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https://www.readbyqxmd.com/read/28343061/automated-diabetic-macular-edema-dme-grading-system-using-dwt-dct-features-and-maculopathy-index
#1
U Rajendra Acharya, Muthu Rama Krishnan Mookiah, Joel E W Koh, Jen Hong Tan, Sulatha V Bhandary, A Krishna Rao, Yuki Hagiwara, Chua Kuang Chua, Augustinus Laude
The cause of diabetic macular edema (DME) is due to prolonged and uncontrolled diabetes mellitus (DM) which affects the vision of diabetic subjects. DME is graded based on the exudate location from the macula. It is clinically diagnosed using fundus images which is tedious and time-consuming. Regular eye screening and subsequent treatment may prevent the vision loss. Hence, in this work, a hybrid system based on Radon transform (RT), discrete wavelet transform (DWT) and discrete cosine transform (DCT) are proposed for an automated detection of DME...
March 19, 2017: Computers in Biology and Medicine
https://www.readbyqxmd.com/read/28324998/prediction-of-the-outcome-in-cardiac-arrest-patients-undergoing-hypothermia-using-eeg-wavelet-entropy
#2
Hana Moshirvaziri, Nima Ramezan-Arab, Shadnaz Asgari
Cardiac arrest (CA) is the leading cause of death in the United States. Induction of hypothermia has been found to improve the functional recovery of CA patients after resuscitation. However, there is no clear guideline for the clinicians yet to determine the prognosis of the CA when patients are treated with hypothermia. The present work aimed at the development of a prognostic marker for the CA patients undergoing hypothermia. A quantitative measure of the complexity of Electroencephalogram (EEG) signals, called wavelet sub-band entropy, was employed to predict the patients' outcomes...
August 2016: Conference Proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society
https://www.readbyqxmd.com/read/28324996/analysis-of-the-non-stationarity-of-neural-activity-during-an-auditory-oddball-task-in-schizophrenia
#3
P Nunez, J Poza, J Gomez-Pilar, A Bachiller, C Gomez, A Lubeiro, V Molina, R Hornero
The aim of this study was to characterize brain dynamics during an auditory oddball task. For this purpose, a measure of the non-stationarity of a given time-frequency representation (TFR) was applied to electroencephalographic (EEG) signals. EEG activity was acquired from 20 schizophrenic (SCH) patients and 20 healthy controls while they underwent a three-stimulus auditory oddball task. The Degree of Stationarity (DS), a measure of the non-stationarity of the TFR, was computed using the continuous wavelet transform...
August 2016: Conference Proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society
https://www.readbyqxmd.com/read/28324937/data-driven-estimation-of-blood-pressure-using-photoplethysmographic-signals
#4
Shi Chao Gao, Peter Wittek, Li Zhao, Wen Jun Jiang
Noninvasive measurement of blood pressure by optical methods receives considerable interest, but the complexity of the measurement and the difficulty of adjusting parameters restrict applications. We develop a method for estimating the systolic and diastolic blood pressure using a single-point optical recording of a photoplethysmographic (PPG) signal. The estimation is data-driven, we use automated machine learning algorithms instead of mathematical models. Combining supervised learning with a discrete wavelet transform, the method is insensitive to minor irregularities in the PPG waveform, hence both pulse oximeters and smartphone cameras can record the signal...
August 2016: Conference Proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society
https://www.readbyqxmd.com/read/28316861/complexity-analysis-of-electroencephalogram-dynamics-in-patients-with-parkinson-s-disease
#5
Guotao Liu, Yanping Zhang, Zhenghui Hu, Xiuquan Du, Wanqing Wu, Chenchu Xu, Xiangyang Wang, Shuo Li
In this study, a new combination scheme has been proposed for detecting Parkinson's disease (PD) from electroencephalogram (EEG) signal recorded from normal subjects and PD patients. The scheme is based on discrete wavelet transform (DWT), sample entropy (SampEn), and the three-way decision model in analysis of EEG signal. The EEG signal is noisy and nonstationary, and, as a consequence, it becomes difficult to distinguish it visually. However, the scheme is a well-established methodology in analysis of EEG signal in three stages...
