检索结果-内蒙古大学图书馆

Mobile Notification System for Blood Pressure and Heartbeat Anomaly Detection

JOURNAL OF WEB ENGINEERING 2020年第5-6期19卷 747-773页

作者： pandey, Saswat Raj Hicks, David Goyal, Ayush Gaurav, Devottam Tiwari, Sanju Mishra Texas A&M Univ Dept Elect Engn & Comp Sci Kingsville TX USA IIT Delhi Dept Comp Sci & Engn New Delhi India Univ Autonoma Tamaulipas Cd Victoria Tamps Mexico

In today's fast-paced world where patients may need to be remotely monitored while they are away or out of hospitals, there is a need for mobile applications that can gather the biometric and biomedical signals from any number of devices and sensors, collecting biometric or biomedical data from a patient. This effort boons the improvement of a circulatory strain and heartbeat anomaly detection and notice apparatus as an Android application that permits quick discovery of any variations from the norm in a patient's fundamental dependent on the pan-tompkins algorithm and reports it to the pertinent emergency clinic or clinical staff. The blood ECG information can be gotten from the health tracker sensors by means of a Bluetooth association and the patient can enter their Blood pressure esteems. In this case, the information gathered from a set of reproduced data, which is identified with a triggering notice from Firebase Cloud function. This notification is further acknowledged by the enrolled specialist. The system's security part is represented by the fine-grained consent procedure which directs that solitary significant authorizations are required for the best possible working of the application which ought to be given to the application. An encryption method using the Blowfish algorithm is included as a feature of the developed mobile Android application to provide secure data transfer of the patient's vital signals.

关键词： Anomaly detection pan-tompkins algorithm Firebase cloud messaging service diastolic blood pressure systolic blood pressure ECG

来源：评论

学校读者我要写书评

暂无评论

A review on remote health monitoring sensors and their filtering techniques

引用

Global Transitions Proceedings 2021年第2期2卷 392-401页

作者： Divya Sri Rajeswari Hitha Shree J Ananya L.N. Simha Maria Nuzhath Subhani Shivaleelavathi B G Veeramma Yatnalli JSS Academy of Technical Education Dr. Vishnuvardhan Rd Uttarahalli-Kengeri Main Road Srinivaspura Bengaluru 560060 India

The novel coronavirus outbreak has affected over 177 million people in around 218 countries and Territories. Covid-19 is life-threatening for the elderly and for those with chronic health conditions. This has set out a state of panic where people have the need to constantly monitor their health. The objective of this paper is to review the various sensor components, filtering algorithms, classification techniques, existing methods and technologies that play an important role in developing a safe remote health monitoring device. Comparison of sensor devices is done to understand the features of available products in the market. An extensive survey of ECG monitoring, lung acoustic signals and how COVID-19 affects this vital signature, thermal sensors, and pulse oximeters are presented. The need for a device in this situation to cater to the health issues is necessary.

关键词： COVID-19 ECG monitoring Health monitoring Lung acoustic signals Machine learning MIT-BIH arrhythmia database pan-tompkins algorithm Pulse oximetry QRS complex Temperature sensor

来源：评论

学校读者我要写书评

暂无评论

A novel IRBF-RVM model for diagnosis of atrial fibrillation

引用

COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2019年 177卷 183-192页

作者： Kong, Dongdong Zhu, Junjiang Wu, Shangshi Duan, Chaoqun Lu, Lixin Chen, Dongxing Shanghai Univ Sch Mechatron Engn & Automat 99 Shanghai Rd Shanghai Peoples R China China Jiliang Univ Coll Mech & Elect Engn Hangzhou Zhejiang Peoples R China Shanghai Tenth Peoples Hosp Dept Cardiovasc Med Shanghai Peoples R China

