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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 cancers.However,the underlying mechanisms have not been completely clarified yet.This study aimed to assess the effects and mechanisms of(-)-epigallocatechin-3-gallate(EGCG)and(-)-epicatechin-3-gallate(ECG)on human melanoma skin A375 cells.Results showed that EGCG and ECG inhibited the proliferation of A375 cells and ECG showed better inhibitory effect.Flow cytometry analysis had shown that EGCG and ECG induced apoptosis and led to cell cycle arrest.EGCG and ECG decreased Bcl-2 expression and upregulated Caspase-3 protein level,indicating the development of apoptosis.Furthermore,EGCG and ECG could decreased mitochondrial membrane potential of A375 cells.In addition,the expression of Beclin-1,LC3 and Sirt3 were downregulated at protein levels,which known to be associated with autophagy.After autophagy was increased by rapamycin,the apoptotic trend was not change,indicating that apoptosis and autophagy are independent.Mechanistically,EGCG and ECG treatments decreased phosphorylated-AMPK(p-AMPK)and increased the ratios of P-PI3K,p-AKT and p-mTOR in melanoma cells.Conclusively,EGCG and ECG induced apoptosis via mitochondrial signaling pathway,downregulated autophagy through modulating the AMPK/mTOR and PI3K/AKT/mTOR signaling pathway.It 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 patient.While transmit-ting these collected data some adversaries may capture and misuse it due to the compromise of security.So,the major aim of this work is to enhance secure trans-mission of ECG signal in WBSN.To attain this goal,we present Pity Beetle Swarm Optimization Algorithm(PBOA)based Elliptic Galois Cryptography(EGC)with Chaotic Neural Network.To optimize the key generation process in Elliptic Curve Cryptography(ECC)over Galoisﬁeld or EGC,private key is chosen optimally using PBOA algorithm.Then the encryption process is enhanced by presenting chaotic neural network which is used to generate chaotic sequences or cipher data.Results 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 level.Nevertheless,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 urgently.To 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 signal.In 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 indexes.In 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 selection.Finally,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 method.The 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

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 survival.In addition,the Internet of Things(IoT)revolution has redefined modern healthcare systems and management by providing continuous monitoring.In this case,the data related to the heart is more important and requires proper analysis.For the analysis of heart data,Electrocardiogram(ECG)is used.In 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 accuracy.The 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 noise.This 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 attributes.Since 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 them.Several features were extracted to improve the detection of ECG data.Compared 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

A Global Training Model for Beat Classification Using Basic Electrocardiogram Morphological Features

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Computers, Materials & Continua 2022年第3期70卷 4503-4521页

作者： Shubha Sumesh John Yearwood Shamsul Huda Shafiq Ahmad School of Information Technology Deakin UniversityBurwoodVIC3128Australia Industrial Engineering Department College of EngineeringKing Saud UniversityP.O.Box 800Riyadh11421Saudi Arabia

Clinical Study and automatic diagnosis of electrocardiogram(ECG)data always remain a challenge in diagnosing cardiovascular activities.The analysis of ECG data relies on various factors like morphological features,classification techniques,methods or models used to diagnose and its performance improvement.Another crucial factor in themethodology is howto train the model for each patient.Existing approaches use standard training model which faces challenges when training data has variation due to individual patient characteristics resulting in a lower detection accuracy.This paper proposes an adaptive approach to identify performance improvement in building a training model that analyze global trainingmethodology against an individual training methodology and identifying a gap between them.We provide our investigation and comparative study on these methods and model with standard classification techniques with basic morphological features and Heart RateVariability(HRV)thatmay aid real time application.This approach helps in analyzing and evaluating the performance of different techniques and can suggests adoption of a best model identification with efficient technique and efficient attribute set for real-time systems.

