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Wavelet transform

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https://www.readbyqxmd.com/read/29149053/laser-induced-breakdown-spectroscopy-for-rapid-discrimination-of-heavy-metal-contaminated-seafood-tegillarca-granosa
#1
Guoli Ji, Pengchao Ye, Yijian Shi, Leiming Yuan, Xiaojing Chen, Mingshun Yuan, Dehua Zhu, Xi Chen, Xinyu Hu, Jing Jiang
Tegillarca granosa samples contaminated artificially by three kinds of toxic heavy metals including zinc (Zn), cadmium (Cd), and lead (Pb) were attempted to be distinguished using laser-induced breakdown spectroscopy (LIBS) technology and pattern recognition methods in this study. The measured spectra were firstly processed by a wavelet transform algorithm (WTA), then the generated characteristic information was subsequently expressed by an information gain algorithm (IGA). As a result, 30 variables obtained were used as input variables for three classifiers: partial least square discriminant analysis (PLS-DA), support vector machine (SVM), and random forest (RF), among which the RF model exhibited the best performance, with 93...
November 17, 2017: Sensors
https://www.readbyqxmd.com/read/29147730/on-the-skilled-plantar-flexor-motor-action-and-unique-electromyographic-activity-of-ballet-dancers
#2
Sakiko Saito, Hiroki Obata, Mayumi Kuno-Mizumura, Kimitaka Nakazawa
The study aimed to compare the ability of dance and non-dance subjects to perform fine control of a simple heel-raising/lowering movement, and to determine if there are any differences in motor unit activity in the primary plantar flexor muscles during the movement. Subjects were instructed to accurately track a sinusoidal trace with a heel-raising and lowering movement at four controlled frequencies (1, 0.5, 0.25, and 0.125 Hz). The ankle joint angle was used to characterize movement errors from the target...
November 16, 2017: Experimental Brain Research. Experimentelle Hirnforschung. Expérimentation Cérébrale
https://www.readbyqxmd.com/read/29142739/classification-of-amyotrophic-lateral-sclerosis-disease-based-on-convolutional-neural-network-and-reinforcement-sample-learning-algorithm
#3
Abdulkadir Sengur, Yaman Akbulut, Yanhui Guo, Varun Bajaj
Electromyogram (EMG) signals contain useful information of the neuromuscular diseases like amyotrophic lateral sclerosis (ALS). ALS is a well-known brain disease, which can progressively degenerate the motor neurons. In this paper, we propose a deep learning based method for efficient classification of ALS and normal EMG signals. Spectrogram, continuous wavelet transform (CWT), and smoothed pseudo Wigner-Ville distribution (SPWVD) have been employed for time-frequency (T-F) representation of EMG signals. A convolutional neural network is employed to classify these features...
December 2017: Health Information Science and Systems
https://www.readbyqxmd.com/read/29142483/new-results-on-the-continuous-weinstein-wavelet-transform
#4
Hatem Mejjaoli, Ahmedou Ould Ahmed Salem
We consider the continuous wavelet transform [Formula: see text] associated with the Weinstein operator. We introduce the notion of localization operators for [Formula: see text]. In particular, we prove the boundedness and compactness of localization operators associated with the continuous wavelet transform. Next, we analyze the concentration of [Formula: see text] on sets of finite measure. In particular, Benedicks-type and Donoho-Stark's uncertainty principles are given. Finally, we prove many versions of Heisenberg-type uncertainty principles for [Formula: see text]...
2017: Journal of Inequalities and Applications
https://www.readbyqxmd.com/read/29140294/a-human-body-pressure-distribution-imaging-system-based-on-wavelet-analysis-and-resistance-tomography
#5
Shuanfeng Zhao, Wenbo Wang, Wei Guo, Chuanwei Zhang
In this paper, a pressure distribution sensing system based on wavelet analysis and resistance tomography is proposed to overcome the shortcomings of a traditional electrode type pressure distribution sensor, which needs to be arranged with many electrodes and has a high production cost. The system uses ADS1256, a constant current source module, a serial communication module, a Raspberry host, a touch screen, and other components. The wavelet transform is used to preprocess the collected signal to improve the anti-jamming performance of the system...