2017: Parkinson's Disease
https://www.readbyqxmd.com/read/28303099/meet-spinky-an-open-source-spindle-and-k-complex-detection-toolbox-validated-on-the-open-access-montreal-archive-of-sleep-studies-mass
#6
Tarek Lajnef, Christian O'Reilly, Etienne Combrisson, Sahbi Chaibi, Jean-Baptiste Eichenlaub, Perrine M Ruby, Pierre-Emmanuel Aguera, Mounir Samet, Abdennaceur Kachouri, Sonia Frenette, Julie Carrier, Karim Jerbi
Sleep spindles and K-complexes are among the most prominent micro-events observed in electroencephalographic (EEG) recordings during sleep. These EEG microstructures are thought to be hallmarks of sleep-related cognitive processes. Although tedious and time-consuming, their identification and quantification is important for sleep studies in both healthy subjects and patients with sleep disorders. Therefore, procedures for automatic detection of spindles and K-complexes could provide valuable assistance to researchers and clinicians in the field...
2017: Frontiers in Neuroinformatics
https://www.readbyqxmd.com/read/28300790/non-destructive-detection-of-wire-rope-discontinuities-from-residual-magnetic-field-images-using-the-hilbert-huang-transform-and-compressed-sensing
#7
Juwei Zhang, Xiaojiang Tan, Pengbo Zheng
Electromagnetic methods are commonly employed to detect wire rope discontinuities. However, determining the residual strength of wire rope based on the quantitative recognition of discontinuities remains problematic. We have designed a prototype device based on the residual magnetic field (RMF) of ferromagnetic materials, which overcomes the disadvantages associated with in-service inspections, such as large volume, inconvenient operation, low precision, and poor portability by providing a relatively small and lightweight device with improved detection precision...
March 16, 2017: Sensors
https://www.readbyqxmd.com/read/28298702/fixed-versus-mixed-rsa-%C3%A2-explaining-visual-representations-by-fixed-and-mixed-feature-sets-from-shallow-and-deep-computational-models
#8
Seyed-Mahdi Khaligh-Razavi, Linda Henriksson, Kendrick Kay, Nikolaus Kriegeskorte
Studies of the primate visual system have begun to test a wide range of complex computational object-vision models. Realistic models have many parameters, which in practice cannot be fitted using the limited amounts of brain-activity data typically available. Task performance optimization (e.g. using backpropagation to train neural networks) provides major constraints for fitting parameters and discovering nonlinear representational features appropriate for the task (e.g. object classification). Model representations can be compared to brain representations in terms of the representational dissimilarities they predict for an image set...
February 2017: Journal of Mathematical Psychology
https://www.readbyqxmd.com/read/28295824/wavelet-entropy-of-bold-time-series-an-application-to-rolandic-epilepsy
#9
Lalit Gupta, Jacobus F A Jansen, Paul A M Hofman, René M H Besseling, Anton J A de Louw, Albert P Aldenkamp, Walter H Backes
PURPOSE: To assess the wavelet entropy for the characterization of intrinsic aberrant temporal irregularities in the time series of resting-state blood-oxygen-level-dependent (BOLD) signal fluctuations. Further, to evaluate the temporal irregularities (disorder/order) on a voxel-by-voxel basis in the brains of children with Rolandic epilepsy. MATERIALS AND METHODS: The BOLD time series was decomposed using the discrete wavelet transform and the wavelet entropy was calculated...
March 11, 2017: Journal of Magnetic Resonance Imaging: JMRI
https://www.readbyqxmd.com/read/28286048/effect-of-age-on-cutaneous-vasomotor-responses-during-local-skin-heating
#10
Gary J Hodges, Matthew M Mallette, Garry A Tew, John M Saxton, James Moss, Alan D Ruddock, Markos Klonizakis
This study examined the effect of ageing on the low-frequency oscillations (vasomotion) of skin blood flow in response to local heating (LH). Skin blood flow was assessed by laser-Doppler flowmetry on the forearm at rest (33°C) and in response to LH of the skin to both 42°C and 44°C in 14 young (24±1years) and 14 older (64±1years) participants. Vasomotion was analyzed using a wavelet transform to investigate power of the frequency intervals associated with endothelial, neural, myogenic, respiratory, and cardiac activities of the laser-Doppler signal...
