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Electrocardiogram(ecg) pattern modeling and recognition via deterministic learning

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Control Theory and Technology 2014年第4期12卷 333-344页

作者： Xunde DONG Cong WANG Junmin HU Shanxing OU School of Automation Science and Engineering South China University of Technology Department of Radiology General Hospital of Guangzhou Military Command

A method for electrocardiogram （ecg） pattern modeling and recognition via deterministic learning theory is presented in this paper. Instead of recognizing ecg signals beat-to-beat, each ecg signal which contains a number of heartbeats is recognized. The method is based entirely on the temporal features （i.e., the dynamics） of ecg patterns, which contains complete information of ecg patterns. A dynamical model is employed to demonstrate the method, which is capable of generating synthetic ecg signals. Based on the dynamical model, the method is shown in the following two phases： the identification （training） phase and the recognition （test） phase. In the identification phase, the dynamics of ecg patterns is accurately modeled and expressed as constant RBF neural weights through the deterministic learning. In the recognition phase, the modeling results are used for ecg pattern recognition. The main feature of the proposed method is that the dynamics of ecg patterns is accurately modeled and is used for ecg pattern recognition. Experimental studies using the Physikalisch-Technische Bundesanstalt （PTB） database are included to demonstrate the effectiveness of the approach.

关键词： ecg Pattern recognition Deterministic learning Dynamics Temporal features

EGCG and ecg induce apoptosis and decrease autophagy via the AMPK/mTOR and PI3K/AKT/mTOR pathway in human melanoma cells

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Chinese Journal of Natural Medicines 2022年第4期20卷 290-300页

作者： DU Bing-Xin LIN Pei LIN Jun School of Life Sciences and Health Engineering Jiangnan UniversityWuxi 214122China

Catechins have been proven to exert antitumor effects in different kinds of ***,the underlying mechanisms have not been completely clarified *** study aimed to assess the effects and mechanisms of(-)-epigallocatechin-3-gallate(EGCG)and(-)-epicatechin-3-gallate(ecg)on human melanoma skin A375 *** showed that EGCG and ecg inhibited the proliferation of A375 cells and ecg showed better inhibitory *** cytometry analysis had shown that EGCG and ecg induced apoptosis and led to cell cycle *** and ecg decreased Bcl-2 expression and upregulated Caspase-3 protein level,indicating the development of ***,EGCG and ecg could decreased mitochondrial membrane potential of A375 *** addition,the expression of Beclin-1,LC3 and Sirt3 were downregulated at protein levels,which known to be associated with *** autophagy was increased by rapamycin,the apoptotic trend was not change,indicating that apoptosis and autophagy are ***,EGCG and ecg treatments decreased phosphorylated-AMPK(p-AMPK)and increased the ratios of P-PI3K,p-AKT and p-mTOR in melanoma ***,EGCG and ecg induced apoptosis via mitochondrial signaling pathway,downregulated autophagy through modulating the AMPK/mTOR and PI3K/AKT/mTOR signaling *** indicated that EGCG and ecg may be utilized in human melanoma treatment.

关键词： EGCG ecg Apoptosis Autophagy PI3K/AKT/mTOR

Secured ecg Signal Transmission Using Optimized EGC with Chaotic Neural Network in WBSN

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Computer Systems Science & Engineering 2023年第2期44卷 1109-1123页

作者： Ishani Mishra Sanjay Jain Vivek Maik Department of ECE New Horizon College of EngineeringBengaluru560103India CMR Institute of Technology Bengaluru560037India SRM University Chennai603203India

In wireless body sensor network(WBSN),the set of electrocardiogram(ecg)data which is collected from sensor nodes and transmitted to the server remotely supports the experts to monitor the health of a *** transmit-ting these collected data some adversaries may capture and misuse it due to the compromise of ***,the major aim of this work is to enhance secure trans-mission of ecg signal in *** attain this goal,we present Pity Beetle Swarm Optimization Algorithm(PBOA)based Elliptic Galois Cryptography(EGC)with Chaotic Neural *** optimize the key generation process in Elliptic Curve Cryptography(ECC)over Galoisﬁeld or EGC,private key is chosen optimally using PBOA *** the encryption process is enhanced by presenting chaotic neural network which is used to generate chaotic sequences or cipher *** of this work show that the proposed cryptogra-phy algorithm attains better encryption time,decryption time,throughput and SNR than the conventional cryptography algorithms.

