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Notice of Violation of IEEE Publication Principles
"Music Pseudo-Bispectrum Detects ECG Ischaemia,"
by Walid A. Zgallai,
in the 17th International Conference on Digital Signal Processing (DSP), July 2011
After careful and considered review of the content and authorship of this paper by a duly constituted expert committee, this paper has been found to be in violation of IEEE's Publication Principles.
This paper contains substantial duplication of original text from the paper cited below. The original text was copied without attribution (including appropriate references to the original author(s) and/or paper title) and without permission.
Due to the nature of this violation, reasonable effort should be made to remove all past references to this paper, and future references should be made to the following article:
"Application of Higher-order Statistics and Subspace Based Techniques to the Analysis and Diagnosis of Electrocardiogram Signals,"
by Sahar El-Khafif,
PhD thesis, School of Engineering, City University, UK, 2002
Up to 30% of patients with suspected or known coronary artery disease are unable to perform an adequate exercise stress test due to poor physical condition. It is beneficial to be able to detect ischaemic heart diseases when these do not manifest themselves as ST depression or elevation. In this paper, a subspace-based MUSIC algorithm is used to examine normal and abnormal episodes from the same patient. The analysis reveals abnormal peaks in both of these episodes as opposed to the frequency analysis of normal episodes taken from normal records. Results presented include 46 records from the MIT-BIH databases. High resolution is obtained using the MUSIC algorithm compared to the maximum entropy method (MEM). The accuracy, sensitivity and specificity of the proposed algorithm are 82.8%, 87% and 90% respectively. This leads to the possibility of the detection of ischaemia without the need for an ex- rcise test.