March 9, 2017: Microvascular Research
https://www.readbyqxmd.com/read/28284000/a-method-for-microcalcifications-detection-in-breast-mammograms
#11
Abbas H Hassin Alasadi, Ahmed Kadem Hamed Al-Saedi
Breast cancer is the most cause of death for women above age 40 around the world. In this paper, we propose a method to detect microcalcifications in digital mammography images using two-dimensional Discrete Wavelets Transform and image enhancement techniques for removing noise as well as to get a better contrast. The initial step is applying a preprocessing techniques to improve the edge of the breast and then segmentation process (Region of interest) for eliminating some regions in the image, which are not useful for the mammography interpretation...
April 2017: Journal of Medical Systems
https://www.readbyqxmd.com/read/28278203/a-high-performance-seizure-detection-algorithm-based-on-discrete-wavelet-transform-dwt-and-eeg
#12
Duo Chen, Suiren Wan, Jing Xiang, Forrest Sheng Bao
In the past decade, Discrete Wavelet Transform (DWT), a powerful time-frequency tool, has been widely used in computer-aided signal analysis of epileptic electroencephalography (EEG), such as the detection of seizures. One of the important hurdles in the applications of DWT is the settings of DWT, which are chosen empirically or arbitrarily in previous works. The objective of this study aimed to develop a framework for automatically searching the optimal DWT settings to improve accuracy and to reduce computational cost of seizure detection...
2017: PloS One
https://www.readbyqxmd.com/read/28272020/a-stationary-wavelet-transform-and-a-time-frequency-based-spike-detection-algorithm-for-extracellular-recorded-data
#13
Florian Lieb, Hans-Georg Stark, Christiane Thielemann
OBJECTIVE Spike detection from extracellular recordings is a crucial preprocessing step when analyzing neuronal activity. The decision whether a specific part of the signal is a spike or not is important for any kind of other subsequent preprocessing steps, like spike sorting or burst detection in order to reduce the classification of erroneously identified spikes. Many spike detection algorithms have already been suggested, all working reasonably well whenever the signal-to-noise ratio is large enough. When the noise level is high, however, these algorithms have a poor performance...
March 8, 2017: Journal of Neural Engineering
https://www.readbyqxmd.com/read/28269816/automatic-epileptic-seizure-detection-in-eegs-using-mf-dfa-svm-based-on-cloud-computing
#14
Zhongnan Zhang, Tingxi Wen, Wei Huang, Meihong Wang, Chunfeng Li
BACKGROUND: Epilepsy is a chronic disease with transient brain dysfunction that results from the sudden abnormal discharge of neurons in the brain. Since electroencephalogram (EEG) is a harmless and noninvasive detection method, it plays an important role in the detection of neurological diseases. However, the process of analyzing EEG to detect neurological diseases is often difficult because the brain electrical signals are random, non-stationary and nonlinear. OBJECTIVE: In order to overcome such difficulty, this study aims to develop a new computer-aided scheme for automatic epileptic seizure detection in EEGs based on multi-fractal detrended fluctuation analysis (MF-DFA) and support vector machine (SVM)...
2017: Journal of X-ray Science and Technology
https://www.readbyqxmd.com/read/28269716/motor-imagery-based-brain-computer-interface-using-transform-domain-features
#15
Ahmed M Elbaz, Ahmed T Ahmed, Ayman M Mohamed, Mohamed A Oransa, Khaled S Sayed, Ayman M Eldeib
Brain Computer Interface (BCI) is a channel of communication between the human brain and an external device through brain electrical activity. In this paper, we extracted different features to boost the classification accuracy as well as the mutual information of BCI systems. The extracted features include the magnitude of the discrete Fourier transform and the wavelet coefficients for the EEG signals in addition to distance series values and invariant moments calculated for the reconstructed phase space of the EEG measurements...
