An algorithm for automated detection of ischemic ECG beats using support vector machines

被引：0

作者：

Mohebbi, M. ^{[1
]}

Moghadam, H. A. ^{[1
]}

机构：

[1] KN Toosi Univ Technol, Elect Engn Dept, Tehran, Iran

来源：

2007 IEEE 15TH SIGNAL PROCESSING AND COMMUNICATIONS APPLICATIONS, VOLS 1-3 | 2007年

关键词：

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Cardiac beat classification is a key process in the detection of myocardial ischemia episodes in the electrocardiogram (ECG) signal. In this paper, we have developed a new method based on support vector machines for detection of ischemic ECG beats. The proposed method consists of a preprocessing stage for QRS detection, baseline wandering removal, noise suppression and ST segment extraction. In the next stage, the ST segment pattern is down-sampled and subtracted from the down-sampled normal template. In the third stage, the resulted patterns are used for training a support vector machine and ischemic beats are detected. To evaluate the algorithm, a cardiac beat data set is constructed using a number of recordings of the ESC ST-T database. The obtained sensitivity and positive predictivity were 92.13% and 90.34%, respectively. The proposed methodology presents better results than other approaches.

引用

页码：1256 / 1259

页数：4

共 50 条

[1] Classification of ECG beats Using Cross Wavelet Transform and Support Vector Machines
Jacob, Neenu
Joseph, Liza Annie
[J]. PROCEEDINGS OF THE 2015 IEEE RECENT ADVANCES IN INTELLIGENT COMPUTATIONAL SYSTEMS (RAICS), 2015, : 191 - 194
[2] Classification of ECG beats by using a fast least square support vector machines with a dynamic programming feature selection algorithm
Acir, N
[J]. NEURAL COMPUTING & APPLICATIONS, 2005, 14 (04): : 299 - 309
[3] Classification of ECG beats by using a fast least square support vector machines with a dynamic programming feature selection algorithm
Nurettin Acır
[J]. Neural Computing & Applications, 2005, 14 : 299 - 309
[4] ECG beats classification using multiclass support vector machines with error correcting output codes
Ubeyli, Elif Derya
[J]. DIGITAL SIGNAL PROCESSING, 2007, 17 (03) : 675 - 684
[5] Automated Pavement Patch Detection and Quantification Using Support Vector Machines
Hadjidemetriou, Georgios M.
Vela, Patricio A.
Christodoulou, Symeon E.
[J]. JOURNAL OF COMPUTING IN CIVIL ENGINEERING, 2018, 32 (01)
[6] ECG Arrhythmia Classification with Support Vector Machines and Genetic Algorithm
Nasiri, Jalal A.
Naghibzadeh, Mahmoud
Yazdi, H. Sadoghi
Naghibzadeh, Bahram
[J]. 2009 THIRD UKSIM EUROPEAN SYMPOSIUM ON COMPUTER MODELING AND SIMULATION (EMS 2009), 2009, : 187 - +
[7] Discriminating ECG signals using Support Vector Machines
Shahbudin, S.
Shamsudin, S. N.
Mohamad, H.
[J]. ISCAIE 2015 - 2015 IEEE SYMPOSIUM ON COMPUTER APPLICATIONS AND INDUSTRIAL ELECTRONICS, 2015, : 175 - 180
[8] ECG Delineation and Ischemic ST-Segment Detection Based in Wavelet Transform and Support Vector Machines
Bustamante, C. A.
Duque, S. I.
Orozco-Duque, A.
Bustamante, J.
[J]. 2013 PAN AMERICAN HEALTH CARE EXCHANGES (PAHCE), 2013,
[9] Morphological and Temporal ECG Features for Myocardial Infarction Detection Using Support Vector Machines
Arenas, Wilson J.
Sotelo, Silvia A.
Zequera, Martha L.
Altuve, Miguel
[J]. VIII LATIN AMERICAN CONFERENCE ON BIOMEDICAL ENGINEERING AND XLII NATIONAL CONFERENCE ON BIOMEDICAL ENGINEERING, 2020, 75 : 172 - 181
[10] Automated Epileptic Seizure Detection using Relevant Features in Support Vector Machines
Mitha, M.
Shiju, S. S.
Viswanadhan, Mithra
[J]. 2014 INTERNATIONAL CONFERENCE ON CONTROL, INSTRUMENTATION, COMMUNICATION AND COMPUTATIONAL TECHNOLOGIES (ICCICCT), 2014, : 1000 - 1004

← 1 2 3 4 5 →