Adaptive Wavelet-Transform-Based ECG Waveforms Detection

S. M. Szilágyi, Z. Benyó, L. Szilágyi, L. Dávid

Research output: Contribution to journalConference article

11 Citations (Scopus)


A Wavelet-transform-based diverse ECG waveform detection method is presented. An adaptive structure of the processing 2407algorithm can significantly increase the recognition ratio. As a first step, the program will correctly determine the position of QRS complexes and will separate the normal and abnormal beats. Our method allows us to modify in real time the mother-wavelet function, and in this way can be customized to an individual subject or specific waveforms. A parametrical model determines the best performing function for a specific waveform. We used our measurements, but for an adequate comparison with other processing algorithms, tests have been made for the commonly used MIT-BIH database, too. To allow greater waveform diversity we also used our measurements. QRS detection rate was above 99.9%, and for other waveforms the method performs quite well too. The negative influence of various noise types, like 50/60 Hz power line, abrupt baseline shift or drift, and low sampling rate in most cases was almost completely eliminated.

Original languageEnglish
Pages (from-to)2412-2415
Number of pages4
JournalAnnual International Conference of the IEEE Engineering in Medicine and Biology - Proceedings
Publication statusPublished - Dec 1 2003
EventA New Beginning for Human Health: Proceedings of the 25th Annual International Conference of the IEEE Engineering in Medicine and Biology Society - Cancun, Mexico
Duration: Sep 17 2003Sep 21 2003


  • Adaptive detection and processing
  • ECG
  • QRS
  • Wavelets

ASJC Scopus subject areas

  • Signal Processing
  • Biomedical Engineering
  • Computer Vision and Pattern Recognition
  • Health Informatics

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