检索结果-南通市图书馆

Attention emotion recognition via ecg signals

Quantitative Biology 2022年第3期10卷 276-286页

作者： Aihua Mao Zihui Du Dayu Lu Jie Luo School of Computer Science&Engineering South China University of TechnologyGuangzhou 510006China School of Medicine South China University of TechnologyGuangzhou 510006China School of Fine Art and Artistic Design Guangzhou UniversityGuangzhou 510006China

Background:Physiological signal-based research has been a hot topic in affective *** works mainly focus on some strong,short-lived emotions(e.g.,joy,anger),while the attention,which is a weak and long-lasting emotion,receives less *** this paper,we present a study of attention recognition based on electrocardiogram(ecg)signals,which contain a wealth of information related to ***:The ecg dataset is derived from 10 subjects and specialized for attention *** relieve the impact of noise of baseline wondering and power-line interference,we apply wavelet threshold denoising as preprocessing and extract rich features by pan-tompkins and wavelet decomposition *** improve the generalized ability,we tested the performance of a variety of combinations of different feature selection algorithms and ***:Experiments show that the combination of generic algorithm and random forest achieve the highest correct classification rate(CCR)of 86.3%.Conclusion:This study indicates the feasibility and bright future of ecg-based attention research.

关键词： affective computing attention recognition ecg signals

来源：

维普期刊数据库

同方期刊数据库评论

在线全文

学校读者我要写书评

暂无评论

De-Noising of ecg signals by Design of an Optimized Wavelet

引用

Circuits and Systems 2016年第11期7卷 3746-3755页

作者： Vahid Makhdoomi Kaviri Masoud Sabaghi Saeid Marjani Department of Electrical Engineering Islamshar Branch Islamic Azad University Tehran Iran Laser and Optics Research School Nuclear Science and Technology Research School Atomic Energy Organization of Iran Tehran Iran Department of Electrical Engineering Ferdowsi University of Mashhad Mashhad Iran

In this paper, a different method for de-noising of ecg signals using wavelets is presented. In this strategy, we will try to design the best wavelet for de-nosing. Genetic algorithm tests wide range of quadrature filter banks and the best of them will be chosen that minimize the Signal-to-Noise Ratio (SNR). Furthermore, the wavelet function and scaling function related to these filters are reported as the best wavelet for de-noising. Simulation results for de-noising of a noisy ecg signal show that using obtained wavelet by proposed method improves the SNR of about 2.5 dB.

关键词： Wavelets De-Noising Genetic Algorithm ecg signals

来源：

维普期刊数据库评论

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

Research on Denoising Algorithm for ecg signals

Research on Denoising Algorithm for ECG Signals

引用

第二十九届中国控制会议

作者： WANG Jie-Sheng,ZHANG Yong,ZHANG Ping,SUN Shi-Feng School of Electronic and Information Engineering,Liaoning University of Science&Technology y,Anshan,114044,P.R.China

In the course of collecting ecg signal data,electromyography interference,baseline drift and 50Hz power line interference will be introduced inevitably,which often makes it difficult to identify the characteristics of ecg signals to some degree by using conventional identification *** filter,a nonlinear signal filter with simple operation and high speed,is used to remove the low-frequencies noises in Electrocardiogram signals,such as baseline *** the dyadic wavelet of WTS is a set of band-pass filters having different frequency bands in every scale,the wavelet transformation was selected to decompose the original *** interference-eliminated ecg signal was formed by reconstruction from the changed coefficients of wavelet.A simulation experiment is adopted to make it sure how to determine the self-adaptive threshold selections,the proper decomposition order and wavelet *** method was tested by using both ecg signals from MIT/BIH database and ecg signals generated via computer *** results show that the algorithm can suppress the main noises existing in ecg signals efficiently and can satisfy the requirements of clinical analysis and diagnosis on ecg waveforms.