November 15, 2017: Sensors
https://www.readbyqxmd.com/read/29138687/analysis-of-the-biceps-brachii-muscle-by-varying-the-arm-movement-level-and-load-resistance-band
#6
Nuradebah Burhan, Mohammad 'Afif Kasno, Rozaimi Ghazali, Md Radzai Said, Shahrum Shah Abdullah, Mohd Hafiz Jali
Biceps brachii muscle illness is one of the common physical disabilities that requires rehabilitation exercises in order to build up the strength of the muscle after surgery. It is also important to monitor the condition of the muscle during the rehabilitation exercise through electromyography (EMG) signals. The purpose of this study was to analyse and investigate the selection of the best mother wavelet (MWT) function and depth of the decomposition level in the wavelet denoising EMG signals through the discrete wavelet transform (DWT) method at each decomposition level...
2017: Journal of Healthcare Engineering
https://www.readbyqxmd.com/read/29132126/improved-analysis-of-ground-vibrations-produced-by-man-made-sources
#7
Daniel Ainalis, Loïc Ducarne, Olivier Kaufmann, Jean-Pierre Tshibangu, Olivier Verlinden, Georges Kouroussis
Man-made sources of ground vibration must be carefully monitored in urban areas in order to ensure that structural damage and discomfort to residents is prevented or minimised. The research presented in this paper provides a comparative evaluation of various methods used to analyse a series of tri-axial ground vibration measurements generated by rail, road, and explosive blasting. The first part of the study is focused on comparing various techniques to estimate the dominant frequency, including time-frequency analysis...
November 10, 2017: Science of the Total Environment
https://www.readbyqxmd.com/read/29118966/gaussian-elimination-based-novel-canonical-correlation-analysis-method-for-eeg-motion-artifact-removal
#8
Vandana Roy, Shailja Shukla, Piyush Kumar Shukla, Paresh Rawat
The motion generated at the capturing time of electro-encephalography (EEG) signal leads to the artifacts, which may reduce the quality of obtained information. Existing artifact removal methods use canonical correlation analysis (CCA) for removing artifacts along with ensemble empirical mode decomposition (EEMD) and wavelet transform (WT). A new approach is proposed to further analyse and improve the filtering performance and reduce the filter computation time under highly noisy environment. This new approach of CCA is based on Gaussian elimination method which is used for calculating the correlation coefficients using backslash operation and is designed for EEG signal motion artifact removal...
2017: Journal of Healthcare Engineering
https://www.readbyqxmd.com/read/29118808/pathological-brain-detection-using-weiner-filtering-2d-discrete-wavelet-transform-probabilistic-pca-and-random-subspace-ensemble-classifier
#9
Debesh Jha, Ji-In Kim, Moo-Rak Choi, Goo-Rak Kwon
Accurate diagnosis of pathological brain images is important for patient care, particularly in the early phase of the disease. Although numerous studies have used machine-learning techniques for the computer-aided diagnosis (CAD) of pathological brain, previous methods encountered challenges in terms of the diagnostic efficiency owing to deficiencies in the choice of proper filtering techniques, neuroimaging biomarkers, and limited learning models. Magnetic resonance imaging (MRI) is capable of providing enhanced information regarding the soft tissues, and therefore MR images are included in the proposed approach...
2017: Computational Intelligence and Neuroscience
https://www.readbyqxmd.com/read/29117768/wavelet-based-feature-extraction-for-classification-of-epileptic-seizure-eeg-signal
#10
A Sharmila, P Mahalakshmi
Electroencephalogram (EEG) signal-processing techniques are the prominent role in the detection and prediction of epileptic seizures. The detection of epileptic activity is cumbersome and needs a detailed analysis of the EEG data. Therefore, an efficient method for classifying EEG data is required. In this work, a constructive pattern recognition strategy for analysing EEG data as normal and epileptic seizure has been proposed. With this strategy, the signals were decomposed into frequency sub-bands using discrete wavelet transform (DWT)...
