Abstract
Ventricular Late Potentials (VLPs) are lowamplitude, high-frequency signals that appear at the end of the QRS complex of a High-Resolution ECG (HRECG) records. VLPs are clinically useful for identifying post-MI (Myocardial Infarction) patients prone to Ventricular Tachycardia (VT) and Sudden Cardiac Death (SCD). In this paper, the Continuous Wavelet Transform (CWT) and a supervised fuzzy clustering algorithm are used together to detect VLPs. The terminal part of the QRS complex in the Vector Magnitude (VM) waveform is processed with the CWT to extract a feature vector. Resulting time-scale representation is subdivided into several sub bands, and the sum of the squared decomposition coefficients is computed in each region. Finally, a supervised Fuzzy clustering method, trained by an appropriate set of these feature vectors, is applied to this data in order to identify VLP.
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Jafari, A., Morradi, M. (2007). Using Supervised Fuzzy Clustering and CWT for Ventricular Late Potentials (VLP) Detection in High-Resolution ECG Signal. In: Jarm, T., Kramar, P., Zupanic, A. (eds) 11th Mediterranean Conference on Medical and Biomedical Engineering and Computing 2007. IFMBE Proceedings, vol 16. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-73044-6_26
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DOI: https://doi.org/10.1007/978-3-540-73044-6_26
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