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Description: 数学形态学用于技术分析ECG心律的探测，很不错的-mathematical morphology for technical analysis of the ECG arrhythmia detection, very good
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Size: 2991 |
Author: zhang Fei |
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Description: 数学形态学用于技术分析ECG心律的探测，很不错的-mathematical morphology for technical analysis of the ECG arrhythmia detection, very good
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Size: 3072 |
Author: zhang Fei |
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Description: 这是关于心电的文章与源码，希望与大家共同分享。-This is about the heart of the article with the electric source, I hope to share with everyone.
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Author: 刘实 |
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Description: 心电信号在线数据知识化辅助诊断算法研究
摘 要:针对心电监护与诊断过程中数据量大、准确性和快速性要求高的特点,提出了一套基于数据知识化的心电
辅助诊断算法.该套算法包括数据识别、冗余处理、转换和提取过程,利用小波变换的多分辨率和抗干扰能力好的
特点,检测QRS波、P波、T波,提高了特征检测的准确性 利用聚类分析具有较好的鲁棒性和适合于大数据量分析
的特点,对QRS进行波形分类 算法结合了单独一搏诊断和串诊断以及多参数综合分析.采用MIT-BIH标准心电
数据库中的部分数据和心电专家确诊的心律失常数据文件对该算法进行了评估,检出率都在95 以上,表明该套
算法对部分心律失常可以进行有效分析.
-A series of data knowledge discovery based electrocardiograph (ECG) auxiliary diagnosis algo-
rithms were presented against the characteristics of huge data quantum, high accuracy and rapidity de-
mands in the ECG monitor and diagnosis process. The algorithm consists of several stages, including data
distinguishing, data redundant processing, data conversion and data extraction. The characteristics of
wavelet transform, multiresolution and high anti-interference, were used to detect QRS, P and T waves
and improve the accuracy of character detection. Clustering analysis characterized by better robustness and
capability to analyze huge data quantum was used to classify QRS wave. The algorithm combines diagnosis
based on one beat, string diagnosis and comprehensive analysis with multiparameters. Verified by partial
data of MIT-BIH standard ECG database and arrhythmia data files diagnosed by ECG experts, the detect-
ability exceeded 95 , which showed that the algorithm could analy
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Author: Shi |
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Description: 用来读取MIT-BIH（http://www.physionet.org/physiobank/database/mitdb/）心电数据库的MATLAB程序--M file used to read MIT-BIH Arrhythmia Database
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Size: 2048 |
Author: 赵阳 |
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Description: 来自MIT-BIH数据库(http://www.physionet.org/physiobank/database/mitdb/)的异常心电数据-Arrhythmia Data from MIT-BIH(http://www.physionet.org/physiobank/database/mitdb/)
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Author: 赵阳 |
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Description: MIT-BIH心律失常数据库数据读取matlab程序-MIT-BIH Arrhythmia Database matlab program to read data
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Author: chenjunhong |
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Description: 心电信号的计算机分析
通过理论结合实际，用C语言编程对MIT心电信号数据进行分析，实现低通滤波、高通滤波、QRS检测、特征提取、心律失常分析，从中了解和掌握数字信号处理的方法和应用。
-ECG computer analysis by the theory with reality, and C language programming on the MIT ECG data analysis, low-pass filter, high pass filtering, QRS detection, feature extraction, arrhythmia analysis, learn about and master the digital signal processing methods and applications.
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Author: 焦春雨 |
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Description: 实现心电监测系统，实现心律失常报警，包括心率过快和心率过慢。-Achieve ECG monitoring system, to achieve the alarm arrhythmia, including rapid heart rate and heart rate is too slow.
