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IEEE International Conference on Microwaves, Communications, Antennas and Electronic Systems (COMCAS)

作者： Anishchenko, Lesya Moscow State Tech Univ Remote Sensing Lab 52d Baumanskaya Str Moscow 105005 Russia

ISBN: (纸本)9781479974733

In present work we discuss the usage of empirical mode decomposition algorithm for bioradar data processing. The algorithm of data processing is considered and the value of the threshold criteria is chosen according to the results of the experimental data processing. It is shown that preprocessing of raw bioradar data, which compensate the difference in amplitude of breathing and heartbeat signals, increases the effectiveness of empirical mode decomposition algorithm in bioradar data processing.

关键词： Bio-radar data processing empirical mode decomposition algorithm heartbeat respiration

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Embedded intelligent fetal heart rate monitoring system under empirical mode decomposition algorithm and its usage in pregnant women with pregnancy bacterial infection complications and nursing

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EXPERT SYSTEMS 2023年第4期40卷 e12825-e12825页

作者： Lian, Faying Liu, Hui Shao, Huan Han, Yanyan Chengyang Peoples Hosp Dept Obstet 600 Great Wall Rd Qingdao Peoples R China

this study was to discuss the application value of the embedded intelligent fetal heart rate monitoring system (EI system) based on empirical mode decomposition (EMD) algorithm in the monitoring of pregnant women with pregnancy bacterial infection complications, aiming to provide a reference basis for timely nursing of pregnancy bacterial infection complications. In this study, the blind source separation method was introduced to denoise the screened intrinsic modal function (IMF) components based on the EI system and the EMD algorithm. The signal to noise ratio (SNR) based on the EMD optimization algorithm was compared with that of other algorithms. Two hundred and sixty-eight pregnant women in the obstetric clinic were selected as the research objects and divided into an observation group (abnormal fetal heart rate) and a control group (normal fetal heart rate). The pregnant women in both groups accepted the conventional nursing methods, and the EI system was adopted to monitor the fetal heart rate. The probability of abnormal fetal heart monitoring of pregnant women in observation group and control group was analysed. At the same time, the probability of bacterial infection complications was analysed. It was found that the EI system based on EMD algorithm showed obviously higher SNR in contrast to other algorithms. The waveforms of fetal heart monitoring in the observation group and the control group were significantly different (chi(2) = 11.215, p < 0.05);microscopic examination of pregnant women secretion in the bacterial infection group revealed the presence of a large number of pathogenic bacteria;it was found that the infection rate of vaginosis was significantly different between the control group and the observation group (p < 0.01);there was a significant difference between the observation group and the control group in the complication of premature rupture of fetal membrane caused by bacteria (chi(2) = 11.2, p < 0.01). In conclusion, the EI system based

关键词： embedded intelligent fetal heart rate monitoring system empirical mode decomposition algorithm pregnancy bacterial infection pregnant women

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Enhanced Sensitivity of CO Photoacoustic Sensors Using empirical mode decomposition Denoising algorithm

IEEE PHOTONICS JOURNAL

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IEEE PHOTONICS JOURNAL 2022年第3期14卷 1页

作者： Li, Lei Tang, Liping Han, Fengtao Wang, Shenghui Gao, Yang Qiao, Yingying Shan, Chongxin Zhengzhou Univ Sch Phys & Microelect Zhengzhou 450001 Peoples R China

A CO sensor based on photoacoustic spectroscopy (PAS) with empirical mode decomposition (EMD) algorithm is investigated and demonstrated in this paper. In the PAS system, the complicated photo-thermal-acoustic conversion is a nonlinear and non-stationary process and contains various noise. In order to compensate the low signal-to-noise ratio (SNR), the EMD is introduced in the PAS system to deal with the photoacoustic signal. The experimental results show that a gain factor of similar to 3.0 on the SNR is achieved and the sensor has an excellent linear response to the gas concentration. The minimum detection level (MDL) for CO detection is reduced to 1.14 ppm with a 300 ms integrated time at room temperature and atmospheric pressure.