关键词： Wireless body sensor network ecg pity beetle swarm optimization algorithm elliptic galois cryptography and chaotic neural network

Optimal decomposition level selection approach in wavelet threshold denoising algorithm for ecg signal

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The Journal of China Universities of Posts and Telecommunications 2023年第4期30卷 86-104页

作者： Yao Yindi Yi Jun Zeng Zhibin Li Xiong Wang Chen Li Yuhang School of Communications and Information Engineering Xi'an University of Posts and TelecommunicationsXi'an 710121China School of Microelectronics Xidian UniversityXi'an 710071China School of Information and Communication Engineering Beijing University of Posts and TelecommunicationsBeijing 100876China

The effect of electrocardiogram(ecg)signal wavelet denoising depends on the optimal configuration of its control parameters and the selection of the optimal decomposition ***,the existing optimal decomposition level selection scheme has some problems,such as lack of reliable theoretical guidance and insufficient accuracy,which need to be solved *** solve this problem,this paper proposes an optimal decomposition level selection method based on multi-index fusion,which is used to select the optimal decomposition level for wavelet threshold denoising of ecg *** the stage of index selection,in order to overcome the limitation of a single evaluation index,the optimal multi-evaluation index is selected through the joint analysis of the geometric and physical significance of traditional evaluation *** the stage of index fusion,based on the method of weighting the selected multiple indexes by the information entropy weight method and the coefficient of variation method,an optimal decomposition level selection method based on the evaluation index Z is proposed to improve the accuracy of the optimal decomposition level ***,extensive experiments are carried out on the real ecg signal from the Massachusetts Institute of Technology-Beth Israel Hospital(MIT-BIH)arrhythmia database and simulated signal to test the performance of the proposed *** experimental results show that the accuracy of this method is superior to other related methods,and it can achieve better denoising effect of ecg signal.

关键词： ecg signal wavelet threshold denoising optimal decomposition level muti-index fusion

A low-power portable ecg sensor interface with dry electrodes

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Journal of Semiconductors 2013年第5期34卷 104-109页

作者：浦小飞万磊张辉秦亚杰洪志良 State Key Laboratory of ASIC & System Fudan University

This paper describes a low-power portable sensor interface dedicated to sensing and processing elec- trocardiogram （ecg） signals. Dry electrodes were employed in this ecg sensor, which eliminates the need of conductive gel and avoids complicated and mandatory skin preparation before electrode attachment. This ecg sensor system consists of two ICs, an analog front-end （AFE） and a successive approximation register analog-to- digital converter （SAR ADC） containing a relaxation oscillator. This proposed design was fabricated in a 0.18 #m 1P6M standard CMOS process. The AFE for extracting the biopotential signals is essential in this ecg sensor. In measurements, the AFE obtains a mid-band gain of 45 dB, referred noise of 2.8μV rms while consuming 1μW from the a bandwidth from 0.6 to 160 Hz, and a total input 1.8 V supply. The noise efficiency factor （NEF） of our design is 3.4. After conditioning, the amplified ecg signal is digitized by a 12-bit SAR ADC with 61.8 dB SNDR and 220 fJ/conversion-step. Finally, a complete ecg sensor interface with three dry copper electrodes is demonstrated in real-word setting, showing successful recordings of a capture ecg waveform.

关键词： ecg sensor interface dry electrode analog front-end SAR ADC

Deep Learning-Based ecg Classification for Arterial Fibrillation Detection

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Computers, Materials & Continua 2024年第6期79卷 4805-4824页

作者： Muhammad Sohail Irshad Tehreem Masood Arfan Jaffar Muhammad Rashid Sheeraz Akram Abeer Aljohani Faculty of Computer Science&Information Technology The Superior UniversityLahore54000Pakistan Intelligent Data Visual Computing Research(IDVCR) Lahore54000Pakistan Department of Computer Science National University of TechnologyIslamabad45000Pakistan Information Systems Department College of Computer and Information SciencesImam Mohammad Ibn Saud Islamic University(IMSIU)Riyadh11432Saudi Arabia Department of Computer Science Applied CollegeTaibah UniversityMedina42353Saudi Arabia

The application of deep learning techniques in the medical field,specifically for Atrial Fibrillation(AFib)detection through Electrocardiogram(ecg)signals,has witnessed significant *** and timely diagnosis increases the patient’s chances of ***,issues like overfitting and inconsistent accuracy across datasets remain *** a quest to address these challenges,a study presents two prominent deep learning architectures,ResNet-50 and DenseNet-121,to evaluate their effectiveness in AFib *** aim was to create a robust detection mechanism that consistently performs *** such as loss,accuracy,precision,sensitivity,and Area Under the Curve(AUC)were utilized for *** findings revealed that ResNet-50 surpassed DenseNet-121 in all evaluated *** demonstrated lower loss rate 0.0315 and 0.0305 superior accuracy of 98.77%and 98.88%,precision of 98.78%and 98.89%and sensitivity of 98.76%and 98.86%for training and validation,hinting at its advanced capability for AFib *** insights offer a substantial contribution to the existing literature on deep learning applications for AFib detection from ecg *** comparative performance data assists future researchers in selecting suitable deep-learning architectures for AFib ***,the outcomes of this study are anticipated to stimulate the development of more advanced and efficient ecg-based AFib detection methodologies,for more accurate and early detection of AFib,thereby fostering improved patient care and outcomes.

