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Discrete wavelet transform

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https://www.readbyqxmd.com/read/28324937/data-driven-estimation-of-blood-pressure-using-photoplethysmographic-signals
#1
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
#2
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/28295824/wavelet-entropy-of-bold-time-series-an-application-to-rolandic-epilepsy
#3
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/28284000/a-method-for-microcalcifications-detection-in-breast-mammograms
#4
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
#5
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/28269716/motor-imagery-based-brain-computer-interface-using-transform-domain-features
#6
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/28269119/analysis-of-embolic-signals-with-directional-dual-tree-rational-dilation-wavelet-transform
#7
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/28268540/robust-watermarking-in-non-roi-of-medical-images-based-on-dct-dwt
#8
M Jamali, S Samavi, N Karimi, S M R Soroushmehr, K Ward, K Najarian
Increasing demand and utilization of telemedicine require transmission of medical information and images over internet. Since authenticity of received images is crucial and patient's information should be included with minimum changes in images, robust watermarking schemes are needed. In this paper, we propose a robust watermark method that embeds patient's information outside the region of interest (ROI) in medical image. In order to find appropriate regions for embedding, we use saliency as a means of measuring importance of regions and find blocks having minimum overlap with the ROI...
August 2016: Conference Proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society
https://www.readbyqxmd.com/read/28268417/combined-accelerometer-and-emg-analysis-to-differentiate-essential-tremor-from-parkinson-s-disease
#9
Nooshin Haji Ghassemi, Franz Marxreiter, Cristian F Pasluosta, Patrick Kugler, Johannes Schlachetzki, Axel Schramm, Bjoern M Eskofier, Jochen Klucken
In this study, we intended to differentiate patients with essential tremor (ET) from tremor dominant Parkinson disease (PD). Accelerometer and electromyographic signals of hand movement from standardized upper extremity movement tests (resting, holding, carrying weight) were extracted from 13 PD and 11 ET patients. The signals were filtered to remove noise and non-tremor high frequency components. A set of statistical features was then extracted from the discrete wavelet transformation of the signals. Principal component analysis was utilized to reduce dimensionality of the feature space...
August 2016: Conference Proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society
https://www.readbyqxmd.com/read/28261818/on-the-impact-of-smoothing-and-noise-on-robustness-of-ct-and-cbct-radiomics-features-for-patients-with-head-and-neck-cancers
#10
Hassan Bagher-Ebadian, Farzan Siddiqui, Liu Chang, Benjamin Movsas, Indrin J Chetty
PURPOSE: We investigated the characteristics of radiomics features extracted from planning CT (pCT) and cone beam CT (CBCT) image datasets acquired for 18 oropharyngeal cancer patients treated with fractionated radiation therapy. Images were subjected to smoothing, sharpening, and noise to evaluate changes in features relative to baseline datasets. METHODS: Textural features were extracted from tumor volumes, contoured on pCT and CBCT images, according to the following 8 different classes: Intensity Based Histogram Features (IBHF), Gray Level Run Length (GLRL), Law's Textural information (LAWS), Discrete Orthonormal Stockwell Transform (DOST), Local Binary Pattern (LBP), Two-Dimensional Wavelet Transform (2DWT), Two Dimensional Gabor Filter (2DGF), and Gray Level Co-Occurrence Matrix (GLCM)...
March 6, 2017: Medical Physics
https://www.readbyqxmd.com/read/28247307/automated-diagnosis-of-heart-sounds-using-rule-based-classification-tree
#11
Mohamed Esmail Karar, Sahar H El-Khafif, Mohamed A El-Brawany
In order to assist the diagnosis procedure of heart sound signals, this paper presents a new automated method for classifying the heart status using a rule-based classification tree into normal and three abnormal cases; namely the aortic valve stenosis, aortic insufficient, and ventricular septum defect. The developed method includes three main steps as follows. First, one cycle of the heart sound signals is automatically detected and segmented based on time properties of the heart signals. Second, the segmented cycle is preprocessed with the discrete wavelet transform and then largest Lyapunov exponents are calculated to generate the dynamical features of heart sound time series...
April 2017: Journal of Medical Systems
https://www.readbyqxmd.com/read/28227980/motor-imagery-based-brain-computer-interface-using-transform-domain-features
#12
Ahmed M Elbaz, Ahmed T Ahmed, Ayman M Mohamed, Mohamed A Oransa, Khaled S Sayed, Ayman M Eldeib, Ahmed M Elbaz, Ahmed T Ahmed, Ayman M Mohamed, Mohamed A Oransa, Khaled S Sayed, Ayman M Eldeib, Mohamed A Oransa, Khaled S Sayed, Ayman M Mohamed, Ahmed T Ahmed, Ahmed M Elbaz, 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/28227348/analysis-of-embolic-signals-with-directional-dual-tree-rational-dilation-wavelet-transform
#13
Gorkem Serbes, Nizamettin Aydin, Gorkem Serbes, Nizamettin Aydin, Nizamettin Aydin, Gorkem Serbes
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/28226716/robust-watermarking-in-non-roi-of-medical-images-based-on-dct-dwt
#14
M Jamali, S Samavi, N Karimi, S M R Soroushmehr, K Ward, K Najarian, M Jamali, S Samavi, N Karimi, S M R Soroushmehr, K Ward, K Najarian, K Ward, S M R Soroushmehr, M Jamali, S Samavi, K Najarian, N Karimi
Increasing demand and utilization of telemedicine require transmission of medical information and images over internet. Since authenticity of received images is crucial and patient's information should be included with minimum changes in images, robust watermarking schemes are needed. In this paper, we propose a robust watermark method that embeds patient's information outside the region of interest (ROI) in medical image. In order to find appropriate regions for embedding, we use saliency as a means of measuring importance of regions and find blocks having minimum overlap with the ROI...