关键词： ecg signals Median Filtering Wavelet Transformation Denoising Algorithm

来源： cnki会议评论

在线全文

cnki会议

学校读者我要写书评

暂无评论

Improved Bat Algorithm with Deep Learning-Based Biomedical ecg Signal Classification Model

引用

Computers, Materials & Continua 2023年第2期74卷 3151-3166页

作者： Marwa Obayya Nadhem NEMRI Lubna A.Alharbi Mohamed K.Nour Mrim M.Alnfiai Mohammed Abdullah Al-Hagery Nermin M.Salem Mesfer Al Duhayyim Department of Biomedical Engineering College of EngineeringPrincess Nourah Bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Information Systems College of Science and Art at MahayilKing Khalid UniversitySaudi Arabia Department of Computer Science College of Computers and Information TechnologyTabuk UniversitySaudi Arabia Department of Computer Sciences College of Computing and Information SystemUmm Al-Qura UniversitySaudi Arabia Department of Information Technology College of Computers and Information TechnologyTaif UniversityP.O.Box 11099Taif21944Saudi Arabia Department of Computer Science College of ComputerQassim UniversitySaudi Arabia Department of Electrical Engineering Faculty of Engineering and TechnologyFuture University in EgyptNew Cairo11835Egypt Department of Computer Science College of Sciences and Humanities-AflajPrince Sattam Bin Abdulaziz UniversitySaudi Arabia

With new developments experienced in Internet of Things(IoT),wearable,and sensing technology,the value of healthcare services has *** evolution has brought significant changes from conventional medicine-based healthcare to real-time observation-based *** Electrocardiogram(ecg)signals are generally utilized in examination and diagnosis of Cardiovascular Diseases(CVDs)since it is quick and non-invasive in *** to increasing number of patients in recent years,the classifier efficiency gets reduced due to high variances observed in ecg signal patterns obtained from *** such scenario computer-assisted automated diagnostic tools are important for classification of ecg *** current study devises an Improved Bat Algorithm with Deep Learning Based Biomedical ecgSignal Classification(IBADL-BecgC)*** accomplish this,the proposed IBADL-BecgC model initially pre-processes the input ***,IBADL-BecgC model applies NasNet model to derive the features from test ecg *** addition,Improved Bat Algorithm(IBA)is employed to optimally fine-tune the hyperparameters related to NasNet ***,Extreme Learning Machine(ELM)classification algorithm is executed to perform ecg classification *** presented IBADL-BecgC model was experimentally validated utilizing benchmark *** comparison study outcomes established the improved performance of IBADL-BecgC model over other existing methodologies since the former achieved a maximum accuracy of 97.49%.

关键词： Data science ecg signals improved bat algorithm deep learning biomedical data data classification machine learning

来源：

维普期刊数据库评论

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

A Smart Heart Disease Diagnostic System Using Deep Vanilla LSTM

引用

Computers, Materials & Continua 2023年第10期77卷 1251-1279页

作者： Maryam Bukhari Sadaf Yasmin Sheneela Naz Mehr Yahya Durrani Mubashir Javaid Jihoon Moon Seungmin Rho Department of Computer Science COMSATS University IslamabadAttock CampusAttockPakistan Department of Computer Science COMSATS University IslamabadIslamabadPakistan Department of Computer Science UET TaxilaTaxilaPakistan Department of AI and Big Data Soonchunhyang UniversityAsanKorea Department of Industrial Security Chung-Ang UniversitySeoul06974Korea