关键词： Convolution neural network atrial fibrillation area under curve ecg false positive rate deep learning classification

Decomposition and compression for ecg and EEG signals with sequence index coding method based on matching pursuit

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The Journal of China Universities of Posts and Telecommunications 2012年第2期19卷 92-95页

作者： ZHANGHong-xin CHENCan-feng WUYan-ling LIPei-hua SchoolofElectronicEngineering BeijingUniversityofPostsandTelecommunicationsBeijing100876China NokiaResearchCentre Beijing100020China KeyLaboratoryofWorkSafetyIntelligentMonitoring BeijingUniversityoPostsandTelecommunicationsBeijing100876China

An efficient compression method is proposed by encoding the sequence index of atoms based on matching pursuit （MP） algorithm with over-complete Gabor dictionary, which has the merit to adjust the compression ratio （CR） according to the practical request with low distortion. It is also combined with genetic algorithm （GA） to reduce the computation complexity Then, the validity of this method is verified by applying it in the compression of electrocardiography （ecg） and Electroencephalography （EEG） signals. The simulation results show that the CR can be achieved at 18：1 with only 1.06% and 2.15% reconstruction errors on ecg and EEG signals respectively. It has higher CR and less reconstruction errors compared to that of the traditional methods, and noise suppression effect is also presented.

关键词： ignal decomposition and compression matching pursuit ecg EEG

Noisy ecg Signal Data Transformation to Augment Classification Accuracy

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Computers, Materials & Continua 2022年第5期71卷 2191-2207页

作者： Iqra Afzal Fiaz Majeed Muhammad Usman Ali Shahzada Khurram Akber Abid Gardezi Shafiq Ahmad Saad Aladyan Almetwally M.Mostafa Muhammad Shafiq Department of Information Technology University of GujratGujrat50700Pakistan Department of Computer Science University of GujratGujrat50700Pakistan Faculty of Computing The Islamia University of BahawalpurBahawalpur63100Pakistan Department of Computer Science COMSATS University IslamabadIslamabadPakistan Industrial Engineering Department College of EngineeringKing Saud UniversityP.O.Box 800Riyadh 11421Saudi Arabia Department of Information Systems College of Computer and Information SciencesKing Saud UniversityP.O.Box 800Riyadh11421Saudi Arabia Department of Information and Communication Engineering Yeungnam UniversityGyeongsan38541Korea

In this era of electronic health,healthcare data is very important because it contains information about human *** addition,the Internet of Things(IoT)revolution has redefined modern healthcare systems and management by providing continuous *** this case,the data related to the heart is more important and requires proper *** the analysis of heart data,Electrocardiogram(ecg)is *** this work,machine learning techniques,such as adaptive boosting(AdaBoost)is used for detecting normal sinus rhythm,atrial fibrillation(AF),and noise in ecg signals to improve the classification *** proposed model uses ecg signals as input and provides results in the form of the presence or absence of disease AF,and classifies other signals as normal,other,or *** article derives different features from the signal using Maximal Information Coefficient(MIC)and minimum Redundancy Maximum Relevance(mRMR)technique,and then classifies them based on their *** the ecg contains some kind of noise and irregular data streams so the purpose of this study is to remove artifacts from the ecg signal by deploying the method of Second-Order-Section(SOS)(filter)and correctly classify *** features were extracted to improve the detection of ecg *** with existing methods,this work gives promising results and can help improve the classification accuracy of the ecg signals.

关键词： ecg atrial fibrillation adaptive boosting heart rate variability

TinyML-Based Classification in an ecg Monitoring Embedded System

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Computers, Materials & Continua 2023年第4期75卷 1751-1764页

作者： Eunchan Kim Jaehyuk Kim Juyoung Park Haneul Ko Yeunwoong Kyung Department of Intelligence and Information Seoul National UniversitySeoul08826Korea Nexon Korea Cop. SeongnamKyungki13487Korea Advanced CP Lab.Samsung Electronics SuwonKyungki16677Korea Department of Electronic Engineering Kyung Hee UniversityYonginKyungki17104Korea Division of Information&Communication Engineering Kongju National UniversityCheonanChungcheongnam31080Korea

Recently, the development of the Internet of Things (IoT) hasenabled continuous and personal electrocardiogram (ecg) monitoring. In theecg monitoring system, classification plays an important role because it canselect useful data (i.e., reduce the size of the dataset) and identify abnormaldata that can be used to detect the clinical diagnosis and guide furthertreatment. Since the classification requires computing capability, the ecgdata are usually delivered to the gateway or the server where the classificationis performed based on its computing resource. However, real-time ecg datatransmission continuously consumes battery and network resources, whichare expensive and limited. To mitigate this problem, this paper proposes atiny machine learning (TinyML)-based classification (i.e., TinyCES), wherethe ecg monitoring device performs the classification by itself based onthe machine-learning model, which can reduce the memory and the networkresource usages for the classification. To demonstrate the feasibility, afterwe configure the convolutional neural networks (CNN)-based model usingecg data from the Massachusetts Institute of Technology (MIT)-Beth IsraelHospital (BIH) arrhythmia and the Physikalisch Technische Bundesanstalt(PTB) diagnostic ecg databases, TinyCES is validated using the TinyMLsupportedArduino prototype. The performance results show that TinyCEScan have an approximately 97% detection ratio, which means that it has greatpotential to be a lightweight and resource-efficient ecg monitoring system.

关键词： Holter ecg arduino internet of things(IoT) TinyML