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Automated technique for EEG signal processing to detect seizure with optimized Variable Gaussian filter and Fuzzy RBFELM classifier

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BIOMEDICAL SIGNAL PROCESSING AND CONTROL 2022年第0期74卷 103450-103450页

作者： Harishvijey, A. Raja, J. Benadict Anna Univ Chennai Tamilnadu India PSNA Coll Engn & Technol Dindigul Tamilnadu India

Epileptic seizure in patients is detected from EEG signals with the use of automatic signal classification tech-niques. The accurate detection of epilepsy is essential to reduce the risk of seizure related complications. However the available automatic signal detection techniques give poor sensitivity and accuracy. In this work, an automatic signal classification method for detecting seizure from EEG signal is presented for obtaining good classification results. The proposed work improves the performance of detection using Variable Gaussian filter (VGF) with social spider algorithm (SSA) (SSA-VGF), Empirical Wavelet Transform (EWT) feature extraction method, K-Principal component analysis (K-PCA) based feature reduction and Fuzzy logic embedded RBF kernel based ELM algorithm (FRBFELM). The SSA-VGF method is used for removing noise artifacts from the given EEG signals. EWT is employed for feature extraction and the size of extracted features is reduced using K-PCA method. Finally the signals are classified as normal signals and epileptic signals using FRBFELM classifier. The perfor-mance of the proposed method is evaluated by measuring the metrics;PSNR, accuracy, sensitivity, and speci-ficity. The value of performance metrics obtained for the proposed work is 98.48%, 98.44% and 98.51%.

关键词： EEG signals Signal classification Noise removal filter algorithm Feature extraction Feature reduction Optimization algorithm

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filter-processing-assisted Fourier transform Raman spectroscopy for forensic discrimination lubricant machinery oils

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MICROCHEMICAL JOURNAL 2024年 204卷

作者： Zhu, Mi Wu, Ting Chen, Yaoqing Zhu, Hongjian Hunan Police Acad Sch Forens Sci Changsha 410138 Peoples R China Hunan Police Acad Inst Food & Environm & Drug Monitoring & Testing Changsha 410138 Peoples R China Changsha Publ Secur Bur Yuelu Branch Changsha 410207 Peoples R China

The primary objective of this study was to investigate the potential value of filtering algorithms in enhancing spectral model performance. 290 mechanical lubricants oils samples from various applications underwent measurement using Fourier transform Raman spectroscopy (FT -Raman). Subsequently, three discrimination models were developed employing C5.0, Bayes discriminant analysis, and support vector machine algorithms. The emphasis was on evaluating the efficacy of four filter algorithms -Fast Fourier Transform (FFT), Discrete Wavelet Transform (DWT), Finite Impulse Response (FIR), and Hilbert Transform (HT) -for enhancing model performance. Among these, the FFT filter-SVM model, DWT filter-SVM model, and FIR (low-pass or band -stop filter)-SVM model emerged as optimal choices for identification. These models demonstrated outstanding performance, achieving 100 % accuracy across all 290 samples. Modelling using FT -Raman and chemometrics offered a simpler, more robust, and valuable approach for identifying mechanical lubrication oils across diverse applications, exploiting the potential of filtering algorithms in forensic spectroscopy.

关键词： Fourier transform Raman spectroscopy Mechanical lubricants oils Chemometrics filter algorithm Forensic science

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A wavelet based algorithm for DTM extraction from airborne laser scanning data

A wavelet based algorithm for DTM extraction from airborne l...

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15th International Conference on Geoinformatics

作者： Xu, Liang Yang, Yan Tian, Qingjiu State Ocean Adm China Marine Surveillance Airborne Detachment E China Sea Shanghai 200137 Peoples R China Nanjing Univ Int Inst Earth Syst Sci Nanjing 210093 Peoples R China

ISBN: (纸本)9780819469120

The automatic extraction of Digital Terrain Model (DTM) from point clouds acquired by airborne laser scanning (ALS) equipment remains a problem in ALS data filtering nowadays. Many filter algorithms have been developed to remove object points and outliers, and to extract DTM automatically. However, it is difficult to filter in areas where few points have identical morphological or geological features that can present the bare earth. Especially in sloped terrain covered by dense vegetation, points representing bare earth are often identified as noisy data below ground. To extract terrain surface in these areas, a new algorithm is proposed. First, the point clouds are cut into profiles based on a scan line segmentation algorithm. In each profile, a ID filtering procedure is determined from the wavelet theory, which is superior in detecting high frequency discontinuities. After combining profiles from different directions, an interpolated and data representing DTM is generated. In order to evaluate the performance of this new approach, we applied it to the data set used in the ISPRS filter test in 2003. 2 samples containing mostly vegetation on slopes have been processed by the proposed algorithm. It can be seen that it filtered most of the objects like vegetation and buildings in sloped area, and smoothed the hilly mountain to be more close to its real terrain surface.

