Key result
A novel artificial neural network-based method detected beat-to-beat ventricular late potentials with a sensitivity of 80%, specificity of 77%, and overall accuracy of 78%.
Why the study?
Does a novel artificial neural network-based method accurately detect beat-to-beat ventricular late potentials from high-resolution ECGs?
Does a novel artificial neural network-based method accurately detect beat-to-beat ventricular late potentials from high-resolution ECGs?
A novel artificial neural network-based method using wavelet transform can detect beat-to-beat ventricular late potentials from high-resolution ECGs with 78% accuracy.
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 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%.
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Wu et al. (2001) studied Ventricular late potentials. Artificial neural network-based method for beat-to-beat detection of VLP was evaluated on Detection accuracy, sensitivity, and specificity of VLP. A novel artificial neural network-based method detected beat-to-beat ventricular late potentials with a sensitivity of 80%, specificity of 77%, and overall accuracy of 78%.
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