Figure 1.
Block diagram of the proposed approach for prediction of SCD.
Figure 2.
The ECG signal of SCD patient, from 2 minute before SCD event and several seconds after that.
Figure 3.
ECG signal of a person on the moment of heart death.
Figure 4.
Noise reduction of a typical ECG signal.
Figure 5.
Extraction of HRV from ECG signal.
(a) One minute of the ECG signal of a healthy person. (b) Extraction of QRS-complexes. (c) The HRV signal which was extracted from (a). (d) One minute the ECG signal of a patient just before occurrence of SCD. (e) Extraction of QRS-complexes. (f) The HRV signal which was extracted from (d).
Figure 6.
Spatial distribution of mean and STD features.
Figure 7.
HRV signal and it’s power spectral density.
(a) Extracted HRV signal. (b) PSD of HRV signal, power in each frequency band is indicated.
Figure 8.
Wigner Ville transform of the HRV signal.
(a) 2D view of a subject(b) 3D view of another subject.
Figure 9.
Poincaré plot.
Figure 10.
(a) Normal persons. (b) SCD Persons.
Table 1.
Comparison of the thirteen extracted HRV parameters from control, and SCD dataset.
Table 2.
The accuracy of MLP and k-NN classifiers with the selected features subsets (individual and combinational) for Healthy and patients prone to SCD.
Figure 11.
Prediction of SCD by classification accuracy 4 minutes before ventricular failure (VF).
(a) ECG signal of a patient prone to SCD. (b) Prediction of SCD by computing the percentage of event probability.
Table 3.
Run the program 16 times for First one minute by means of MLP classifier and composition feature vector.
Table 4.
Run the program 16 times for Second one minute by means of MLP classifier and composition feature vector.
Table 5.
Run the program 16 times for Third one minute by means of MLP classifier and composition feature vector.
Table 6.
Run the program 16 times for Forth one minute by means of MLP classifier and composition feature vector.
Table 7.
Accuracy, Sensitivity, Specificity, and Precision measures for all intervals before SCD.
Table 8.
Average of separating percent between healthy person and patients prone to SCD, 4minute before incident, by means of composition vector motion method.
Table 9.
Predictive accuracy for the proposed method and Shen’s method [18] (2-minute analysis).