Background and objective: Atrial fibrillation (AF) is one of the common cardiovascular diseases, and electrocardiography (ECG) is a key indicator for the detection and diagnosis of AF and other heart diseases. In this study, an improved machine learning method is proposed for rapid modeling and accurate diagnosis of AF. Methods: This paper presents a novel IRBF-RVM model that combines the integrated radial basis function (IRBF) and relevance vector machine (RVM), which is utilized for the diagnosis of AF. The synchronous 12-lead ECG signals are collected from the human body surface so as to fully reflect the electrical activity of the whole heart. RR intervals of the QRS-waves in ECG signals are obtained by means of the classical pan-tompkins algorithm. The RR-features extracted from RR intervals are adopted as the diagnostic features for AF patients. In addition, the conventional RBF-RVM model, support vector machine (SVM) and other machine learning methods are also investigated for the diagnosis of AF so as to reflect the advantage of the proposed IRBF-RVM model. The open MIT-BIH arrhythmia database (MITDB) is also used to evaluate the predictive performance of these state-of-the-art methods. Results: Altogether 1056 AF patients and 904 healthy people are participated in this study and validate the effectiveness of each channel of the 12-lead ECG signals. Experimental results show that the classification rate of IRBF-RVM can reach up to 98.16% by recurring to Channel II of the 12-lead ECG signals. Conclusions: IRBF-RVM absorbs the advantages of IRBF, which makes the kernel parameter of IRBF-RVM have a much larger selectable region than RBF-RVM. In addition, RVM has faster modeling and recognition speed in comparison with SVM. This work lays the foundation for the application of RVM to accurate diagnosis of AF. (C) 2019 Elsevier B.V. All rights reserved.

关键词： Atrial fibrillation pan-tompkins algorithm Integrated radial basis function Relevance vector machine

来源：评论

学校读者我要写书评

暂无评论

Determination of R-peaks in ECG Signal Using Hilbert Transform and pan-tompkins algorithms 25

Determination of R-peaks in ECG Signal Using Hilbert Transfo...

引用

25th Signal Processing and Communications Applications Conference (SIU)

作者： Diker, Aykut Avci, Engin Gedikpinar, Mehmet Bitlis Eren Univ Enformat Bolumu Bitlis Turkey Firat Univ Elekt Elekt Muhendisligi Yazilim Muhendisligi Elazig Turkey

ISBN: (纸本)9781509064946

Electrocardiogram (ECG) illustrates the electrical activity in the heart, and it is the most important physiological parameter that gives the correct assessment regarding the functioning of the heart. Accurate detection of R peaks in the ECG signal is an important parameter in determining of heart disorders. In this study, The R peaks of ECG records from the Physionet MIT-BIH arrhythmia database were detected using Hilbert Transform and pan-tompkins algorithms. The pan-tompkins R peak detector is validated using the ECG records of the MIT-BIH arrhythmia database, and achieves with detection accuracy of 97.12%, sensitivity of 98.46% and positive predictivity of 98.62%.

关键词： ECG Hilbert Transform pan-tompkins algorithm MIT-BIH

来源：评论

学校读者我要写书评

暂无评论

A new emotion detection algorithm using extracted features of the different time-series generated from ST intervals Poincare map

引用

BIOMEDICAL SIGNAL PROCESSING AND CONTROL 2020年第0期59卷 101902-101902页

作者： Baghizadeh, Maryam Maghooli, Keivan Farokhi, Fardad Dabanloo, Nader Jafarnia Islamic Azad Univ Dept Biomed Engn Sci & Res Branch Tehran Iran Islamic Azad Univ Dept Biomed Engn Cent Tehran Branch Tehran Iran

Emotion recognition using biological signals plays an essential role in studying psychological states. In many studies, the distinct effect of each physiological signal usually is ignored. By limiting the physiological signals and just evaluating the ECG signals, the paper aims to study the dynamical behavior of the Poincare map in the form of the feature extraction from the generated time-series. For this purpose, the detection of emotional states in the two-dimensional model of emotion (Arousal-Valence) is utilized using the electrocardiogram signals recorded on the MAHNOB-HCl tagging database. So after signal processing, the waves of Q, R, S, and T are detected by processing the ECG signals using the pan-tompkins algorithm. The Poincare map is adopted for the RR, QT, and ST intervals, then five different time-series are extracted from this mapping, subsequently, various features are extracted from these different time-series in the time domain, frequency domain, time-frequency domain, and nonlinear domain analysis. Finally, the classification of emotional states is performed using three classifiers: KNN, SVM, and MLP. In this study, the extracted optimal features of the different time-series generated from Poincare map of the ST Intervals are achieved the best average accuracies of 82.17 % +/- 4.73 and 78.07 % +/- 3.59 in the arousal and valence model, respectively. The superiority of the obtained results from the extracted features of the five different time-series generated from ST-Intervals Poincare map expresses that in comparison with RR and QT Intervals, the ST Intervals are more affected by ANS response to the emotional stimuli. (C) 2020 Elsevier Ltd. All rights reserved.

