Quantification of Diagnostic Information from Electrocardiogram Signal: A Review

被引:13
作者
Dandapat, S. [1 ]
Sharma, L. N. [1 ]
Tripathy, R. K. [1 ]
机构
[1] Indian Inst Technol Guwahati, Gauhati 781039, India
来源
ADVANCES IN COMMUNICATION AND COMPUTING | 2015年 / 347卷
关键词
Diagnostic measure; Multichannel ECG; Multivariate sample entropy; MSD; PRD; WEDD; ECG DATA-COMPRESSION; DISCRETE WAVELET TRANSFORM; SAMPLE ENTROPY; QUALITY; CLASSIFICATION; PCA; FIBRILLATION; ALGORITHM; ICA; LDA;
D O I
10.1007/978-81-322-2464-8_2
中图分类号
TP301 [理论、方法];
学科分类号
081202 ;
摘要
Electrocardiogram (ECG) contains the information about the contraction and relaxation of heart chambers. This diagnostic information will change due to various cardiovascular diseases. This information is used by a cardiologist for accurate detection of various life-threatening cardiac disorders. ECG signals are subjected to number of processing, for computer aided detection and localization of cardiovascular diseases. These processing schemes are categorized as filtering, synthesis, compression and transmission. Quantifying diagnostic information from an ECG signal in an efficient way, is always a challenging task in the area of signal processing. This paper presents a review on state-of-art diagnostic information extraction approaches and their applications in various ECG signal processing schemes such as quality assessment and cardiac disease detection. Then, a new diagnostic measure for multilead ECG (MECG) is proposed. The proposed diagnostic measure (MSD) is defined as the difference between multivariate sample entropy values for original and processed MECG signals. The MSD measure is evaluated over MECG compression framework. Experiments are conducted over both normal and pathological MECG from PTB database. The results demonstrate that the proposed MSD measure is effective in quantifying diagnostic information in MECG. The MSD measure is also compare with other measures such as WEDD, PRD and RMSE.
引用
收藏
页码:17 / 39
页数:23
相关论文
共 46 条
[41]   ECG Analysis Using Multiple Instance Learning for Myocardial Infarction Detection [J].
Sun, Li ;
Lu, Yanping ;
Yang, Kaitao ;
Li, Shaozi .
IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING, 2012, 59 (12) :3348-3356
[42]  
Sundnes J, 2007, COMPUTING ELECT ACTI, V1, DOI DOI 10.1007/3-540-33437-8
[43]   ECG DATA-COMPRESSION BY CORNER DETECTION [J].
TAI, SC .
MEDICAL & BIOLOGICAL ENGINEERING & COMPUTING, 1992, 30 (06) :584-590
[44]  
Thaler M.S., 2010, The Only Ekg Book You'll Ever Need, V365
[45]   Application of Kernel Principal Component Analysis for Single-Lead-ECG-Derived Respiration [J].
Widjaja, Devy ;
Varon, Carolina ;
Dorado, Alexander Caicedo ;
Suykens, Johan A. K. ;
Van Huffel, Sabine .
IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING, 2012, 59 (04) :1169-1176
[46]   The weighted diagnostic distortion (WDD) measure for ECG signal compression [J].
Zigel, Y ;
Cohen, A ;
Katz, A .
IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING, 2000, 47 (11) :1422-1430