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 Platform: |
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 Platform: |
Size: 3072 |
Author:zhang Fei |
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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 Platform: |
Size: 68608 |
Author:Shi |
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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. Platform: |
Size: 2909184 |
Author:焦春雨 |
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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. Platform: |
Size: 273408 |
Author:Amit Majumder |
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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. Platform: |
Size: 863232 |
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. Platform: |
Size: 150528 |
Author:NABINA N RAWTHER |
Hits:
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. Platform: |
Size: 1542144 |
Author:NABINA N RAWTHER |
Hits:
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. Platform: |
Size: 180224 |
Author:NABINA N RAWTHER |
Hits:
Search - arrhythmia detection