A novel method for beat-to-beat detection of ventricular late potentials (VLP) from high-resolution electrocardiograms (ECGs) is presented. ECG signals from the X lead are first filtered using a bandpass filter, and then a time-sequence adaptive filter, to improve its signal-to-noise ratio. Eight features are extracted using the wavelet transform, from the VLP time-frequency distribution of the filtered ECG signals, and used as inputs of specially designed artificial neural network for VLP recognition. The artificial neural network was trained and tested using clinical data, respectively. The results show that the presented method can detect beat-to-beat-based VLP with sensitivity of 80% and specificity of 77%, and the detection accuracy is 78%.
['Shuicai Wu', 'Yongxian Qian', 'Zhiyong Gao', 'Jiarui Lin']
A novel method for beat-to-beat detection of ventricular late potentials