关键词： ALS filter algorithm DTM wavelet

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BREAST CANCER IMAGE CLASSIFICATION ON WSI WITH SPATIAL CORRELATIONS 44

BREAST CANCER IMAGE CLASSIFICATION ON WSI WITH SPATIAL CORRE...

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44th IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

作者： Ye, Jiandong Luo, Yihao Zhu, Chuang Liu, Fang Zhang, Yue Beijing Univ Posts & Telecommun Beijing Peoples R China

ISBN: (纸本)9781479981311

As common cancer, breast cancer kills thousands of women every year. It's significant to provide doctors computer-aided diagnosis (CAD) to ease their workload as well as improve detection quality. Patch-level CNNs are usually used to classify the breast tissue slice, and the CNNs classify each patch independently ignoring the spatial correlations, resulting in wrong isolated label map. However, the probability distribution of cancer type is related to their adjacent patches. In this paper, we propose a framework integrating CNN and filter algorithm aimed at extracting spatial information and improving the performance of the classification. The network was trained on a breast cancer dataset provided by ICIAR18. For 4-class classification, compared to CNN methods without using spatial correlations, the proposed method achieved about 10% improvement on accuracy over the validation dataset and get smoother probability maps. Our experiments also show that larger kernel size gets better performance.

关键词： CNN filter algorithm Breast Cancer Detection Spatial Correlations

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Application of the EKF algorithm in the DTMB Positioning System 5th

Application of the EKF Algorithm in the DTMB Positioning Sys...

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5th International Conference on Communications, Signal Processing, and Systems (CSPS)

作者： Fu, Chengbiao Tian, Zengshan Tian, Anhong Qujing Normal Coll Dept Informat Engn Qujing 655000 Peoples R China Chongqing Univ Posts & Telecommun Coll Commun & Informat Engn Chongqing 400065 Peoples R China

ISBN: (纸本)9789811032295;9789811032288

Because single GPS technology cannot realize localization in a dense urban environment, and in view of Chinese digital television terrestrial broadcasting (DTMB) signals using with fixed transmitters, wide coverages, high positioning accuracy, and low cost, this paper suggests a location method based on DTMB. Because the observation model of the DTMB's location system is gauss non-linear, this article contrasts and analyzes the tracking effect of two kinds of environment: a line-of-sight (LOS) environment and a non-line-of-sight (NLOS) environment. Simulation results show that: the tracking trajectory is in line with the true trace, due to the interference of the NLOS environment;the error of NLOS is bigger than LOS;and the tracking accuracy of NLOS is better than LOS. This all proves the feasibility and superiority of a location system based on DTMB.

关键词： DTMB positioning system filter algorithm Simulation result

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Ground extraction from airborne laser data based on wavelet analysis

Ground extraction from airborne laser data based on wavelet ...

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5th International Symposium on Multispectral Image Processing and Pattern Recognition

作者： Xu, Liang Yang, Yan Jiang, Bowen Li, Jia State Ocean Adm China Marine Surveillance Airborne Detachment E China Sea Shanghai 200137 Peoples R China Nanjing Univ Int Inst Earth Syst Sci Nanjing 210093 Jiangsu Peoples R China

ISBN: (纸本)9780819469519

With the advantages of high resolution and accuracy, airborne laser scanning data are widely used in topographic mapping. In order to generate a DTM, measurements from object features such as buildings, vehicles and vegetation have to be classified and removed. However, the automatic extraction of bare earth from point clouds acquired by airborne laser scanning equipment remains a problem in LIDAR data filtering nowadays. In this paper, a filter algorithm based on wavelet analysis is proposed. Relying on the capability of detecting discontinuities of continuous wavelet transform and the feature of multi-resolution analysis, the object points can be removed, while ground data are preserved. In order to evaluate the performance of this approach, we applied it to the data set used in the ISPRS filter test in 2003. 15 samples have been tested by the proposed approach. Results showed that it filtered most of the objects like vegetation and buildings, and extracted a well defined ground model.

关键词： aLS filter algorithm DTM wavelet

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Adaptive identification methods of nonlinear random electrical signal

Adaptive identification methods of nonlinear random electric...

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19th International Symposium on Applied Electromagnetics and Mechanics (ISEM)

作者： Liu, Jian Wang, Lihui Tian, Zhengqi State Grid Jiangsu Elect Power Co Res Inst Nanjing 211103 Peoples R China Southeast Univ Sch Instrument Sci & Engn Nanjing 210096 Peoples R China

The nonlinearity of the electric vehicle DC charging equipment and the complexity of the charging environment lead to the complex and changeable DC charging signal of the electric vehicle. It is urgent to study the distortion signal recognition method suitable for the electric vehicle DC charging. Focusing on the characteristics of fundamental and ripple in DC charging signal, the Kalman filter algorithm is used to establish the matrix model, and the state variable method is introduced into the filter algorithm to track the parameter state, and the amplitude and phase of the fundamental waves and each secondary ripple are identified;In view of the time-varying characteristics of the unsteady and abrupt signal in the DC charging signal, the stratification and threshold parameters of the wavelet transform are corrected, and a multi-resolution method is established to identify and separate the unsteady and abrupt signals. Identification method of DC charging distortion signal of electric vehicle based on Kalman/modified wavelet transform is used to decompose and identify the signal characteristics of the whole charging process. Experiment results demonstrate that the algorithm can accurately identify ripple, sudden change and unsteady wave during charging. It has higher signal to noise ratio and lower mean root mean square error.