关键词： ECG morphological feature HRV global adaptive training multilayer perceptron(MLP) support vector machine(SVM) random forest(RF)

Hesperidin attenuates arsenic trioxide-induced cardiac toxicity in rats

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Asian Pacific Journal of Tropical Biomedicine 2023年第4期13卷 156-164页

作者： Gayatri Khuntia Jeevan Ranjan Dash Biswadeep Jena Uma Kanta Mishra Subash Chandra Parija Department of Pharmacology and Toxicology College of Veterinary Sciences and Animal HusbandryOUATBhubaneswarIndia Department of Veterinary Surgery and Radiology College of Veterinary Sciences and Animal HusbandryOUATBhubaneswarIndia Department of Veterinary Anatomy and Histology College of Veterinary Sciences and Animal HusbandryOUATBhubaneswarIndia

Objective:To explore the cardioprotective effect of hesperidin against arsenic trioxide-induced cardiac toxicity in rats.Methods:Cardiac toxicity was induced by oral administration of 4 mg/kg arsenic trioxide for 30 days.Hematological,biochemical,electrocardiography,echocardiography,and histopathological examinations were performed.Results:Hesperidin decreased the neutrophil-to-lymphocyte ratio,calcium,creatine kinase-myoglobin binding,lactate dehydrogenase,IL-6,and lipid peroxidation,as well as increased sodium and potassium concentration and superoxide dismutase and catalase activity in arsenic trioxide-intoxicated rats.Moreover,it reduced peak systolic velocity and end-diastolic velocity while increasing heart rate.Arsenic trioxide-induced histopathological damage to cardiac tissue was prominently alleviated by hesperidin treatment.Conclusions:Hesperidin attenuates arsenic trioxide-induced cardiac toxicity in rats.Therefore,it can be further explored as a cardioprotective agent.

关键词： Arsenic trioxide Hesperidin Cardiotoxicity ECG CK-MB LDH

Heart Disease Classification Using Multiple K-PCA and Hybrid Deep Learning Approach

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Computer Systems Science & Engineering 2022年第6期41卷 1273-1289页

作者： S.Kusuma Dr.Jothi K.R School of Computer Science and Engineering Vellore Institute of TechnologyVellore632014India

One of the severe health problems and the most common types of heartdisease (HD) is Coronary heart disease (CHD). Due to the lack of a healthy lifestyle, HD would cause frequent mortality worldwide. If the heart attack occurswithout any symptoms, it cannot be cured by an intelligent detection system.An effective diagnosis and detection of CHD should prevent human casualties.Moreover, intelligent systems employ clinical-based decision support approachesto assist physicians in providing another option for diagnosing and detecting HD.This paper aims to introduce a heart disease prediction model including phaseslike (i) Feature extraction, (ii) Feature selection, and (iii) Classification. At first,the feature extraction process is carried out, where the features like a time-domainindex, frequency-domain index, geometrical domain features, nonlinear features,WT features, signal energy, skewness, entropy, kurtosis features are extractedfrom the input ECG signal. The curse of dimensionality becomes a severe issue.This paper provides the solution for this issue by introducing a new ModifiedPrincipal Component Analysis known as Multiple Kernel-based PCA for dimensionality reduction. Furthermore, the dimensionally reduced feature set is thensubjected to a classification process, where the hybrid classifier combining bothRecurrent Neural Network (RNN) and Restricted Boltzmann Machine (RBM)is used. At last, the performance analysis of the adopted scheme is compared overother existing schemes in terms of specific measures.

关键词： Heart disease prediction ECG recurrent neural network pca restricted boltzmann machine

Machine Learning for Detecting Blood Transfusion Needs Using Biosignals

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Computer Systems Science & Engineering 2023年第8期46卷 2369-2381页

作者： Hoon Ko Chul Park Wu Seong Kang Yunyoung Nam Dukyong Yoon Jinseok Lee Department of Biomedical Engineering College of Electronics and InformationKyung Hee UniversityYongin17104Korea Department of Internal Medicine Wonkwang University School of MedicineIksan54538Korea Department of Trauma Surgery Jeju Regional Trauma CenterCheju Halla General HospitalJeju63127Korea Department of Computer Science and Engineering Soonchunhyang UniversityAsan31538Korea Department of Biomedical Systems Informatics Yonsei University College of MedicineYongin03722Korea

Adequate oxygen in red blood cells carrying through the body to the heart and brain is important to maintain life.For those patients requiring blood,blood transfusion is a common procedure in which donated blood or blood components are given through an intravenous line.However,detecting the need for blood transfusion is time-consuming and sometimes not easily diagnosed,such as internal bleeding.This study considered physiological signals such as electrocardiogram(ECG),photoplethysmogram(PPG),blood pressure,oxygen saturation(SpO2),and respiration,and proposed the machine learning model to detect the need for blood transfusion accurately.For the model,this study extracted 14 features from the physiological signals and used an ensemble approach combining extreme gradient boosting and random forest.The model was evaluated by a stratified five-fold crossvalidation:the detection accuracy and area under the receiver operating characteristics were 92.7%and 0.977,respectively.

