Optimization of ECG Classification by Means of Feature Selection

• Published in: IEEE transactions on biomedical engineering Vol. 58; no. 8; pp. 2168 - 2177
• Main Authors: Mar, Tanis, Zaunseder, Sebastian, Martínez, Juan Pablo, Llamedo, Mariano, Poll, Rüdiger
• Format: Journal Article
• Language: English
• Published: New York, NY IEEE 01.08.2011; Institute of Electrical and Electronics Engineers
• Subjects: Accuracy; Algorithms; Applied sciences; Arrhythmias, Cardiac - diagnosis; Arrhythmias, Cardiac - physiopathology; Artificial Intelligence; Artificial neural networks; Association for the Advancement of Medical Instrumentation (AAMI); Biological and medical sciences; classification; Computer science; control theory; systems; Computerized, statistical medical data processing and models in biomedicine; Connectionism. Neural networks; Diagnosis, Computer-Assisted - methods; ECG; Electrocardiography; Electrocardiography - methods; Electrocardiography. Vectocardiography; Electrodiagnosis. Electric activity recording; Exact sciences and technology; feature selection (FS); Heart beat; Humans; Indexes; Investigative techniques, diagnostic techniques (general aspects); linear discriminant analysis (LDA); Medical management aid. Diagnosis aid; Medical sciences; multilayer perceptron (MLP); Pattern Recognition, Automated - methods; Reproducibility of Results; Sensitivity; Sensitivity and Specificity; sequential forward floating search (SFFS); Training; Discriminant analysis; feature selection (FS); sequential forward floating search (SFFS); ECG; Instrumentation; Neural network; classification; Optimization; multilayer perceptron (MLP); Electrodiagnosis; Association for the Advancement of Medical Instrumentation (AAMI); Electrocardiography; Signal processing; linear discriminant analysis (LDA); Biomedical engineering
• ISSN: 0018-9294; 1558-2531; 1558-2531
• DOI: 10.1109/TBME.2011.2113395

This study tackles the ECG classification problem by means of a methodology, which is able to enhance classification performance while simultaneously reducing the computational resources, making it specially adequate for its application in the improvement of ambulatory settings. For this purpose, the sequential forward floating search (SFFS) algorithm is applied with a new criterion function index based on linear discriminants. This criterion has been devised specifically to be a quality indicator in ECG arrhythmia classification. Based on this measure, a comprehensive feature set is analyzed with the SFFS algorithm, and the most suitable subset returned is additionally evaluated with a multilayer perceptron (MLP) to assess the robustness of the model. Aiming at obtaining meaningful estimates of the real-world performance and facilitating comparison with similar studies, the present contribution follows the Association for the Advancement of Medical Instrumentation standard EC57:1998 and the same interpatient division scheme used in several previous studies. Results show that by applying the proposed methods, the performance obtained in similar studies under the same constraints can be exceeded, while keeping the requirements suitable for ambulatory monitoring.