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Discrete wavelet transform

Yuanyao Li, Jinsong Huang, Shui-Hua Jiang, Faming Huang, Zhilu Chang
It is important to monitor the displacement time series and to explore the failure mechanism of reservoir landslide for early warning. Traditionally, it is a challenge to monitor the landslide displacements real-timely and automatically. Globe Position System (GPS) is considered as the best real-time monitoring technology, however, the accuracies of the landslide displacements monitored by GPS are not assessed effectively. A web-based GPS system is developed to monitor the landslide displacements real-timely and automatically in this study...
December 7, 2017: Scientific Reports
Adam Czaplicki, Wiesława Kuniszyk-Jóźkowiak, Janusz Jaszczuk, Marta Jarocka, Jacek Walawski
PURPOSE: The purpose of the current study was to assess the effectiveness of rehabilitation in patients after anterior cruciate ligament reconstruction (ACLR) using a wavelet analysis of the torque-time curve patterns of the extensors of the affected knee. The analysis aimed at the quantitative evaluation of irregularities in these torque-time patterns. METHODS: The study involved a group of 22 men who had had ACL reconstruction. The torque-time characteristics were recorded 3, 6 and 12 months after the surgery by an isokinetic dynamometer...
2017: Acta of Bioengineering and Biomechanics
Saiby Madan, Kajri Srivastava, A Sharmila, P Mahalakshmi
Epileptic seizures are manifestations of epilepsy. Careful analysis of EEG records can provide valuable insight and improved understanding of the mechanism causing epileptic disorders. The detection of epileptic form discharges in EEG is an important component in the diagnosis of epilepsy. As EEG signals are non-stationary, the conventional frequency and time domain analysis does not provide better accuracy. So, in this work an attempt has been made to provide an overview of the determination of epilepsy by implementation of Hurst exponent (HE)-based discrete wavelet transform techniques for feature extraction from EEG data sets obtained during ictal and pre ictal stages of affected person and finally classifying EEG signals using SVM and KNN Classifiers...
November 30, 2017: Journal of Medical Engineering & Technology
K Manjula, K Vijayarekha, B Venkatraman
Welding is an integral part of component fabrication in industry. Even though the science and art of welding are more than 100 years old, defects continue to occur during welding. Codes of practice require that the welds be tested and evaluated. Conventionally ultrasonic testing has been widely applied in industry for the detection and evaluation of the flaws/defects in the weldments. With advances in sensor and signal analysis technologies, the last two decades have seen extensive developments in the field of ultrasonic testing...
November 6, 2017: Ultrasonics
Aleksandra Michalska, Agnieszka Martyna, Grzegorz Zadora
The main aim of this study was to verify whether selected analytical parameters may affect solving the comparison problem of Raman spectra with the use of the likelihood ratio (LR) approach. Firstly the LR methodologies developed for Raman spectra of blue automotive paints obtained with the use of 785nm laser source (results published by the authors previously) were implemented for good quality spectra recorded for these paints with the use of 514.5nm laser source. For LR models construction two types of variables were used i...
November 3, 2017: Forensic Science International
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
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
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
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
Iben H Bruun, Semira M S Hissabu, Erik S Poulsen, Sadasivan Puthusserypady
Early detection of Atrial Fibrillation (AF) is crucial in order to prevent acute and chronic cardiac rhythm disorders. In this study, a novel method for robust automatic AF detection (AAFD) is proposed by combining atrial activity (AA) and heart rate variability (HRV), which could potentially be used as a screening tool for patients suspected to have AF. The method includes an automatic peak detection prior to the feature extraction, as well as a noise cancellation technique followed by a bagged tree classification...
July 2017: Conference Proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society
Fernando Vaquerizo-Villar, Daniel Alvarez, Gonzalo C Gutierrez-Tobal, Veronica Barroso-Garcia, Leila Kheirandish-Gozal, Andrea Crespo, Felix Del Campo, David Gozal, Roberto Hornero
Sleep apnea hypopnea syndrome (SAHS) is a highly prevalent respiratory disorder that may cause many negative consequences for the health and development of children. The gold standard for diagnosis is the overnight polysomnography (PSG), which is a high cost, complex, intrusive, and time-demanding technique. To improve the early detection of pediatric SAHS, we propose an automated analysis of the SpO2 signal from nocturnal oximetry. A database composed of 298 SpO2 recordings from children ranging from 0 to 13 years old was used for this purpose...
July 2017: Conference Proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society
P Kunumpol, W Umpaipant, N Kanchanaranya, T Charoenpong, S Vongkittirux, T Kupakanjana, C Tantibundhit
This work proposed an automated screening system for Age-related Macular Degeneration (AMD), and distinguishing between wet or dry types of AMD using fundus images to assist ophthalmologists in eye disease screening and management. The algorithm employs contrast-limited adaptive histogram equalization (CLAHE) in image enhancement. Subsequently, discrete wavelet transform (DWT) and locality sensitivity discrimination analysis (LSDA) were used to extract features for a neural network model to classify the results...
