ECG pattern recognition and classification using non-linear transformations and neural networks: A review

• Published in: International journal of medical informatics Vol. 52; no. 1; pp. 191 - 208
• Main Authors: Maglaveras, Nicos, Stamkopoulos, Telemachos, Diamantaras, Konstantinos, Pappas, Costas, Strintzis, Michael
• Format: Journal Article Conference Proceeding
• Language: English
• Published: Shannon Elsevier Ireland Ltd 01.10.1998; Elsevier
• Subjects: Algorithms; Atrial Fibrillation - diagnosis; Biological and medical sciences; Biomedical signal processing; ECG processing; Electrocardiography; Electrocardiography. Vectocardiography; Electrodiagnosis. Electric activity recording; Humans; Investigative techniques, diagnostic techniques (general aspects); Ischemia detection; Medical sciences; Myocardial Ischemia - diagnosis; Neural networks; Neural Networks (Computer); Non-linear principal component analysis; Pattern Recognition, Automated; Sensitivity and Specificity; Signal Processing, Computer-Assisted; Non-linear principal component analysis; Ischemia detection; Neural networks; ECG processing; Biomedical signal processing; Human; Pattern recognition; Neural network; Non linear transformation; Electrodiagnosis; Biomedical data processing; Classification; Electrocardiography; Myocardium; Circulatory system; Diagnosis; Signal analysis; Principal component analysis
• ISSN: 1386-5056; 1872-8243
• DOI: 10.1016/S1386-5056(98)00138-5

The most widely used signal in clinical practice is the ECG. ECG conveys information regarding the electrical function of the heart, by altering the shape of its constituent waves, namely the P, QRS, and T waves. Thus, the required tasks of ECG processing are the reliable recognition of these waves, and the accurate measurement of clinically important parameters measured from the temporal distribution of the ECG constituent waves. In this paper, we shall review some current trends on ECG pattern recognition. In particular, we shall review non-linear transformations of the ECG, the use of principal component analysis (linear and non-linear), ways to map the transformed data into n-dimensional spaces, and the use of neural networks (NN) based techniques for ECG pattern recognition and classification. The problems we shall deal with are the QRS/PVC recognition and classification, the recognition of ischemic beats and episodes, and the detection of atrial fibrillation. Finally, a generalised approach to the classification problems in n-dimensional spaces will be presented using among others NN, radial basis function networks (RBFN) and non-linear principal component analysis (NLPCA) techniques. The performance measures of the sensitivity and specificity of these algorithms will also be presented using as training and testing data sets from the MIT-BIH and the European ST-T databases.