August 2016: Conference Proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society
https://www.readbyqxmd.com/read/28226610/data-driven-estimation-of-blood-pressure-using-photoplethysmographic-signals
#15
Shi Chao Gao, Peter Wittek, Li Zhao, Wen Jun Jiang, Shi Chao Gao, Peter Wittek, Li Zhao, Wen Jun Jiang, 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/28226587/combined-accelerometer-and-emg-analysis-to-differentiate-essential-tremor-from-parkinson-s-disease
#16
Nooshin Haji Ghassemi, Franz Marxreiter, Cristian F Pasluosta, Patrick Kugler, Johannes Schlachetzki, Axel Schramm, Bjoern M Eskofier, Jochen Klucken, Nooshin Haji Ghassemi, Franz Marxreiter, Cristian F Pasluosta, Patrick Kugler, Johannes Schlachetzki, Axel Schramm, Bjoern M Eskofier, Jochen Klucken, Johannes Schlachetzki, Nooshin Haji Ghassemi, Cristian F Pasluosta, Patrick Kugler, Franz Marxreiter, Axel Schramm, Bjoern M Eskofier, Jochen Klucken
In this study, we intended to differentiate patients with essential tremor (ET) from tremor dominant Parkinson disease (PD). Accelerometer and electromyographic signals of hand movement from standardized upper extremity movement tests (resting, holding, carrying weight) were extracted from 13 PD and 11 ET patients. The signals were filtered to remove noise and non-tremor high frequency components. A set of statistical features was then extracted from the discrete wavelet transformation of the signals. Principal component analysis was utilized to reduce dimensionality of the feature space...
August 2016: Conference Proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society
https://www.readbyqxmd.com/read/28207900/classification-and-identification-of-rhodobryum-roseum-limpr-and-its-adulterants-based-on-fourier-transform-infrared-spectroscopy-ftir-and-chemometrics
#17
Zhen Cao, Zhenjie Wang, Zhonglin Shang, Jiancheng Zhao
Fourier-transform infrared spectroscopy (FTIR) with the attenuated total reflectance technique was used to identify Rhodobryum roseum from its four adulterants. The FTIR spectra of six samples in the range from 4000 cm-1 to 600 cm-1 were obtained. The second-derivative transformation test was used to identify the small and nearby absorption peaks. A cluster analysis was performed to classify the spectra in a dendrogram based on the spectral similarity. Principal component analysis (PCA) was used to classify the species of six moss samples...
2017: PloS One
https://www.readbyqxmd.com/read/28194648/epileptic-seizure-classifications-of-single-channel-scalp-eeg-data-using-wavelet-based-features-and-svm
#18
Suparerk Janjarasjitt
In this study, wavelet-based features of single-channel scalp EEGs recorded from subjects with intractable seizure are examined for epileptic seizure classification. The wavelet-based features extracted from scalp EEGs are simply based on detail and approximation coefficients obtained from the discrete wavelet transform. Support vector machine (SVM), one of the most commonly used classifiers, is applied to classify vectors of wavelet-based features of scalp EEGs into either seizure or non-seizure class. In patient-based epileptic seizure classification, a training data set used to train SVM classifiers is composed of wavelet-based features of scalp EEGs corresponding to the first epileptic seizure event...
February 13, 2017: Medical & Biological Engineering & Computing
https://www.readbyqxmd.com/read/28191461/three-class-mammogram-classification-based-on-descriptive-cnn-features
#19
M Mohsin Jadoon, Qianni Zhang, Ihsan Ul Haq, Sharjeel Butt, Adeel Jadoon
In this paper, a novel classification technique for large data set of mammograms using a deep learning method is proposed. The proposed model targets a three-class classification study (normal, malignant, and benign cases). In our model we have presented two methods, namely, convolutional neural network-discrete wavelet (CNN-DW) and convolutional neural network-curvelet transform (CNN-CT). An augmented data set is generated by using mammogram patches. To enhance the contrast of mammogram images, the data set is filtered by contrast limited adaptive histogram equalization (CLAHE)...
2017: BioMed Research International
https://www.readbyqxmd.com/read/28173806/ecg-signal-performance-de-noising-assessment-based-on-threshold-tuning-of-dual-tree-wavelet-transform
#20
Oussama El B'charri, Rachid Latif, Khalifa Elmansouri, Abdenbi Abenaou, Wissam Jenkal
BACKGROUND: Since the electrocardiogram (ECG) signal has a low frequency and a weak amplitude, it is sensitive to miscellaneous mixed noises, which may reduce the diagnostic accuracy and hinder the physician's correct decision on patients. METHODS: The dual tree wavelet transform (DT-WT) is one of the most recent enhanced versions of discrete wavelet transform. However, threshold tuning on this method for noise removal from ECG signal has not been investigated yet...
February 7, 2017: Biomedical Engineering Online
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