July 2017: Conference Proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society
Aymen Ayaz, Muhammad Zubair Ahmad, Khawar Khurshid, Awais M Kamboh
This paper presents a novel algorithm for classification of patients with Alzheimer's disease (AD) and Mild Cognitive Impairment (MCI) from the healthy controls (HC) using structural MRI. Feature extraction is based on discrete 3D wavelet transform followed by PCA for transforming the feature space into linearly uncorrelated variables. Linear SVM is used for classification purposes with clinical dementia rating used as the target vector. Proposed methodology is fully automated and independent of the annotation of region of interest...
July 2017: Conference Proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society
Kairui Guo, Henry Candra, Hairong Yu, Huiqi Li, Hung T Nguyen, Steven W Su
Emotion classification is one of the state-of-the-art topics in biomedical signal research, and yet a significant portion remains unknown. This paper offers a novel approach with a combined classifier to recognise human emotion states based on electroencephalogram (EEG) signal. The objective is to achieve high accuracy using the combined classifier designed, which categorises the extracted features calculated from time domain features and Discrete Wavelet Transform (DWT). Two innovative designs are involved in this project: a novel variable is established as a new feature and a combined SVM and HMM classifier is developed...
July 2017: Conference Proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society
Said A Hassan, Sherif A Abdel-Gawad
Two signal processing methods, namely, Continuous Wavelet Transform (CWT) and the second was Discrete Fourier Transform (DFT) were introduced as alternatives to the classical Derivative Spectrophotometry (DS) in analysis of binary mixtures. To show the advantages of these methods, a comparative study was performed on a binary mixture of Naltrexone (NTX) and Bupropion (BUP). The methods were compared by analyzing laboratory prepared mixtures of the two drugs. By comparing performance of the three methods, it was proved that CWT and DFT methods are more efficient and advantageous in analysis of mixtures with overlapped spectra than DS...
August 14, 2017: Spectrochimica Acta. Part A, Molecular and Biomolecular Spectroscopy
Ping Zhou, Tongjing Zhu, Chunliu He, Zhiyong Li
Intravascular optical coherence tomography (IVOCT) has been successfully utilized for in vivo diagnostics of coronary plaques. However, classification of atherosclerotic tissues is mainly performed manually by experienced experts, which is time-consuming and subjective. To overcome these limitations, an automatic method of segmentation and classification of IVOCT images is developed in this paper. The method is capable of detecting the plaque contour between the fibrous tissues and other components. Subsequently, the method classifies the tissues based on their texture features described by Fourier transform and discrete wavelet transform...
July 1, 2017: Journal of the Optical Society of America. A, Optics, Image Science, and Vision
Tina P George, Tessamma Thomas
Long noncoding RNAs (lncRNAs) which were initially dismissed as "transcriptional noise" have become a vital area of study after their roles in biological regulation were discovered. Long noncoding RNAs have been implicated in various developmental processes and diseases. Here, we perform exon mapping of human lncRNA sequences (taken from National Center for Biotechnology Information GenBank) using digital filters. Antinotch digital filters are used to map out the exons of the lncRNA sequences analyzed. The period 3 property which is an established indicator for locating exons in genes is used here...
2017: Genomics Insights
Fang Li, Anxiang Lu, Jihua Wang
A modeling method based on discrete wavelet transform (DWT) was introduced to analyze the concentration of chromium, copper, zinc, arsenic and lead in soil with a portable X-ray fluorescence (XRF) spectrometer. A total of 111 soil samples were collected and observed. Denoising and baseline correction were performed on each spectrum before modeling. The optimum conditions for pre-processing were denoising with Coiflet 3 on the 3rd level and baseline correction with Coiflet 3 on the 9th level. Calibration curves were established for the five heavy metals (HMs)...
September 30, 2017: International Journal of Environmental Research and Public Health
Yubo Wang, Yijie Ding, Fei Guo, Leyi Wei, Jijun Tang
Since the importance of DNA-binding proteins in multiple biomolecular functions has been recognized, an increasing number of researchers are attempting to identify DNA-binding proteins. In recent years, the machine learning methods have become more and more compelling in the case of protein sequence data soaring, because of their favorable speed and accuracy. In this paper, we extract three features from the protein sequence, namely NMBAC (Normalized Moreau-Broto Autocorrelation), PSSM-DWT (Position-specific scoring matrix-Discrete Wavelet Transform), and PSSM-DCT (Position-specific scoring matrix-Discrete Cosine Transform)...
2017: PloS One
Roghayyeh Arvanaghi, Sabalan Daneshvar, Hadi Seyedarabi, Atefeh Goshvarpour
BACKGROUND AND OBJECTIVE: Each of Electrocardiogram (ECG) and Atrial Blood Pressure (ABP) signals contain information of cardiac status. This information can be used for diagnosis and monitoring of diseases. The majority of previously proposed methods rely only on ECG signal to classify heart rhythms. In this paper, ECG and ABP were used to classify five different types of heart rhythms. To this end, two mentioned signals (ECG and ABP) have been fused. METHODS: These physiological signals have been used from MINIC physioNet database...
November 2017: Computer Methods and Programs in Biomedicine
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