Nonlinear Trend Estimation of the Ventricular Repolarization Segment for T-Wave Alternans Detection

• Published in: IEEE transactions on biomedical engineering Vol. 57; no. 10; pp. 2402 - 2412
• Main Authors: Blanco-Velasco, Manuel, Cruz-Roldán, Fernando, Godino-Llorente, Juan Ignacio, Barner, Kenneth E.
• Format: Journal Article
• Language: English
• Published: New York, NY IEEE 01.10.2010; Institute of Electrical and Electronics Engineers; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Applied sciences; Biological and medical sciences; Computer Simulation; Computerized, statistical medical data processing and models in biomedicine; Data mining; Databases, Factual; ECG; Electrocardiography; Electrocardiography - methods; Electrocardiography. Vectocardiography; Electrodiagnosis. Electric activity recording; empirical-mode decomposition (EMD); Exact sciences and technology; Frequency; Heart attacks; Heart Ventricles - physiopathology; Hjorth descriptors; Humans; Information, signal and communications theory; Investigative techniques, diagnostic techniques (general aspects); Medical management aid. Diagnosis aid; Medical sciences; Miscellaneous; Models, Cardiovascular; Noise robustness; Nonlinear Dynamics; repolarization; Samarium; Signal processing; Signal Processing, Computer-Assisted; Stress; T-wave alternans (TWA); Telecommunications and information theory; Testing; Working environment noise; Human; Repolarization; Empirical mode decomposition; empirical-mode decomposition (EMD); Decomposition method; Empirical method; ECG; Wave detection; Hjorth descriptors; Electrodiagnosis; T wave; Electrocardiography; Signal processing; T-wave alternans (TWA); Biomedical engineering
• ISSN: 0018-9294; 1558-2531
• DOI: 10.1109/TBME.2010.2048109

Repolarization alternans or T-wave alternans (TWA) is a subject of great interest as it has been shown as a risk stratifier for sudden cardiac death. As TWA consists of subtle and nonvisible variations of the ST-T complex, its detection may become more difficult in noisy environments, such as stress testing or Holter recordings. In this paper, a technique based on the empirical-mode decomposition (EMD) to separate the useful information of the ST-T complex from noise and artifacts is proposed. The identification of the useful part of the signal is based on the study of complexity in the EMD domain by means of the Hjorth descriptors. As a result, a robust technique to extract the trend of the ST-T complex has been achieved. The evaluation of the method is carried out with the spectral method (SM) over several public domain databases with ECGs sampled at different frequencies. The results show that the SM with the proposed technique outperforms the traditional SM by more than 2 dB. Also, the robustness of this technique is guaranteed as it does not introduce any additional distortion to the detector in noiseless conditions.