Key result
A probabilistic neural network approach combined with discrete wavelet transform and cross recurrence quantification analysis was presented for classifying ventricular tachyarrhythmias from ECG signals.
Why the study?
Arrhythmias are a leading cause of cardiovascular mortality, but direct evidence from clinical records has been lacking.
Does a probabilistic neural network approach accurately classify ventricular tachyarrhythmias from ECG signals?
Does a probabilistic neural network approach accurately classify ventricular tachyarrhythmias from ECG signals?
A probabilistic neural network approach combined with discrete wavelet transform and cross recurrence quantification analysis provides a methodology for classifying ventricular tachyarrhythmias from ECG signals.
Accurate observation of cardiac dysrhythmias are extremely important for clinical applications. Arrhythmias are one of the leading causes of cardiovascular mortality, direct evidences of clinical records has been lacking. Authors of this paper presents a unified approach for classifying ventricular tachyarrhythmias. The methodology adopted by the authors of this work are discrete wavelet transform (DWT) for extracting the features from ECG signals, cross recurrence quantification analysis (CRQA) for calculating the recurrent rate values using the cross recurrence plot (CRP) toolbox of Matlab and probabilistic neural network (PNN) concept for classification of ECG signals.
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Saraswat et al. (2018) studied Ventricular tachyarrhythmias. Probabilistic neural network (PNN) approach was evaluated on Classification of ECG signals. A probabilistic neural network approach combined with discrete wavelet transform and cross recurrence quantification analysis was presented for classifying ventricular tachyarrhythmias from ECG signals.
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