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Discrete wavelet transform

Hongqiang Li, Danyang Yuan, Youxi Wang, Dianyin Cui, Lu Cao
Automatic recognition of arrhythmias is particularly important in the diagnosis of heart diseases. This study presents an electrocardiogram (ECG) recognition system based on multi-domain feature extraction to classify ECG beats. An improved wavelet threshold method for ECG signal pre-processing is applied to remove noise interference. A novel multi-domain feature extraction method is proposed; this method employs kernel-independent component analysis in nonlinear feature extraction and uses discrete wavelet transform to extract frequency domain features...
October 20, 2016: Sensors
Siyuan Lu, Xin Qiu, Jianpin Shi, Na Li, Zhi-Hai Lu, Peng Chen, Meng-Meng Yang, Fang-Yuan Liu, Wen-Juan Jia, Yudong Zhang
(Aim) It is beneficial to classify brain images as healthy or pathological automatically, because 3D brain images can generate so much information that it is time consuming and tedious for manual analysis. Among various 3D brain imaging techniques, magnetic resonance (MR) imaging is the most suitable for brain, and it is now widely applied in hospitals, because it is helpful in the four ways of diagnosis, prognosis, pre-surgical, and postsurgical procedures. Although there are automatic detection methods, they suffer from low accuracy...
October 19, 2016: CNS & Neurological Disorders Drug Targets
Zhenhu Liang, Yue Gu, Xuejing Duan, Lei Cheng, Shujuan Liang, Yunjie Tong, Xiaoli Li
Monitoring the changes of cerebral hemodynamics and the state of consciousness during general anesthesia (GA) is clinically important. There is a great need for developing advanced detectors to investigate the physiological processes of the brain during GA. We developed a multichanneled, functional near-infrared spectroscopy (fNIRS) system device and applied it to GA operation monitoring. The cerebral hemodynamic data from the forehead of 11 patients undergoing propofol and sevoflurane anesthesia were analyzed...
October 2016: Neurophotonics
Sandeep Raj, Kailash Chandra Ray, Om Shankar
BACKGROUND AND OBJECTIVE: The increase in the number of deaths due to cardiovascular diseases (CVDs) has gained significant attention from the study of electrocardiogram (ECG) signals. These ECG signals are studied by the experienced cardiologist for accurate and proper diagnosis, but it becomes difficult and time-consuming for long-term recordings. Various signal processing techniques are studied to analyze the ECG signal, but they bear limitations due to the non-stationary behavior of ECG signals...
November 2016: Computer Methods and Programs in Biomedicine
Nima Karimian, Zimu Guo, Mark Tehranipoor, Domenic Forte
Traditional passwords are inadequate as cryptographic keys, as they are easy to forge and are vulnerable to guessing. Human biometrics have been proposed as a promising alternative due to their intrinsic nature. Electrocardiogram (ECG) is an emerging biometric that is extremely difficult to forge and circumvent, but has not yet been heavily investigated for cryptographic key generation. ECG has challenges with respect to immunity to noise, abnormalities, etc. In this paper, we propose a novel key generation approach that extracts keys from real valued ECG features with high reliability and entropy in mind...
September 8, 2016: IEEE Transactions on Bio-medical Engineering
F C Cruz, E F Simas Filho, M C S Albuquerque, I C Silva, C T T Farias, L L Gouvêa
This work studies methods for efficient extraction and selection of features in the context of a decision support system based on neural networks. The data comes from ultrasonic testing of steel welded joints, in which are found three types of flaws. The discrete Fourier, wavelet and cosine transforms are applied for feature extraction. Statistical techniques such as principal component analysis and the Wilcoxon-Mann-Whitney test are used for optimal feature selection. Two different artificial neural network architectures are used for automatic classification...