关键词： Electrocardiogram (ECG) Emotion detection Poincare map Feature extraction pan-tompkins algorithm

来源：评论

学校读者我要写书评

暂无评论

Automatic QRS Onset Detection of ECG Signal using Secant Line Slope Formula 15

Automatic QRS Onset Detection of ECG Signal using Secant Lin...

引用

IEEE 15th International Colloquium on Signal Processing and its Applications (CSPA)

作者： Jumahat, Shaliza Beng, Gan Kok Misran, Norbahiah Islam, Mohammad Tariqul Mahri, Nurhafizah Univ Kebangsaan Malaysia Fac Engn & Built Environm Ctr Adv Elect & Commun Engn Bangi 43000 Selangor Malaysia Univ Kebangsaan Malaysia Fac Engn & Built Environm Bangi 43000 Selangor Malaysia

ISBN: (纸本)9781538675632

In automatic electrocardiogram (ECG) signal analysis, the QRS onset must be identified prior to QT interval or QRS duration measurements. These measurements are decisive ECG parameters for diagnosing cardiac abnormalities among cardiologists. Hence, the efficiency of the developed automatic algorithm to detect the QRS onset is essential to obtain an accurate result of the ECG parameters. In this report, an algorithm to detect the QRS onset based on secant line slope formula is proposed. The preprocessing and wave delineation process were implemented in MATLAB using modified pan-tompkins algorithm (an established adaptive threshold method). The window of the preceding Q-wave was determined before calculating the slope of secant line along the descending slope for QRS onset detection. The performance of the proposed algorithm was evaluated using 25 subjects from Pusat Perubatan Universiti Kebangsaan Malaysia (PPUKM) and volunteered participants under the approval of Research and Ethics Committee, PPUKM (Code of ethics approval: FF-2013-313). All data were acquired using biosignal amplifier (*** by ***, Austria) with 2 minutes duration of recording and sampled at 512 Hz. The efficiency of the proposed algorithm has obtained a sensitivity of 99.67%, positive predictivity of 99.39%, and accuracy of 99.07%. The result shows stable performance and insensitivity of the proposed algorithm towards ECG wave morphology changes.

关键词： automatic ECG detection pan-tompkins algorithm QRS onset secant line slope

来源：评论

学校读者我要写书评

暂无评论

Computation-efficient and compact FPGA design for a real-time wearable arrhythmia-detector

引用

Biomedical Engineering Advances 2021年 2卷

作者： Ria Ghosh Lakshman S. Tamil Department of Computer Engineering University of Texas at Dallas Richardson 75080 USA Department of Electrical Engineering University of Texas at Dallas Richardson 75080 USA

The epidemic of diabetes, obesity and unhealthy lifestyles have highly contributed to increasing number of patients with heart problems. Wearable fitness trackers are not accurate enough in heart problem detection and the current software-based algorithms, when implemented in devices like smartwatches are not efficient in terms of hardware resource utilization and computational speed. To address these limitations, this paper proposes an automated heartbeat classifying hardware chip-design which can be placed in any kind of wearable device for real-time cardiac monitoring that would help to ensure early diagnosis of any kind of cardiac abnormality. The algorithm burnt on the hardware is a modification of the pan-tompkins beat-detection algorithm to which a novel classifier algorithm is added. It exhibits high computational speed with an accuracy of 99.65% in extremely noisy situations, when applied on the MIT/BIH arrhythmia database. The hardware utilization on the SPARTAN-6 FPGA for the presented design is just 32% allowing space for much more multi-tasking and upgrading to be done when implemented on a wearable device as an ASIC.