关键词： Absorption Signal to noise ratio Sensors Noise reduction Sensitivity Spectroscopy Filtering algorithms CO sensor empirical mode decomposition algorithm near-infrared light source photoacoustic spectroscopy

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Efficient methodology for electrocardiogram beat classification

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IET SIGNAL PROCESSING 2016年第7期10卷 825-832页

作者： Sharma, Piyush Ray, Kailash Chandra Indian Inst Technol Dept Elect Engn Patna 801103 Bihar India

Electrocardiogram (ECG) beat behaves as a non-linear and non-stationary signal. Since most of the existing data processing tools are poor alternatives for processing such signals, Hilbert-Huang transform (HHT) proves to be an efficient method as it deals with a time-varying frequency spectrum. In this study, a new and efficient methodology is proposed using HHT for feature selection which includes a set of essential features such as weighted mean frequency, Kolmogorov complexity and other statistical features (median, standard deviation, kurtosis, skewness and central moment) computed from the intrinsic mode functions extracted using the empirical mode decomposition (EMD) algorithm. Further, one-against-one multi-class support vector machine is employed for the classification of six generic ECG beats, namely: normal, left bundle branch block, right bundle branch block, premature ventricular contraction, paced beat and atrial premature beat. The classification process in this study yields better results than existing methodologies in terms of classification accuracy equal to 99.51% along with sensitivity, specificity and positive predictivity of 98.64, 99.77 and 98.17%, respectively.

关键词： electrocardiography Hilbert transforms support vector machines electrocardiogram beat classification ECG Hilbert-Huang transform time-varying frequency spectrum weighted mean frequency Kolmogorov complexity empirical mode decomposition algorithm support vector machine

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Arrhythmia Prediction on Optimal Features Obtained from the ECG as Images

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Computer Systems Science & Engineering 2023年第1期44卷 129-142页

作者： Fuad A.M.Al-Yarimi Department of Computer Science King Khalid UniversityAbhaKingdom of Saudi Arabia

A critical component of dealing with heart disease is real-time identifi-cation,which triggers rapid *** main challenge of real-time identification is illustrated here by the rare occurrence of cardiac *** contribu-tions to cardiac arrhythmia prediction using supervised learning approaches gen-erally involve the use of demographic features(electronic health records),signal features(electrocardiogram features as signals),and temporal *** the signal of the electrical activity of the heartbeat is very sensitive to differences between high and low heartbeats,it is possible to detect some of the irregularities in the early stages of *** paper describes the training of supervised learning using features obtained from electrocardiogram(ECG)image to correct the limitations of arrhythmia prediction by using demographic and electrocardio-graphic signal *** experimental study demonstrates the usefulness of the proposed Arrhythmia Prediction by Supervised Learning(APSL)method,whose features are obtained from the image formats of the electrocardiograms used as input.

关键词： ECG records electrocardiogram morphological features(MF) empirical mode decomposition algorithm HOS

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FPGA and ASIC realisation of EMD algorithm for real-time signal processing

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IET CIRCUITS DEVICES & SYSTEMS 2020年第6期14卷 741-749页

作者： Das, Kaushik Nath, Debanjali Pradhan, Sambhu Nath Natl Inst Technol Elect & Commun Engn Dept Agartala India

In this study, the authors have proposed both field-programmable gate array (FPGA) and application specific integrated circuit (ASIC) based realisation of the empirical mode decomposition (EMD) algorithm for the real-time signal processing. Here, a single module is used for the calculation of maxima and minima, and another single module is used for the calculation of upper and lower envelopes instead of using separate modules for each calculation. In this work, the traditional cubic spline interpolation has been replaced with sawtooth transform followed by a smoothing module called moving average. In this study firstly, Verilog-HDL code for the EMD is written using Xilinx Vivado and tested in the simulation phase, later dumped into Digilentinc Basys 3 FPGA board to do the hardware verification. For ASIC, the code is synthesised using Cadence Genus tool with the semi-conductor laboratory 180 nm cell library and the layout is made in the Cadence Innovus tool. The proposed EMD can work with a clock/sampling rate up to 25 MHz and has a layout area of 3.9 mm(2). For the reduction of power consumption of the overall system, clock gating has been used which helps to reduce the dynamic power of the modules, when they are not in use.

