Paper Info
Title
A robust knowledge-based technique for ischemia detection in noisy ECGs
Abstract
In cases where the signal-to-noise ratio (SNR) in ECGs is very poor the correct definition of characteristics as the isoelectric line and the J-point (beginning of the ST segment) is difficult. Inaccurate definition of those ECG characteristics can lead an automated ischemia detector to an incorrect diagnosis. We propose a method capable of extracting from noisy long duration ECG recordings those ECG characteristics that can be used for myocardial ischemia detection and analysis. We tested the performance of the method using noisy ECGs from the European Society of Cardiology ST-T database (ESC ST-T database). The results were more than satisfactory and the performance of our ischemia defector was improved in all cases. The proposed technique has low computational effort and can be executed in real time.
Year
DOI
Venue
2000
10.1109/KES.2000.884159
KES
Keywords
Field
DocType
electrocardiography,medical expert systems,medical signal processing,real-time systems,signal detection,ECG characteristics,ECGs,ESC ST-T database,European Society of Cardiology ST-T database,J-point,SNR,ST segment,automated ischemia detector,computational effort,incorrect diagnosis,ischemia detection,isoelectric line,myocardial ischemia detection,noisy ECGs,noisy long duration ECG recordings,real time detection,robust knowledge based technique,signal-to-noise ratio
ST segment,Pattern recognition,Detection theory,Computer science,Signal-to-noise ratio,Ischemia,Ischemic pain,Robustness (computer science),Artificial intelligence,Electrocardiography,Detector
Conference
Volume
Citations 
PageRank 
2
2
0.60
References 
Authors
1
5
Name
Order
Citations
PageRank
Costas Papaloukas125516.43
Dimitrios I. Fotiadis2941121.32
aristidis likas31926140.40
Athanasios P. Liavas417816.11
Lampros K. Michalis517721.89