Effective smart healthcare frameworks contain novel and emerging solutions for remote disease diagnostics,which aid in the prevention of several diseases including heart-related *** this context,regular monitoring of cardiac patients through smart healthcare systems based on Electrocardiogram(ecg)signals has the potential to save many *** existing studies,several heart disease diagnostic systems are proposed by employing different state-of-the-art methods,however,improving such methods is always an intriguing area of ***,in this research,a smart healthcare system is proposed for the diagnosis of heart disease using ecg *** proposed framework extracts both linear and time-series information on the ecg signals and fuses them into a single framework *** linear characteristics of ecg signals are extracted by convolution layers followed by Gaussian Error Linear Units(GeLu)and time series characteristics of ecg beats are extracted by Vanilla Long Short-Term Memory Networks(LSTM).Following on,the feature reduction of linear information is done with the help of ID Generalized Gated Pooling(GGP).In addition,data misbalancing issues are also addressed with the help of the Synthetic Minority Oversampling Technique(SMOTE).The performance assessment of the proposed model is done over the two publicly available datasets named MIT-BIH arrhythmia database(MITDB)and PTB Diagnostic ecg database(PTBDB).The proposed framework achieves an average accuracy performance of 99.14%along with a 95%recall value.

关键词： Smart systems deep learning ecg signals heart disease concurrent learning LSTM generalized gated pooling

来源：

维普期刊数据库评论

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

Optimized Pan-Tompkins Based Heartbeat Detection Algorithms

Optimized Pan-Tompkins Based Heartbeat Detection Algorithms

引用

第32届中国控制与决策会议

作者： Xueyu Wu Zhonghua Wang Bo Xu Xibo Ma College of Electrical Engineering University of Jinan CAS Key Laboratory of Molecular Imaging Institute of AutomationChinese Academy of Science The University of Chinese Academy of Science

The whole physiological state of cardiac electrical activity is reflected by electrocardiogram（ecg）signals collected from the body *** signals can be used as important basis for the diagnosis of heart,***,when analyzing ecg signals,people often process the signals in a record into units of ***,accurate heartbeat detection is an important prerequisite for the correct diagnosis of heart disease by the *** traditional heartbeat detection only obtains the QRS complex based on the Pan-Tompkins algorithm,and then performs time interception based on previous experience（forward and backward）.The accuracy of heartbeats detection will be affected by individual *** this paper,an optimized Pan-Tompkins algorithm based on adding P-wave and T-wave location is proposed for the detection of ecg *** algorithm was evaluated using 109966 samples from the MITBIH Arrhythmia Database including a variety of arrhythmia data（such as atrioventricular block,ventricular tachycardia,early ventricular contraction,atrial flutter,ventricular premature beats,ventricular fibrillation）.109519 samples were correctly detected from 109966 *** method achieved 99.68% positive prediction rate（P+） with 99.90% sensitivity（SE）.Our approach will lay a foundation for the processing of ecg signals and therefore improve the accuracy for the diagnosis of heart diseases.

关键词： Pan-Tompkins heartbeat detection ecg signals

来源： cnki会议评论

在线全文

cnki会议

学校读者我要写书评

暂无评论

Design and implementation of a portable intelligent electrocardiograph

Design and implementation of a portable intelligent electroc...

引用

第26届中国控制与决策会议

作者： Chen Nankai Wang Yaonan Lu Xiaoqing College of Electrical and Information Engineering Hunan University

ISBN: (纸本)9781479937097

In this paper, a novel portable intelligent electrocardiograph（ ecg） is designed which is low-cost, small size and easy to popularize. With AT89S52 single chip and LCD12864 monitor as its core controller and display instrument respectively, based on an improved signal processing and waveform detection method, the ecg is able to achieve real-time display of heart waveform and heart rate on its LCD. In particular, a secondary amplifying circuit that contains the high-pass, low-pass and band-stop filter circuits is proposed, which is low-power-consuming, highly-precise, and robust against various disturbances. Moreover, the detected ecg signal data can be stored automatically on a SD card for further analysis and diagnosis.

关键词： ecg signals Active filter circuits Intelligent detection Portable

来源： cnki会议评论

在线全文

cnki会议

学校读者我要写书评

暂无评论

欢迎您,

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

在线全文

在线全文

在线全文

在线全文

在线全文

在线全文

在线全文

请选择保存的检索档案：

请选择收藏分类：

通借通还

欢迎您,

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

在线全文

在线全文

在线全文

在线全文

在线全文

在线全文

在线全文

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：