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Wavelet transform

Mingshun Jiang, Yaozhang Sai, Xiangyi Geng, Qingmei Sui, Xiaohui Liu, Lei Jia
We proposed and studied an impact detection system based on a fiber Bragg grating (FBG) sensor array and multiple signal classification (MUSIC) algorithm to determine the location and the number of low velocity impacts on a carbon fiber-reinforced polymer (CFRP) plate. A FBG linear array, consisting of seven FBG sensors, was used for detecting the ultrasonic signals from impacts. The edge-filter method was employed for signal demodulation. Shannon wavelet transform was used to extract narrow band signals from the impacts...
October 24, 2016: Sensors
Deepak Ranjan Nayak, Ratnakar Dash, Banshidhar Majhi
This paper presents an automatic classification system for segregating pathological brain from normal brains in magnetic resonance imaging scanning. The proposed system employs contrast limited adaptive histogram equalization scheme to enhance the diseased region in brain MR images. Two-dimensional stationary wavelet transform is harnessed to extract features from the preprocessed images. The feature vector is constructed using the energy and entropy values, computed from the level-2 SWT coefficients. Then, the relevant and uncorrelated features are selected using symmetric uncertainty ranking filter...
October 24, 2016: CNS & Neurological Disorders Drug Targets
Peyvand Ghaderyan, Ataollah Abbasi
Automatic workload estimation has received much attention because of its application in error prevention, diagnosis, and treatment of neural system impairment. The development of a simple but reliable method using minimum number of psychophysiological signals is a challenge in automatic workload estimation. To address this challenge, this paper presented three different decomposition techniques (Fourier, cepstrum, and wavelet transforms) to analyze electrodermal activity (EDA). The efficiency of various statistical and entropic features was investigated and compared...
October 22, 2016: International Journal of Psychophysiology
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
Min Wu, Ting Wan, Xiongbo Wan, Yuxiao Du, Jinhua She
This letter describes the improvement of two methods of detecting high-frequency oscillations (HFO) and their use to localize epileptic seizure onset zones (SOZs). The wavelet transform (WT) method was improved by combining the complex Morlet WT with Shannon entropy to enhance the temporal-frequency resolution during HFO detection. And the matching pursuit (MP) method was improved by combining it with an adaptive genetic algorithm to improve the speed and accuracy of the calculations for HFO detection. The HFOs detected by these two methods were used to localize SOZs in five patients...
October 20, 2016: Neural Computation
Kaori Tamura, Takaaki Mizuba, Tsuyoshi Okamoto, Mayumi Matsufuji, Sachio Takashima, Keiji Iramina
INTRODUCTION: The level of residual cognitive function in patients with early brain injury is a key factor limiting rehabilitation and the quality of life. Although understanding residual function is necessary for appropriate rehabilitation, the extent of its effects on cognitive improvement remains unknown. The present study evaluated cognitive function in patients with severe motor and intellectual disabilities after early brain injuries due to cerebral hemorrhage (CH) or periventricular leukomalacia (PVL)...
October 18, 2016: Journal of Clinical Neurophysiology: Official Publication of the American Electroencephalographic Society
Hamid Abbasi, Laura Bennet, Alistair J Gunn, Charles P Unsworth
Currently, there are no developed methods to detect sharp wave transients that exist in the latent phase after hypoxia-ischemia (HI) in the electroencephalogram (EEG) in order to determine if these micro-scale transients are potential biomarkers of HI. A major issue with sharp waves in the HI-EEG is that they possess a large variability in their sharp wave profile making it difficult to build a compact 'footprint of uncertainty' (FOU) required for ideal performance of a Type-2 fuzzy logic system (FLS) classifier...
August 18, 2016: International Journal of Neural Systems
Yao Tian, Kenneth S Burch
Raman spectroscopy is a powerful technique, widely used in both academia and industry. In part, the technique's extensive use stems from its ability to uniquely identify and image various material parameters: composition, strain, temperature, lattice/excitation symmetry, and magnetism in bulk, nano, solid, and organic materials. However, in nanomaterials and samples with low thermal conductivity, these measurements require long acquisition times. On the other hand, charge-coupled device (CCD) detectors used in Raman microscopes are vulnerable to cosmic rays...
October 17, 2016: Applied Spectroscopy
Zewei Wang, Qing Yang, Li Min Chen
The goals of this study are to characterize the temporal dynamics of inter-regional connectivity of the brain in chronic headache (CH) patients versus their age/gender matched controls (CONCH, n=28 pairs), and to determine whether dynamic measures reveal additional features to static functional connectivity and correlate with psychometric scores. Cortical thickness and inter-regional resting state fMRI connectivity were quantified and compared between CH and CONCH groups. Six cortical regions of interest (ROI) pairs that exhibited correlated cortical thickness and static functional connectivity abnormalities were selected for temporal dynamic analysis...
October 14, 2016: Magnetic Resonance Imaging
Akshansh Gupta, Dhirendra Kumar
A brain computer interface (BCI) is a communication system by which a person can send messages or requests for basic necessities without using peripheral nerves and muscles. Response to mental task-based BCI is one of the privileged areas of investigation. Electroencephalography (EEG) signals are used to represent the brain activities in the BCI domain. For any mental task classification model, the performance of the learning model depends on the extraction of features from EEG signal. In literature, wavelet transform and empirical mode decomposition are two popular feature extraction methods used to analyze a signal having non-linear and non-stationary property...