August 2016: Conference Proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society
https://www.readbyqxmd.com/read/28269284/cross-frequency-information-transfer-from-eeg-to-emg-in-grasping
#16
Winnie K Y So, Lingling Yang, Beth Jelfs, Qi She, Savio W H Wong, Joseph N Mak, Rosa H M Chan
This paper presents an investigation into the cortico-muscular relationship during a grasping task by evaluating the information transfer between EEG and EMG signals. Information transfer was computed via a non-linear model-free measure, transfer entropy (TE). To examine the cross-frequency interaction, TEs were computed after the times series were decomposed into various frequency ranges via wavelet transform. Our results demonstrate the capability of TE to capture the direct interaction between EEG and EMG...
August 2016: Conference Proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society
https://www.readbyqxmd.com/read/28269192/a-probabilistic-framework-based-on-slic-superpixel-and-gaussian-processes-for-segmenting-nerves-in-ultrasound-images
#17
Julian Gil Gonzalez, Mauricio A Alvarez, Alvaro A Orozco
We deal with an important problem in the field of anesthesiology known as automatic segmentation of nerve structures depicted in ultrasound images. This is important to aid the experts in anesthesiology, in order to carry out Peripheral Nerve Blocking (PNB). Ultrasound imaging has gained recent interest for performing PNB procedures since it offers a non-invasive visualization of the nerve and the anatomical structures around it. However, the location of these nerves in ultrasound images is a difficult task for the specialist due to the artifacts (i...
August 2016: Conference Proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society
https://www.readbyqxmd.com/read/28269120/automated-embolic-signal-detection-using-adaptive-gain-control-and-classification-using-anfis
#18
Praotasna Sombune, Phongphan Phienphanich, Sombat Muengtaweepongsa, Anuchit Ruamthanthong, Charturong Tantibundhit
This work proposes an automated system for real-time high-accuracy detection of cerebral embolic signals (ES) to couple with transcranial Doppler ultrasound (TCD) devices in diagnosing a risk of stroke. The algorithm employs Adaptive Gain Control (AGC) approach to capture suspected ESs in real-time. Then, Adaptive Wavelet Packet Transform (AWPT) and Fast Fourier Transform (FFT) are used to extract from them features most efficiently representing ES, which determined by Sequential Feature Selection technique...
August 2016: Conference Proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society
https://www.readbyqxmd.com/read/28269119/analysis-of-embolic-signals-with-directional-dual-tree-rational-dilation-wavelet-transform
#19
Gorkem Serbes, Nizamettin Aydin
The dyadic discrete wavelet transform (dyadic-DWT), which is based on fixed integer sampling factor, has been used before for processing piecewise smooth biomedical signals. However, the dyadic-DWT has poor frequency resolution due to the low-oscillatory nature of its wavelet bases and therefore, it is less effective in processing embolic signals (ESs). To process ESs more effectively, a wavelet transform having better frequency resolution than the dyadic-DWT is needed. Therefore, in this study two ESs, containing micro-emboli and artifact waveforms, are analyzed with the Directional Dual Tree Rational-Dilation Wavelet Transform (DDT-RADWT)...
August 2016: Conference Proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society
https://www.readbyqxmd.com/read/28269116/wolff-parkinson-white-wpw-syndrome-the-detection-of-delta-wave-in-an-electrocardiogram-ecg
#20
Hassan Adam Mahamat, Sabir Jacquir, Cliff Khalil, Gabriel Laurent, Stephane Binczak
The delta wave remains an important indicator to diagnose the WPW syndrome. In this paper, a new method of detection of delta wave in an ECG signal is proposed. Firstly, using the continuous wavelet transform, the P wave, the QRS complex and the T wave are detected, then their durations are computed after determination of the boundary location (onsets and offsets of the P, QRS and T waves). Secondly, the PR duration, the QRS duration and the upstroke of the QRS complex are used to determine the presence or absence of the delta wave...
August 2016: Conference Proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society
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