August 24, 2016: Ultrasonics
Hansa Kundra, Jason C Park, J Jason McAnany
PURPOSE: To compare measurements of the full-field photopic negative response (PhNR), as well as intra-subject variation in the PhNR, using time and time-frequency domain analyses. METHODS: Full-field ERGs were recorded from 20 normally sighted subjects (aged 24-65 years) elicited by a long-wavelength pulse (3 cd s m(-2)) presented against a short-wavelength adapting field (12.5 cd m(-2)). Three to 10 waveforms were obtained from each subject, and each waveform was analyzed using standard time domain analyses of the PhNR, as well as a discrete wavelet transform (DWT) to extract time-frequency components that correspond to the PhNR...
October 2016: Documenta Ophthalmologica. Advances in Ophthalmology
Saleha Khatun, Ruhi Mahajan, Bashir I Morshed
Electroencephalogram (EEG) is a technique for recording the asynchronous activation of neuronal firing inside the brain with non-invasive scalp electrodes. Artifacts, such as eye blink activities, can corrupt these neuronal signals. While ocular artifact (OA) removal is well investigated for multiple channel EEG systems, in alignment with the recent momentum toward minimalistic EEG systems for use in natural environments, we investigate unsupervised and effective removal of OA from single-channel streaming raw EEG data...
2016: IEEE Journal of Translational Engineering in Health and Medicine
Aodhán Hickey, Brook Galna, John C Mathers, Lynn Rochester, Alan Godfrey
BACKGROUND: Multi-resolution analyses involving wavelets are commonly applied to data derived from accelerometer-based wearable technologies (wearables) to identify and quantify postural transitions (PTs). Previous studies fail to provide rationale to inform their choice of wavelet and scale approximation when utilising discrete wavelet transforms. This study examines varying combinations of those parameters to identify best practice recommendations for detecting and quantifying sit-to-stand (SiSt) and stand-to-sit (StSi) PTs...
September 2016: Gait & Posture
Vahid Faghih Dinevari, Ghader Karimian Khosroshahi, Mina Zolfy Lighvan
Wireless capsule endoscopy (WCE) is a new noninvasive instrument which allows direct observation of the gastrointestinal tract to diagnose its relative diseases. Because of the large number of images obtained from the capsule endoscopy per patient, doctors need too much time to investigate all of them. So, it would be worthwhile to design a system for detecting diseases automatically. In this paper, a new method is presented for automatic detection of tumors in the WCE images. This method will utilize the advantages of the discrete wavelet transform (DWT) and singular value decomposition (SVD) algorithms to extract features from different color channels of the WCE images...
2016: Applied Bionics and Biomechanics
Yuning Yang, Andrew Mason
Hardware-efficient feature extraction is an important step for real-time and on-chip spike sorting. Based on an analysis of spike energy spectrum, a new feature set is developed using the positive and negative spike peaks in low and high frequency bands. A separability metric that evaluates the informativeness and noise sensitivity of features is introduced to optimize the cutoff frequency of each band. Haar-based discrete wavelet transform was chosen to implement memory- and hardware-efficient filters for extracting frequency band separability features...
July 12, 2016: IEEE Transactions on Neural Systems and Rehabilitation Engineering
Bahareh Langari, Saeed Vaseghi, Ales Prochazka, Babak Vaziri
This paper presents improvements in image gap restoration through incorporation of edge-based directional interpolation within multi-scale pyramid transforms. Two types of image edges are reconstructed; (a) the local edges or textures, inferred from the gradients of the neighbouring pixels and (b) the global edges between image objects or segments, inferred using Canny detector. Through a process of pyramid transformation and down-sampling, the image is progressively transformed into a series of reduced size layers until at the pyramid apex the gap size is one sample...
July 13, 2016: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
Anna Pastuszczak, Bartłomiej Szczygieł, Michał Mikołajczyk, Rafał Kotyński
Minimal mutual coherence of discrete noiselets and Haar wavelets makes this pair of bases an essential choice for the measurement and compression matrices in compressed-sensing-based single-pixel detectors. In this paper, we propose an efficient way of using complex-valued and nonbinary noiselet functions for object sampling in single-pixel cameras with binary spatial light modulators and incoherent illumination. The proposed method allows us to determine m complex noiselet coefficients from m+1 binary sampling measurements...
