Name
Title | ||
|---|---|---|
An association rule mining-based methodology for automated detection of ischemic ECG beats. |
Abstract | ||
|---|---|---|
Currently, an automated methodology based on association rules is presented for the detection of ischemic beats in long duration electrocardiographic (ECG) recordings. The proposed approach consists of three stages. 1) Preprocessing: Noise is removed and all the necessary ECG features are extracted. 2) Discretization: The continuous valued features are transformed to categorical. 3) CLASSIFICATION: An association rule extraction algorithm is utilized and a rule-based classification model is created. According to the proposed methodology, electrocardiogram (ECG) features extracted from the ST segment and the T-wave, as well as the patient's age, were used as inputs. The output was the classification of the beat as ischemic or not. Various algorithms were tested both for discretization and for classification using association rules. To evaluate the methodology, a cardiac beat dataset was constructed using several recordings of the European Society of Cardiology ST-T database. The obtained sensitivity (Se) and specificity (Sp) was 87% and 93%, respectively. The proposed methodology combines high accuracy with the ability to provide interpretation for the decisions made, since it is based on a set of association rules. |
Year | DOI | Venue |
|---|---|---|
2006 | 10.1109/TBME.2006.873753 | IEEE transactions on bio-medical engineering |
Keywords | Field | DocType |
sensitivity,electrocardiography,medical signal detection,automated ischemic beat detection,noise,rule-based classification,medical signal processing,cardiac beat dataset,feature extraction,t-wave,st segment,signal preprocessing,electrocardiographic recordings,signal classification,association rule mining,data mining,association rules,automated ischemic ecg beat detection,discretization,specificity,patient age | Data mining,Discretization,Pattern recognition,Computer science,Extraction algorithm,Categorical variable,Feature extraction,Association rule learning,Preprocessor,Artificial intelligence,Signal classification,Beat (music) | Journal |
Volume | Issue | ISSN |
53 | 8 | 0018-9294 |
Citations | PageRank | References |
23 | 1.03 | 26 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Themis P Exarchos | 1 | 30 | 2.63 |
| Costas Papaloukas | 2 | 255 | 16.43 |
| Dimitrios I. Fotiadis | 3 | 941 | 121.32 |
| Lampros K Michalis | 4 | 23 | 2.05 |