September 3, 2016: Brain Informatics
Seda Guzel Aydin, Turgay Kaya, Hasan Guler
This paper illustrates the wavelet-based feature extraction for emotion assessment using electroencephalogram (EEG) signal through graphical coding design. Two-dimensional (valence-arousal) emotion model was studied. Different emotions (happy, joy, melancholy, and disgust) were studied for assessment. These emotions were stimulated by video clips. EEG signals obtained from four subjects were decomposed into five frequency bands (gamma, beta, alpha, theta, and delta) using "db5" wavelet function. Relative features were calculated to obtain further information...
June 2016: Brain Informatics
Mahsa Akhbari, Mohammad B Shamsollahi, Omid Sayadi, Antonis A Armoundas, Christian Jutten
In this paper, we propose a novel method for extracting fiducial points (FPs) of electrocardiogram (ECG) signals. We propose the use of multi hidden Markov model (MultiHMM) as opposed to the traditional use of Classic HMM. In the MultiHMM method, each segment of an ECG beat is represented by a separate ergodic continuous density HMM. Each HMM has different state number and is trained separately. In the test step, the log-likelihood of two consecutive HMMs is compared and a path is estimated, which shows the correspondence of each part of the ECG signal to the HMM with the maximum log-likelihood...
September 28, 2016: Computers in Biology and Medicine
Andrzej Mysiak, MaƂgorzata Kobusiak-Prokopowicz, Konrad Kaaz, Kamila Jarczewska, Wojciech Glabisz
BACKGROUND: The theory of chaos proves a deterministic mechanism of induction of multiple complex processes previously thought to be random in nature. This research explains how these complex processes develop. The aim of the study was to test the hypothesis of the chaotic nature of myocardial electrical events during ventricular tachycardia (VT) and ventricular fibrillation (VF). METHODS: Original hardware and software was developed for digitalization of on-line electrocardiography (ECG) data, with the functions of automatic and manual identification as well as categorization of specific ventricular arrhythmias...
October 13, 2016: Cardiology Journal
Ferhat Canbay, Vecdi Emre Levent, Gorkem Serbes, H Fatih Ugurdag, Sezer Goren, Nizamettin Aydin
The authors aimed to develop an application for producing different architectures to implement dual tree complex wavelet transform (DTCWT) having near shift-invariance property. To obtain a low-cost and portable solution for implementing the DTCWT in multi-channel real-time applications, various embedded-system approaches are realised. For comparison, the DTCWT was implemented in C language on a personal computer and on a PIC microcontroller. However, in the former approach portability and in the latter desired speed performance properties cannot be achieved...
September 2016: Healthcare Technology Letters
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
Li Zhao, Weiying Dai, Salil Soman, David Hackney, Eric Wong, Philip Robson, David Alsop
Functional imaging provides hemodynamic and metabolic information and is increasingly being incorporated into clinical diagnostic and research studies. Typically functional images have reduced signal-to-noise ratio and spatial resolution compared to other non-functional cross sectional images obtained as part of a routine clinical protocol. We hypothesized that enhancing visualization and interpretation of functional images with anatomic information could provide preferable quality and superior diagnostic value...
October 6, 2016: IEEE Transactions on Medical Imaging
Wolfgang G Kunz, Wieland H Sommer, Lukas Havla, Franziska Dorn, Felix G Meinel, Olaf Dietrich, Grete Buchholz, Birgit Ertl-Wagner, Kolja M Thierfelder
OBJECTIVES: To determine the detection rate of intracranial vessel occlusions using CT perfusion-based wavelet-transformed angiography (waveletCTA) in acute ischemic stroke patients, in whom single-phase CTA (spCTA) failed to detect an occlusion. METHODS: Subjects were selected from a cohort of 791 consecutive patients who underwent multiparametric CT including whole-brain CT perfusion. Inclusion criteria were (1) significant cerebral blood flow (CBF) deficit, (2) no evidence of vessel occlusion on spCTA and (3) follow-up-confirmed acute ischemic infarction...
October 8, 2016: European Radiology
Michiko Nishiyama, Masako Sonobe, Kazuhiro Watanabe
In this paper, we present a pulse pressure waveform sensor that does not constrain a wearer's daily activity; the sensor uses hetero-core fiber optics. Hetero-core fiber sensors have been found to be sensitive to moderate bending. To detect minute pulse pressure changes from the radial artery at the wrist, we devised a fiber sensor arrangement using three-point bending supports. We analyzed and evaluated the measurement validity using wavelet transformation, which is well-suited for biological signal processing...
September 1, 2016: Biomedical Optics Express
Mehri Owjimehr, Habibollah Danyali, Mohammad Sadegh Helfroush, Alireza Shakibafard
Fatty liver disease is progressive and may not cause any symptoms at early stages. This disease is potentially fatal and can cause liver cancer in severe stages. Therefore, diagnosing and staging fatty liver disease in early stages is necessary. In this paper, a novel method is presented to classify normal and fatty liver, as well as discriminate three stages of fatty liver in ultrasound images. This study is performed with 129 subjects including 28 normal, 47 steatosis, 42 fibrosis, and 12 cirrhosis images...
June 8, 2016: Ultrasonic Imaging
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