17
Ameer Mohammed, Majid Zamani, Richard Bayford, Andreas Demosthenous
In Parkinson's disease (PD), on-demand deep brain stimulation (DBS) is required so that stimulation is regulated to reduce side effects resulting from continuous stimulation and PD exacerbation due to untimely stimulation. Also, the progressive nature of PD necessitates the use of dynamic detection schemes that can track the nonlinearities in PD. This paper proposes the use of dynamic feature extraction feature extraction and dynamic pattern classification to achieve dynamic PD detection taking into account the demand for high accuracy, low computation and real-time detection...
July 3, 2017: IEEE Transactions on Neural Systems and Rehabilitation Engineering
https://www.readbyqxmd.com/read/28682119/application-of-time-frequency-domain-transform-to-three-dimensional-interpolation-of-medical-images
#18
Shengqing Lv, Yimin Chen, Zeyu Li, Jiahui Lu, Mingke Gao, Rongrong Lu
Medical image three-dimensional (3D) interpolation is an important means to improve the image effect in 3D reconstruction. In image processing, the time-frequency domain transform is an efficient method. In this article, several time-frequency domain transform methods are applied and compared in 3D interpolation. And a Sobel edge detection and 3D matching interpolation method based on wavelet transform is proposed. We combine wavelet transform, traditional matching interpolation methods, and Sobel edge detection together in our algorithm...
July 6, 2017: Journal of Computational Biology: a Journal of Computational Molecular Cell Biology
https://www.readbyqxmd.com/read/28680910/compressed-sensing-magnetic-resonance-imaging-based-on-shearlet-sparsity-and-nonlocal-total-variation
#19
Ali Pour Yazdanpanah, Emma E Regentova
Compressed sensing (CS) has been utilized for acceleration of data acquisition in magnetic resonance imaging (MRI). MR images can then be reconstructed with an undersampling rate significantly lower than that required by the Nyquist sampling criterion. However, the CS usually produces images with artifacts, especially at high reduction rates. We propose a CS MRI method called shearlet sparsity and nonlocal total variation (SS-NLTV) that exploits SS-NLTV regularization. The shearlet transform is an optimal sparsifying transform with excellent directional sensitivity compared with that by wavelet transform...
April 2017: Journal of Medical Imaging
https://www.readbyqxmd.com/read/28676672/study-on-optimal-selection-of-wavelet-vanishing-moments-for-ecg-denoising
#20
Ziran Peng, Guojun Wang
The frequency characteristics of wavelets and the vanishing moments of wavelet filters are both important parameters of wavelets. Clarifying the relationship between the wavelet frequency characteristics and the vanishing moments of the wavelet filter can provide a theoretical basis for selecting the best wavelet. In this paper, the frequency characteristics of wavelets were analyzed by mathematical modeling, the mathematical relationship between wavelet frequency characteristics and vanishing moments was clarified, the optimal wavelet base function was selected hierarchically according to the amplitude frequency characteristics of ECG signal, and an accurate notch filter was realized according to the frequency characteristics of the noise...
July 4, 2017: Scientific Reports
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