November 9, 2017: Journal of Medical Engineering & Technology
https://www.readbyqxmd.com/read/29113056/ultrafast-spectroscopy-of-fano-like-resonance-between-optical-phonon-and-excitons-in-cdse-quantum-dots-dependence-of-coherent-vibrational-wave-packet-dynamics-on-pump-fluence
#11
Victor Nadtochenko, Nikolay Denisov, Arseniy Aybush, Fedor Gostev, Ivan Shelaev, Andrey Titov, Stanislav Umanskiy, And Dmitry Cherepanov
The main goal of the present work is to study the coherent phonon in strongly confined CdSe quantum dots (QDs) under varied pump fluences. The main characteristics of coherent phonons (amplitude, frequency, phase, spectrogram) of CdSe QDs under the red-edge pump of the excitonic band [1S(e)-1S3/2(h)] are reported. We demonstrate for the first time that the amplitude of the coherent optical longitudinal-optical (LO) phonon at 6.16 THz excited in CdSe nanoparticles by a femtosecond unchirped pulse shows a non-monotone dependence on the pump fluence...
November 4, 2017: Nanomaterials
https://www.readbyqxmd.com/read/29111840/ecg-beat-classification-using-empirical-mode-decomposition-and-mixture-of-features
#12
Santanu Sahoo, Monalisa Mohanty, Suresh Behera, Sukanta Kumar Sabut
Computer-aided analysis is useful in predicting arrhythmia conditions of the heart by analysing the recorded ECG signals. In this work, we proposed a method to detect, extract informative features to classify six types of heartbeat of ECG signals obtained from the MIT-BIH Arrhythmia database. The powerful discrete wavelet transform (DWT) is used to eliminate different sources of noises. Empirical mode decomposition (EMD) with adaptive thresholding has been used to detect precise R-peaks and QRS complex. The significant features consists of temporal, morphological and statistical were extracted from the processed ECG signals and combined to form a set of features...
November 7, 2017: Journal of Medical Engineering & Technology
https://www.readbyqxmd.com/read/29081750/coherence-and-coupling-functions-reveal-microvascular-impairment-in-treated-hypertension
#13
Valentina Ticcinelli, Tomislav Stankovski, Dmytro Iatsenko, Alan Bernjak, Adam E Bradbury, Andrew R Gallagher, Peter B M Clarkson, Peter V E McClintock, Aneta Stefanovska
The complex interactions that give rise to heart rate variability (HRV) involve coupled physiological oscillators operating over a wide range of different frequencies and length-scales. Based on the premise that interactions are key to the functioning of complex systems, the time-dependent deterministic coupling parameters underlying cardiac, respiratory and vascular regulation have been investigated at both the central and microvascular levels. Hypertension was considered as an example of a globally altered state of the complex dynamics of the cardiovascular system...
2017: Frontiers in Physiology
https://www.readbyqxmd.com/read/29078042/pet-ct-image-fusion-using-random-forest-and-%C3%A3-trous-wavelet-transform
#14
Ayan Seal, Debotosh Bhattacharjee, Mita Nasipuri, Dionisio Rodríguez-Esparragón, Ernestina Menasalvas, Consuelo Gonzalo-Martin
New image fusion rules for multimodal medical images are proposed in this work. Image fusion rules are defined by Random Forest (RF) learning algorithm and a translation-invariant à-trous wavelet transform (AWT). The proposed method is threefold. First, source images are decomposed into approximation and detail coefficients using AWT. Second, RF is used to choose pixels from the approximation and detail coefficients for forming the approximation and detail coefficients of the fused image. Lastly, inverse AWT (iAWT) is applied to reconstruct fused image...
October 27, 2017: International Journal for Numerical Methods in Biomedical Engineering
https://www.readbyqxmd.com/read/29074247/climatic-and-dam-induced-impacts-on-river-water-temperature-assessment-and-management-implications
#15
Mariola Kędra, Łukasz Wiejaczka
In a changing climate with a warming trend in air temperature, river water temperature increases as a result of heat exchange with the atmosphere. Moreover, of the different types of anthropogenic activity impacting rivers, the construction of dams appears to have multi-dimensional effects on the river environment, and it especially affects the thermal condition of rivers. The aim of the study is to identify and assess the impact of these two distinct sources of water temperature distortion in relation to the natural thermal conditions of rivers...
