Title
A Novel Wavelet Transform-Homogeneity Model for Sudden Cardiac Death Prediction Using ECG Signals.
Abstract
Sudden cardiac death (SCD) is one of the main causes of death among people. A new methodology is presented for predicting the SCD based on ECG signals employing the wavelet packet transform (WPT), a signal processing technique, homogeneity index (HI), a nonlinear measurement for time series signals, and the Enhanced Probabilistic Neural Network classification algorithm. The effectiveness and usefulness of the proposed method is evaluated using a database of measured ECG data acquired from 20 SCD and 18 normal patients. The proposed methodology presents the following significant advantages: (1) compared with previous works, the proposed methodology achieves a higher accuracy using a single nonlinear feature, HI, thus requiring low computational resource for predicting an SCD onset in real-time, unlike other methodologies proposed in the literature where a large number of nonlinear features are used to predict an SCD event; (2) it is capable of predicting the risk of developing an SCD event up to 20 min prior to the onset with a high accuracy of 95.8%, superseding the prior 12 min prediction time reported recently, and (3) it uses the ECG signal directly without the need for transforming the signal to a heart rate variability signal, thus saving time in the processing.
Year
DOI
Venue
2018
10.1007/s10916-018-1031-5
J. Medical Systems
Keywords
Field
DocType
Cardiology,Enhanced probabilistic neural network,Homogeneity analysis,Sudden cardiac death,Wavelet transform
Signal processing,Data mining,Homogeneity (statistics),Pattern recognition,Heart rate variability,Probabilistic neural network,Sudden cardiac death,Artificial intelligence,Wavelet packet decomposition,Medicine,Computational resource,Wavelet transform
Journal
Volume
Issue
ISSN
42
10
0148-5598
Citations 
PageRank 
References 
1
0.48
39
Authors
4