Development of analytical approach for an automated analysis of continuous long-term single lead ECG for diagnosis of paroxysmal atrioventricular block

Muammar M. Kabir, Larisa G. Tereshchenko

    Research output: Contribution to journalConference article

    3 Scopus citations

    Abstract

    Reliable detection of significant ECG features such as the P-wave, QRS-complex and T-wave are of major clinical importance, In this paper we introduce a new algorithm based on synchrosqueezing wavelet transform for detection of P-waves in long-term ECG recordings. Synchrosqueezing is a powerful time-frequency analysis tool that provides precise frequency representation of a multicomponent signal through mode decomposition. First, we analyzed four wavelet filters with different filter parameters, to identifY the best specification for quantification of QRS and P-wave. Second, the algorithm was tested on ECG recording comprising of events with paroxysmal atrioventricular block and validated through visual scanning. Using morlet wavelet with a peak frequency of 5Hz and separation of 0.1 Hz, our proposed algorithm was able to detect 95.5% of P-waves. From this study, it appears that synchrosqueezing wavelet transform may provide a powerful robust technique for automated ECG analysis.

    Original languageEnglish (US)
    Article number7043192
    Pages (from-to)913-916
    Number of pages4
    JournalComputing in Cardiology
    Volume41
    Issue numberJanuary
    StatePublished - Jan 1 2014
    Event41st Computing in Cardiology Conference, CinC 2014 - Cambridge, United States
    Duration: Sep 7 2014Sep 10 2014

    ASJC Scopus subject areas

    • Computer Science(all)
    • Cardiology and Cardiovascular Medicine

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