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Author: 解玉仙 |
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Description: 读入mit-bih心律失常数据库的文件，并根据读取文件的数据画出心脏跳动波形图-Read into the mit-bih arrhythmia database files, and read the file according to the data to draw the heart beat waveform
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Author: 肖威 |
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Description: The early detection of arrhythmia is very important
for the cardiac patients. This done by analyzing the
electrocardiogram (ECG) signals and extracting some features
from them. These features can be used in the classification of
different types of arrhythmias. In this paper, we present three
different algorithms of features extraction: Fourier transform
(FFT), Autoregressive modeling (AR), and Principal Component
Analysis (PCA). The used classifier will be Artificial Neural
Networks (ANN). We observed that the system that depends on
the PCA features give the highest accuracy. The proposed
techniques deal with the whole 3 second intervals of the training
and testing data. We reached the accuracy of 92.7083
compared to 84.4 for the reference that work on a similar
data.-The early detection of arrhythmia is very important
for the cardiac patients. This is done by analyzing the
electrocardiogram (ECG) signals and extracting some features
from them. These features can be used in the classification of
different types of arrhythmias. In this paper, we present three
different algorithms of features extraction: Fourier transform
(FFT), Autoregressive modeling (AR), and Principal Component
Analysis (PCA). The used classifier will be Artificial Neural
Networks (ANN). We observed that the system that depends on
the PCA features give the highest accuracy. The proposed
techniques deal with the whole 3 second intervals of the training
and testing data. We reached the accuracy of 92.7083
compared to 84.4 for the reference that work on a similar
data.
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Size: 273408 |
Author: Amit Majumder |
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Description: MIT-BIH Arrhythmia ecg , you can see this record frorm 1977 ,female-MIT-BIH Arrhythmia ecg , you can see this record frorm 1977 ,female
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Size: 90112 |
Author: ahmed bataineh |
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Description: Cardiac arrhythmia diagnosis method using linear discriminant analysis on ECG signals
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Size: 45056 |
Author: alishah |
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Description: Quantitative Investigation of QRS Detection Rules Using the MIT/BIH Arrhythmia Database
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Author: zoll |
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Description: The electrocardiogram (ECG) is a well known method that can be used to measure Heart Rate Variability (HRV). This paper
describes a procedure for processing electrocardiogram signals (ECG) to detect Heart Rate Variability (HRV). In recent years, there
have been wide-ranging studies on Heart rate variability in ECG signals and analysis of Respiratory Sinus Arrhythmia (RSA).
Normally the Heart rate variability is studied based on cycle length variability, heart period variability, RR variability and RR
interval tachogram.
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Author: jeeva |
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Description: Detection of life threatening Arrhythmias are important for successful defibrillation therapy.
This paper provides an idea about arrhythmia detection based on surface ECG analysis.
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Author: NABINA N RAWTHER |
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Description: Detection of life threatening Arrhythmias are important for successful defibrillation therapy.
This paper provides an idea about arrhythmia detection based on surface ECG analysis.
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Size: 150528 |
Author: NABINA N RAWTHER |
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Description: Detection of life threatening Arrhythmias are important for successful defibrillation therapy.
This paper provides an idea about arrhythmia detection based on surface ECG and PCG analysis.
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Size: 1542144 |
Author: NABINA N RAWTHER |
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Description: Detection of life threatening Arrhythmias are important for successful defibrillation therapy.
This paper provides an idea about arrhythmia detection based on surface ECG analysis.
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Size: 180224 |
Author: NABINA N RAWTHER |
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Description: 心律失常是一种常见的内科疾病，会对患者的生活及健康造成严重影响。当心律失常发生时，心房和心室收缩程序改变，能使心排血量下降30 左右，从而使血液循环失常。因此，对心律失常进行早期诊断具有重要的临床意义,而利用心音信号识别算法可以快速识别心律失常的病例,有助于提高诊断速度。
-Arrhythmia is a common medical diseases, can cause serious influence to the patient s life and health. When cardiac arrhythmias occur, atrial and ventricular systolic program change, can make the cardiac output fell by around 30 , which makes the circulation of the blood disorder. Early diagnosis of arrhythmia, therefore, has important clinical significance, and the use of heart sounds signal recognition algorithm can quickly identify arrhythmia cases, helps to improve the diagnosis speed.
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Size: 78848 |
Author: 温嘉杰 |
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