关键词： Cardiac arrhythmia Beat classification Butterworth filtering ECG sensors Hardware-utilization HDL Coder pan-tompkins algorithm Pre-ventricular contraction Pre-atrial contraction QRS complex VHLD SPARTAN-6

来源：评论

学校读者我要写书评

暂无评论

A Two-Stage Feature Extraction Approach for ECG Signals 3rd

A Two-Stage Feature Extraction Approach for ECG Signals

引用

3rd International Afro-European Conference for Industrial Advancement (AECIA)

作者： Houssein, Essam H. Kilany, Moataz Hassanien, Aboul Ella Snasel, Vaclav Minia Univ Fac Comp & Informat Al Minya Egypt Cairo Univ Fac Comp & Informat Cairo Egypt VSB TU Ostrava Dept CS & IT4Innovat Ostrava Czech Republic Sci Res Grp Egypt SRGE Cairo Egypt

ISBN: (纸本)9783319608341;9783319608334

This paper investigate various techniques of extracting features from the electrocardiogram (ECG) signal in order to analyze the ECG signals to detect the heart disease. Feature extraction, is a one of the widespread process of decompose the ECG data. This paper introduce a two-stage feature extraction approach to extract features from ECG signals for different types of arrhythmias. Firstly, Modified pan-Tomkins algorithm (MPTA) is implemented to remove noise and extract nine features. Then the proposed Improved Feature Extraction algorithm (IFEA) is applied to extract additionally ten different features from the ECG signal. The MIT-BIH arrhythmia database have been used to test the proposed approach. It is obvious from the results that the proposed approach shows a high classification in terms of the following four statistical measures: Accuracy (Ac) 98.37%, Recall 48.29%, Precision 43.91%, F Measure 45.31%, and Specificity (Sp) 93.30%, respectively.

关键词： ECG Feature extraction Wavelet transform pan-tompkins algorithm

来源：评论

学校读者我要写书评

暂无评论

You are touching me! A Monitoring Mechanism for Secure Key Generation

You are touching me! A Monitoring Mechanism for Secure Key G...

引用

作者： JIM BENGTSSON CHALMERS UNIVERSITY OF TECHNOLOGY

学位级别：硕士

The complexity of the technology we interact with in our day to day life increases, but the effort we want to spend managing these interactions does not. Secure com- munication is one of these interactions. This thesis proposes a design of a security protocol that generates symmetric cryptographic keys which can be used as a basis for secure connection between two people. The creation of the keys is based upon the two biometrics, ECG and EEG, and that the two people are physically touching. A proof of concept which depends on the ECG of two people has been implemented. Given the correct EEG hardware, we argue that this proof of concept could easily be extendable to a full system. Through statistical analysis on ECG measurements and experimentation, supporting algorithms have been implemented. The biggest contribution has been the creation of a touch detection algorithm, which can detect the moment when a person is touched, based on their ECG signal.

关键词： Biometric Electrocardiogram Electroencephalography Inter-Pulse Interval Cryptography Symmetric key pan-tompkins algorithm Continuous Wavelet Transform Microcontroller

来源：评论

学校读者我要写书评

暂无评论

A Fully Automatic Novel Method to Determine QT Interval Based on Continuous Wavelet Transform

引用

ISTANBUL UNIVERSITY-JOURNAL OF ELECTRICAL AND ELECTRONICS ENGINEERING 2017年第1期17卷 3092-3098页

作者： Yilmaz, Cuneyt Iscan, Mehmet Yilmaz, Abdurrahman Yildiz Tech Univ Mechatron Engn Dept Istanbul Turkey

Nowadays, automatic recognition algorithm is being frequently utilized to extract the information concerning cardiac abnormalities. In this study, a fully automatic novel method based on the continuous wavelet transform (CWT) was developed for QT intervals in various ECG signals. Especially, the determination of T-wave end is the paramount problem to be solved. The developed method was performed to find the beginning of QRS complexes and the end of T-wave. The proposed algorithm was tested on MIT-BIH-NSR database given by QT database, then, it yielded the scores 15.17 milliseconds and root-mean-square error of 17.19 milliseconds at silver standard, 19.22 milliseconds and 20.22 milliseconds at gold standard, respectively. In conclusion, the proposed algorithm is a fully automatic method to attain a high performance in the calculation of QT intervals at various ECG signals.

关键词： ECG Signal QT interval ECG signal classification pan-tompkins algorithm Continuous Wavelet Transform

来源：评论

学校读者我要写书评

暂无评论

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

请选择保存的检索档案：

请选择收藏分类：

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

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

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：