A novel ECG diagnostic system for the detection of 13 different diseases. (January 2022)

• Record Type: Journal Article
• Authors: Monedero, Iñigo
• Is Part Of: Engineering applications of artificial intelligence. Volume 107(2022)
• Publication Date: 2022-01
• Subjects: Electrocardiogram -- Expert system -- Wavelet transform -- Decision tree; Engineering -- Data processing -- Periodicals; Artificial intelligence -- Periodicals; Expert systems (Computer science) -- Periodicals; Ingénierie -- Informatique -- Périodiques; Intelligence artificielle -- Périodiques; Systèmes experts (Informatique) -- Périodiques; Artificial intelligence; Engineering -- Data processing; Expert systems (Computer science); Periodicals; 620.00285
• DOI: 10.1016/j.engappai.2021.104536
• Languages: English
• ISSNs: 0952-1976

Abstract

Abstract: Manual analysis of electrocardiogram (ECG) signals is a laborious and prone-to-error task, even for a specialist with many hours of experience. For this reason, research on automatic ECG diagnosis is widespread in the literature and continues to grow each year. The present paper describes a novel and fully functional expert system for automatic diagnosis of 13 different diseases using standard 12-lead ECGs. This system makes three significant contributions to the state of the art: (a) the large number of different diseases diagnosed; (b) the use of 5 leads for a more precise identification and measurement of the ECG waves; and (c) a novel noise indicator that measures the quality of the acquired ECG signal. The kernel of the system consists of a set of rules that replicate a specialist's diagnostic process but with the speed of an automatic system. The rules use a set of parameters generated after a noise-filtering process of the ECG signal and subsequent identification of its different waves (P, QRS complex, T, and Delta). The design of the rules was carried out with the collaboration of a specialist with more than 20 years of experience in ECG diagnosis and using a database of 284, 000 ECGs as support. The system was validated by the specialist, obtaining a reliability of 80.8%. Given the complexity of the problem and the number of diagnoses covered, the results are considered satisfactory and make the system a useful support tool for diagnosis.