关键词： Blood transfusion ECG PPG pulse transit time blood pressure machine learning

Advances in telemedicine for the management of the elderly cardiac patient

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Journal of Geriatric Cardiology 2021年第9期18卷 759-767页

作者： Nadim El Jamal Bernard Abi-Saleh Hussain Isma’eel Division of Cariology American University of Beirut Faculty of Medicine and Medical CenterBeirutLebanon

Telemedicine is the use of information and communication technology to deliver healthcare at a distance.It has been resorted to during the COVID-19 pandemic to lessen the need for in-person patient care decreasing the risk of transmission,and it can be of benefit afterward in the management of cardiac disease.The elderly population has unique challenges concerning the use of telehealth technologies.We thus review the advances in telemedicine technologies in treating elderly cardiac patients including in our discussion only studies with a mean age of participants above 60.Remote monitoring of blood pressure,weight,and symptoms,along with home ECG recording has been found to be superior to usual in-clinic follow up.Combining remote monitoring with video conferencing with physicians,patient education websites,and applications is also of benefit.Remote monitoring of Implantable Cardioverter Defibrillators(ICD)and Cardiac Resynchronization Therapy Defibrillators(CRT-D)is also beneficial but can be at the cost of an increase in both appropriate and inappropriate interventions.Implantable sensing devices compatible with remote monitoring have been developed and have been shown to improve care and cost-effectiveness.New smartphone software can detect arrhythmias using home ECG recordings and can detect atrial fibrillation using smartphone cameras.Remote monitoring of implanted pacemakers has shown non-inferiority to in clinic follow up.On the other hand,small-scale questionnaire-based studies demonstrated the willingness of the elderly cardiac patients to use such technologies,and their satisfaction with their use and ease of use.Large-scale studies should further investigate useability in samples more representative of the general elderly population with more diverse socioeconomic and educational backgrounds.Accordingly,it seems that studying integrating multiple technologies into telehealth programs is of great value.Further efforts should also be put in validating the technologies for specific diseases along with the

关键词： ECG CRT ICD Advances in telemedicine for the management of the elderly cardiac patient

一种基于心电信号的pRRx序列分析心律失常的方法研究

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北京大学学报（自然科学版） 2018年第6期54卷 1166-1172页

作者：李冉王新安赵天夏刘彦伶李秋平北京大学深圳研究生院集成微系统科学工程与应用重点实验室深圳518055

提出一种从ECG数据中提取pRRx序列,进而分析心律失常的方法。选取20例正常窦性心律和20例心律失常患者的ECG数据,计算相应的pRRx序列(x从1～100 ms取值),两组pRRx序列的分布呈现明显的差异。对pRRx序列进行线性和非线性分析,结果表明:1... 详细信息

提出一种从ECG数据中提取pRRx序列,进而分析心律失常的方法。选取20例正常窦性心律和20例心律失常患者的ECG数据,计算相应的pRRx序列(x从1～100 ms取值),两组pRRx序列的分布呈现明显的差异。对pRRx序列进行线性和非线性分析,结果表明:1)线性指标中,AVRR, rMSSD和SDSD在两组序列中表现出显著性差异(P<0.001);2)非线性指标中,pRRx序列直方分布信息熵dh(S)、功率谱直方分布信息熵ph(S)功率谱全频段信息熵(Spf)和pRRx的分形维数(Dsf, Dcf, Dvm和Drms)在两组序列中表现出显著的统计学差异(P<0.001)。因此,基于pRRx序列的分析在一定程度上能够反映心脏系统的线性和非线性性质,可以作为一种新颖且有效的心律失常分析方法。

关键词： ECG pRRx序列线性指标非线性指标熵分形维数心律失常