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https://www.readbyqxmd.com/read/28711988/emotion-recognition-based-on-eeg-features-in-movie-clips-with-channel-selection
#1
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
#2
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/28706561/automatic-detection-of-epilepsy-and-seizure-using-multiclass-sparse-extreme-learning-machine-classification
#3
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
#4
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
#5
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/28692997/automated-classification-and-removal-of-eeg-artifacts-with-svm-and-wavelet-ica
#6
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/28692691/extracting-time-frequency-feature-of-single-channel-vastus-medialis-emg-signals-for-knee-exercise-pattern-recognition
#7
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
#8
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
#9
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
#10
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
#11
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
#12
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
#13
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/28674650/an-adaptive-singular-spectrum-analysis-method-for-extracting-brain-rhythms-of-electroencephalography
#14
Hai Hu, Shengxin Guo, Ran Liu, Peng Wang
Artifacts removal and rhythms extraction from electroencephalography (EEG) signals are important for portable and wearable EEG recording devices. Incorporating a novel grouping rule, we proposed an adaptive singular spectrum analysis (SSA) method for artifacts removal and rhythms extraction. Based on the EEG signal amplitude, the grouping rule determines adaptively the first one or two SSA reconstructed components as artifacts and removes them. The remaining reconstructed components are then grouped based on their peak frequencies in the Fourier transform to extract the desired rhythms...
2017: PeerJ
https://www.readbyqxmd.com/read/28655467/determination-of-skeletal-muscle-microvascular-flowmotion-with-contrast-enhanced-ultrasound
#15
Sarah J Blackwood, Renee M Dwyer, Eloise A Bradley, Michelle A Keske, Stephen M Richards, Stephen Rattigan
Most methods of assessing flowmotion (rhythmic oscillation of blood flow through tissue) are limited to small sections of tissue and are invasive in tissues other than skin. To overcome these limitations, we adapted the contrast-enhanced ultrasound (CEUS) technique to assess microvascular flowmotion throughout a large region of tissue, in a non-invasive manner and in real time. Skeletal muscle flowmotion was assessed in anaesthetised Sprague Dawley rats, using CEUS and laser Doppler flowmetry (LDF) for comparison...
June 24, 2017: Ultrasound in Medicine & Biology
https://www.readbyqxmd.com/read/28654638/increased-inspiratory-resistance-affects-the-dynamic-relationship-between-blood-pressure-changes-and-subarachnoid-space-width-oscillations
#16
Magdalena Wszedybyl-Winklewska, Jacek Wolf, Ewa Swierblewska, Katarzyna Kunicka, Kamila Mazur, Marcin Gruszecki, Pawel J Winklewski, Andrzej F Frydrychowski, Leszek Bieniaszewski, Krzysztof Narkiewicz
BACKGROUND AND OBJECTIVE: Respiration is known to affect cerebrospinal fluid (CSF) movement. We hypothesised that increased inspiratory resistance would affect the dynamic relationship between blood pressure (BP) changes and subarachnoid space width (SAS) oscillations. METHODS: Experiments were performed in a group of 20 healthy volunteers undergoing controlled intermittent Mueller Manoeuvres (the key characteristic of the procedure is that a studied person is subjected to a controlled, increased inspiratory resistance which results in marked potentiation of the intrathoracic negative pressure)...
2017: PloS One
https://www.readbyqxmd.com/read/28653015/classification-of-breast-masses-in-ultrasound-images-using-self-adaptive-differential-evolution-extreme-learning-machine-and-rough-set-feature-selection
#17
Kadayanallur Mahadevan Prabusankarlal, Palanisamy Thirumoorthy, Radhakrishnan Manavalan
A method using rough set feature selection and extreme learning machine (ELM) whose learning strategy and hidden node parameters are optimized by self-adaptive differential evolution (SaDE) algorithm for classification of breast masses is investigated. A pathologically proven database of 140 breast ultrasound images, including 80 benign and 60 malignant, is used for this study. A fast nonlocal means algorithm is applied for speckle noise removal, and multiresolution analysis of undecimated discrete wavelet transform is used for accurate segmentation of breast lesions...
April 2017: Journal of Medical Imaging
https://www.readbyqxmd.com/read/28649965/pawflim-reducing-bias-and-uncertainty-to-enable-lower-photon-count-in-flim-experiments
#18
Mauro Silberberg, Hernán E Grecco
Förster resonant energy transfer measured by fluorescence lifetime imaging microscopy (FRET-FLIM) is the method of choice for monitoring the spatio-temporal dynamics of protein interactions in living cells. To obtain an accurate estimate of the molecular fraction of interacting proteins requires a large number of photons, which usually precludes the observation of a fast process, particularly with time correlated single photon counting (TCSPC) based FLIM. In this work, we propose a novel method named pawFLIM (phasor analysis via wavelets) that allows the denoising of FLIM datasets by adaptively and selectively adjusting the desired compromise between spatial and molecular resolution...
June 26, 2017: Methods and Applications in Fluorescence
https://www.readbyqxmd.com/read/28649016/epileptic-seizure-detection-based-on-imbalanced-classification-and-wavelet-packet-transform
#19
Qi Yuan, Weidong Zhou, Liren Zhang, Fan Zhang, Fangzhou Xu, Yan Leng, Dongmei Wei, Meina Chen
PURPOSE: Automatic seizure detection is significant for the diagnosis of epilepsy and the reduction of massive workload for reviewing continuous EEG recordings. METHODS: Compared with the long non-seizure periods, the durations of the seizure events are much shorter in the continuous EEG recordings. So the seizure detection task can be regarded as an imbalanced classification problem. In this paper, a novel method based on the weighted extreme learning machine (ELM) is proposed for seizure detection with imbalanced EEG data distribution...
June 8, 2017: Seizure: the Journal of the British Epilepsy Association
https://www.readbyqxmd.com/read/28645845/the-feature-weighted-receptive-field-an-interpretable-encoding-model-for-complex-feature-spaces
#20
REVIEW
Ghislain St-Yves, Thomas Naselaris
We introduce the feature-weighted receptive field (fwRF), an encoding model designed to balance expressiveness, interpretability and scalability. The fwRFis organized around the notion of a feature map-a transformation of visual stimuli into visual features that preserves the topology of visual space (but not necessarily the native resolution of the stimulus). The key assumption of the fwRFmodel is that activity in each voxel encodes variation in a spatially localized region across multiple feature maps. This region is fixed for all feature maps; however, the contribution of each feature map to voxel activity is weighted...
June 20, 2017: NeuroImage
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