October 23, 2017: Science of the Total Environment
https://www.readbyqxmd.com/read/29065663/diagnosis-of-alzheimer-s-disease-using-dual-tree-complex-wavelet-transform-pca-and-feed-forward-neural-network
#16
Debesh Jha, Ji-In Kim, Goo-Rak Kwon
Background. Error-free diagnosis of Alzheimer's disease (AD) from healthy control (HC) patients at an early stage of the disease is a major concern, because information about the condition's severity and developmental risks present allows AD sufferer to take precautionary measures before irreversible brain damage occurs. Recently, there has been great interest in computer-aided diagnosis in magnetic resonance image (MRI) classification. However, distinguishing between Alzheimer's brain data and healthy brain data in older adults (age > 60) is challenging because of their highly similar brain patterns and image intensities...
2017: Journal of Healthcare Engineering
https://www.readbyqxmd.com/read/29065660/twin-svm-based-classification-of-alzheimer-s-disease-using-complex-dual-tree-wavelet-principal-coefficients-and-lda
#17
Saruar Alam, Goo-Rak Kwon, Ji-In Kim, Chun-Su Park
Alzheimer's disease (AD) is a leading cause of dementia, which causes serious health and socioeconomic problems. A progressive neurodegenerative disorder, Alzheimer's causes the structural change in the brain, thereby affecting behavior, cognition, emotions, and memory. Numerous multivariate analysis algorithms have been used for classifying AD, distinguishing it from healthy controls (HC). Efficient early classification of AD and mild cognitive impairment (MCI) from HC is imperative as early preventive care could help to mitigate risk factors...
2017: Journal of Healthcare Engineering
https://www.readbyqxmd.com/read/29065613/r-peak-detection-method-using-wavelet-transform-and-modified-shannon-energy-envelope
#18
Jeong-Seon Park, Sang-Woong Lee, Unsang Park
Rapid automatic detection of the fiducial points-namely, the P wave, QRS complex, and T wave-is necessary for early detection of cardiovascular diseases (CVDs). In this paper, we present an R peak detection method using the wavelet transform (WT) and a modified Shannon energy envelope (SEE) for rapid ECG analysis. The proposed WTSEE algorithm performs a wavelet transform to reduce the size and noise of ECG signals and creates SEE after first-order differentiation and amplitude normalization. Subsequently, the peak energy envelope (PEE) is extracted from the SEE...
2017: Journal of Healthcare Engineering
https://www.readbyqxmd.com/read/29065597/patient-specific-deep-architectural-model-for-ecg-classification
#19
Kan Luo, Jianqing Li, Zhigang Wang, Alfred Cuschieri
Heartbeat classification is a crucial step for arrhythmia diagnosis during electrocardiographic (ECG) analysis. The new scenario of wireless body sensor network- (WBSN-) enabled ECG monitoring puts forward a higher-level demand for this traditional ECG analysis task. Previously reported methods mainly addressed this requirement with the applications of a shallow structured classifier and expert-designed features. In this study, modified frequency slice wavelet transform (MFSWT) was firstly employed to produce the time-frequency image for heartbeat signal...
2017: Journal of Healthcare Engineering
https://www.readbyqxmd.com/read/29060837/transient-reduction-in-theta-power-caused-by-interictal-spikes-in-human-temporal-lobe-epilepsy
#20
Manling Ge, Jundan Guo, Yangyang Xing, Zhiguo Feng, Weide Lu, Xinxin Ma, Yuehua Geng, Xin Zhang
The inhibitory impacts of spikes on LFP theta rhythms(4-8Hz) are investigated around sporadic spikes(SSs) based on intracerebral EEG of 4 REM sleep patients with temporal lobe epilepsy(TLE) under the pre-surgical monitoring. Sequential interictal spikes in both genesis area and extended propagation pathway are collected, that, SSs genesis only in anterior hippocampus(aH)(possible propagation pathway in Entorhinal cortex(EC)), only in EC(possible propagation pathway in aH), and in both aH and EC synchronously...
July 2017: Conference Proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society
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