关键词： DC charging nonlinearity distortion signal filter algorithm

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Research on Signal Processing of Micro/Nano Displacement Sensor for Piezoelectric Actuator Based on Forward Linear Prediction Wavelet Transform

Research on Signal Processing of Micro/Nano Displacement Sen...

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IEEE International Conference on Mechatronics and Automation

作者： Chen, Renbing Yu, Yong Song, Bo Ge, Yunjian Chinese Acad Sci Inst Intelligent Machines Hefei Anhui Peoples R China Univ Sci & Technol China Dept Automat Hefei Anhui Peoples R China Kagoshima Univ Dept Engn Mech Kagoshima 8900065 Japan

ISBN: (纸本)9781424426928

The micro/nano displacement measurement system described in this paper is a sensor that could measure micro/nanometer displacement level and has applications in a PZT actuator. As it is used in precision instruments, the sensor is required not to accommodate much space in order to save the room of measuring device, and what's more, it can detect the system's useful signal from the polluted weak signal. Therefore, in this paper we propose a novel signal processing algorithm based on forward linear prediction wavelet transform (DWT-FLP). This method not only uses the fast wavelet transform to achieve signal's wavelet decomposition and wavelet reconstruction, but also utilizes the FLP to obtain the wavelet coefficients, it can get a good de-noise effect. In the end, some correlative experiments have been made, the results illustrate the validity of the filtering algorithm and the sensor has a higher level of static output, real-time, linearity, low absolute deviation and resolution.

关键词： Micro/nano Displacement Sensor Weak Signal Detection De-noise DWT-FLP filter algorithm

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A Hybrid Feature Selection algorithm Applied to High-dimensional Imbalanced Small-sample Data Classification

A Hybrid Feature Selection Algorithm Applied to High-dimensi...

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Annual Summit and Conference of the Asia-Pacific-Signal-and-Information-Processing-Association (APSIPA ASC)

作者： Feng, Fang Lv, Qingquan Wang, Mingsong Yang, Xuhui Zhou, Qingguo Zhou, Rui Lanzhou Univ Sch Informat Sci & Engn Lanzhou Gansu Peoples R China Lanzhou Inst Technol Sch Elect & Informat Engn Lanzhou Gansu Peoples R China State Grid Gansu Elect Power Res Inst Lanzhou Gansu Peoples R China

ISBN: (纸本)9781728132488

With the rapid development of microarray technology and interdisciplinary science, it is possible for microarray technology to be used to predict diseases. Microarray technology has the advantages of high speed, high efficiency and reliability in disease prediction. However, microarray data are usually high-dimensional with small samples, additionally, the samples are often imbalanced, which brings a lot of difficulties to researchers. In view of the above problems, it is proposed in this paper a filter-Wrapper hybrid feature selection algorithm Union Information Gini Cost-sensitive Feature Selection General Vector Machine (UIG-CFGVM) to tackle the high-dimensional imbalanced small-sample problem. The improved hybrid algorithm is as follows: Firstly, the most common features are removed by the proposed hybrid filter algorithm UIG, which is obtained by Information Gain (Info)and Gini Index (Gini). Secondly, Cost-sensitive Feature selection General Vector Machine (CFGVM) is used as Wrapper method to further improve the performance of the algorithm. The experimental results show that the proposed algorithm UIG-CFGVM has better classification performance in seven biomedical high-dimensional imbalanced small-sample datasets compared with other similar algorithms.

关键词： filter algorithm Wrapper algorithm Feature selection High-dimensional Imbalanced Small-sample data

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Interpretation of beat-to-beat blood pressure values in the presence of ventilatory changes

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Journal of Clinical Monitoring 1985年第1期1卷 65-70页

作者： Ellis, David M. Hewlett-Packard Corporation Waltham 02254 MA 175 Wyman St United States

When blood pressure is measured invasively in the operating room or intensive care unit, it is reasonable to expect useful systolic, diastolic, and mean pressure values to be displayed at all times. If blood pressure is stable from beat to beat, display of values from any beat will suffice. However, the pressure often fluctuates from beat to beat. Display of values from every beat would yield a confusing jumble of numbers. This article describes an algorithm for selecting values that appear physiologically useful from blood pressure waveforms that vary with ventilation. Two classes of variation are discussed. The algorithm is optimized for the type of variation seen in pulmonary artery pressure waveforms. © 1985 Little, Brown and Company, Inc.

关键词： Blood pressure measurement filter algorithm Pulmonary artery pressure Ventilation

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