• Journal of Biomedical Engineering Research
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• v.17 no.4
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• pp.507-514
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• 1996

The automated ECG diagnostic systems in hospital have a low P-wave detection capacity in case of some diseases like conduction block. The purpose of this study is to improve the P-wave detection ca- pacity using wavelet transform. The first procedure is to remove baseline drift by subtracting the median filtered signal from the original signal. The second procedure is to cancel ECG's QRS-T complex from median filtered signal to get P-wave candidate. Before we subtracted the templete from QRS-T complex, we estimated the best matching between templete and QRS-T complex to minimize the error. Then, wavelet transform was applied to confirm P-wave. In particular, haiti wavelet was used to magnify P-wave that consisted of low frequency components and to reject high frequency noise of QRS-T complex cancelled signal. Finally, p-wave was discriminated and confirmed by threshold value. By using this method, We can got the around 95.1% P-wave detection. It was compared with contextual information.

• Journal of Biomedical Engineering Research
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• v.17 no.2
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• pp.235-240
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• 1996

The automated ECG diagnostic systems that are odd in hospitals have low performance of P-wave detection when faced with some diseases such as conduction block. So, the purpose of this study was the improvement of detection performance in conduction block which is low in P-wave detection. The first procedure was removal of baseline drift by subtracting the median filtered signal of 0.4 second length from the original signal. Then the algorithm detected R peak and T end point and cancelled the QRS-T complex to get'p prototypes'. Next step was magnification of P prototypes with dispersion and detection of'p candidates'in the magnified signal, and then extraction of contextual information concerned with P-waves. For the last procedure, the CIR was applied to P candidates to confirm P-waves. The rule base consisted of three rules that discriminate and confirm P-waves. This algorithm was evaluated using 500 patient's raw data P-wave detection perFormance was in- creased 6.8% compared with the QRS-T complex cancellation method without application of the rule base.