Adaptive wavelet-transform-based ECG waveforms detection

A Wavelet-transform-based diverse ECG waveform detection method is presented. An adaptive structure of the processing algorithm 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.