关键词： application specific integrated circuits hardware description languages interpolation field programmable gate arrays splines (mathematics) integrated circuit design signal processing Hilbert transforms logic design mathematics computing ASIC EMD algorithm real-time signal processing empirical mode decomposition algorithm traditional cubic spline interpolation smoothing module Verilog-HDL code Digilentinc Basys 3 FPGA board semiconductor laboratory cell library field-programmable gate array application specific integrated circuit sawtooth transform Xilinx Vivado simulation phase hardware verification Cadence Genus tool Cadence Innovus tool power consumption reduction clock gating dynamic power reduction moving average size 180 0 nm frequency 25 0 MHz

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Multiscale synchrony behaviors of paired financial time series by 3D multi-continuum percolation

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PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS 2018年 492卷 1481-1494页

作者： Wang, M. Wang, J. Wang, B. T. Beijing Jiaotong Univ Sch Sci Inst Financial Math & Financial Engn Beijing 100044 Peoples R China

Multiscale synchrony behaviors and nonlinear dynamics of paired financial time series are investigated, in an attempt to study the cross correlation relationships between two stock markets. A random stock price model is developed by a new system called three-dimensional (3D) multi-continuum percolation system, which is utilized to imitate the formation mechanism of price dynamics and explain the nonlinear behaviors found in financial time series. We assume that the price fluctuations are caused by the spread of investment information. The cluster of 3D multi-continuum percolation represents the cluster of investors who share the same investment attitude. In this paper, we focus on the paired return series, the paired volatility series, and the paired intrinsic mode functions which are decomposed by empirical mode decomposition. A new cross recurrence quantification analysis is put forward, combining with multiscale cross-sample entropy, to investigate the multiscale synchrony of these paired series from the proposed model. The corresponding research is also carried out for two China stock markets as comparison. (C) 2017 Elsevier B.V. All rights reserved.

关键词： Multiscale synchrony analysis 3D multi-continuum percolation system Random price series model empirical mode decomposition algorithm Cross recurrence quantification analysis Multiscale cross-sample entropy

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Fault Diagnosis in Shipboard Area Power Distribution Systems Based on PSO-SVM-CNN 3

Fault Diagnosis in Shipboard Area Power Distribution Systems...

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3rd International Conference on Electrical Engineering and Control Science, IC2ECS 2023

作者： Wang, Huimin Shi, Weifeng College of Logistics Engineering Shanghai Maritime University Shanghai China

ISBN: (纸本)9798350382426

Shipboard power systems are the core of maritime electrical automation, playing a crucial role in ensuring the normal operation of vessels. Efficient fault diagnosis facilitates rapid localization and judgment of faults, thereby safeguarding the safety of maritime operations. This research uses Simulink to simulate and analyze line faults in shipboard area distribution power systems. The simulation model is constructed to emulate common line faults in the power system. The study employs the empirical mode decomposition (EMD) algorithm and Hilbert-Huang Transform as methods for extracting fault characteristics in shipboard power distribution systems. EMD is utilized to analyze the characteristic quantities of voltage and current fault signals, followed by the extraction of fault features from intrinsic mode functions through the Hilbert-Huang Transform. The extracted fault feature vectors serve as input parameters for a convolutional neural network (CNN) optimized using the particle swarm optimization (PSO) and support vector machine (SVM) to establish a fault diagnosis model. The analysis demonstrates that the PSO-SVM-CNN model significantly enhances the fault diagnosis accuracy of shipboard area power distribution systems, achieving a precision rate of up to 99.5%. This research contributes to advancing the safety and reliability of shipboard electrical systems through effective fault diagnosis methodologies. © 2023 IEEE.

关键词： component Convolutional Neural Network empirical mode decomposition algorithm Hilbert-Huang Transform Particle Swarm algorithm Shipboard Area Power Distribution Electrical System

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