July 1, 2016: Applied Optics
Souhir Chabchoub, Sofienne Mansouri, Ridha Ben Salah
Impedance cardiography (ICG) is a non-invasive technique for diagnosing cardiovascular diseases. In the acquisition procedure, the ICG signal is often affected by several kinds of noise which distort the determination of the hemodynamic parameters. Therefore, doctors cannot recognize ICG waveform correctly and the diagnosis of cardiovascular diseases became inaccurate. The aim of this work is to choose the most suitable method for denoising the ICG signal. Indeed, different wavelet families are used to denoise the ICG signal...
September 2016: Australasian Physical & Engineering Sciences in Medicine
Xiaoyuan Li, Qiwei Li, Li Shi, Liucheng Jiao
The response properties of individual neurons in the primary visual cortex (V1) are among the most thoroughly described in the mammalian central nervous system, but they reveal less about higher-order processes like visual perception. Neural activity is highly nonlinear and non-stationary over time, greatly complicating the relationships among the spatiotemporal characteristics of visual stimuli, local field potential (LFP) signal components, and the underlying neuronal activity patterns. We applied discrete wavelet transformation to detect new features of the LFP that may better describe the association between visual input and neural ensemble activity...
2016: Open Biomedical Engineering Journal
Yi Zhang, Keigo Hirakawa
Low light photography suffers from blur and noise. In this paper, we propose a novel method to recover a dense estimate of spatially varying blur kernel as well as a denoised and deblurred image from a single noisy and object motion blurred image. A proposed method takes the advantage of the sparse representation of double discrete wavelet transform-a generative model of image blur that simplifies the wavelet analysis of a blurred image-and the Bayesian perspective of modeling the prior distribution of the latent sharp wavelet coefficient and the likelihood function that makes the noise handling explicit...
September 2016: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
Yibo Zhang, Yichen Wu, Yun Zhang, Aydogan Ozcan
Lens-free holographic microscopy can achieve wide-field imaging in a cost-effective and field-portable setup, making it a promising technique for point-of-care and telepathology applications. However, due to relatively narrow-band sources used in holographic microscopy, conventional colorization methods that use images reconstructed at discrete wavelengths, corresponding to e.g., red (R), green (G) and blue (B) channels, are subject to color artifacts. Furthermore, these existing RGB colorization methods do not match the chromatic perception of human vision...
2016: Scientific Reports
Salim Lahmiri
Hybridisation of the bi-dimensional empirical mode decomposition (BEMD) with denoising techniques has been proposed in the literature as an effective approach for image denoising. In this Letter, the Student's probability density function is introduced in the computation of the mean envelope of the data during the BEMD sifting process to make it robust to values that are far from the mean. The resulting BEMD is denoted tBEMD. In order to show the effectiveness of the tBEMD, several image denoising techniques in tBEMD domain are employed; namely, fourth order partial differential equation (PDE), linear complex diffusion process (LCDP), non-linear complex diffusion process (NLCDP), and the discrete wavelet transform (DWT)...
March 2016: Healthcare Technology Letters
Fadoua Bahja, Joseph Di Martino, Elhassan Ibn Elhaj, Driss Aboutajdine
A new wavelet-based method is presented in this work for estimating and tracking the pitch period. The main idea of the proposed new approach consists in extracting the cepstrum excitation signal and applying on it a wavelet transform whose resulting approximation coefficients are smoothed, for a better pitch determination. Although the principle of the algorithms proposed has already been considered previously, the novelty of our methods relies in the use of powerful wavelet transforms well adapted to pitch determination...
2016: SpringerPlus
Zsuzsanna Püspöki, Martin Storath, Daniel Sage, Michael Unser
We give a methodology-oriented perspective on directional image analysis and rotation-invariant processing. We review the state of the art in the field and make connections with recent mathematical developments in functional analysis and wavelet theory. We unify our perspective within a common framework using operators. The intent is to provide image-processing methods that can be deployed in algorithms that analyze biomedical images with improved rotation invariance and high directional sensitivity. We start our survey with classical methods such as directional-gradient and the structure tensor...
2016: Advances in Anatomy, Embryology, and Cell Biology
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