.png){kind=icon}
返回
Current methods in electrocardiogram characterization
.png){kind=icon}
PRE
AI
DOI：10.1016/j.compbiomed.2014.02.012.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
The Electrocardiogram (ECG) is the P-QRS-T wave depicting the cardiac activity of the heart. The subtle changes in the electric potential patterns of repolarization and depolarization are indicative of the disease afflicting the patient. These clinical time domain features of the ECG waveform can be used in cardiac health diagnosis. Due to the presence of noise and minute morphological parameter values, it is very difficult to identify the ECG classes accurately by the naked eye. Various computer aided cardiac diagnosis (CACD) systems, analysis methods, challenges addressed and the future of cardiovascular disease screening are reviewed in this paper. Methods developed for time domain, frequency transform domain, and time-frequency domain analysis, such as the wavelet transform, cannot by themselves represent the inherent distinguishing features accurately. Hence, nonlinear methods which can capture the small variations in the ECG signal and provide improved accuracy in the presence of noise are discussed in greater detail in this review. A CACD system exploiting these nonlinear features can help clinicians to diagnose cardiovascular disease more accurately. (C) 2014 Elsevier Ltd. All rights reserved.
Keyword:
Electrocardiogram
Cardiovascular diseases (CVD)
Arrhythmia
Computer aided cardiac diagnosis (CACD)
Transform domain techniques
Non-linear methods
Wavelets
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
6.3
论文数:
8.3K
被引数:
3.3W
机构
引用论文
.png){kind=icon}
Application of principal component analysis to ECG signals for automated diagnosis of cardiac health
PREAI
分享收藏分享收藏
查看更多内容