The time-frequency method combined with principal component analysis and a multilayer perceptron classifier achieved a 99.16% accuracy in predicting sudden cardiac death from one-minute ECG signals, compared to 74.36% for classical linear methods.
Case-Control (n=70)
Does a time-frequency method on ECG signals improve the detection of sudden cardiac death compared to classical linear techniques in subjects with and without SCD?
Time-frequency analysis of heart rate variability signals significantly improves the early detection and prediction of sudden cardiac death compared to classical linear methods.
Absolute Event Rate: 99.16% vs 74.36%
Early detection of sudden cardiac death may be used for surviving the life of cardiac patients. In this paper we have investigated an algorithm to detect and predict sudden cardiac death, by processing of heart rate variability signal through the classical and time-frequency methods. At first, one minute of ECG signals, just before the cardiac death event are extracted and used to compute heart rate variability (HRV) signal. Five features in time domain and four features in frequency domain are extracted from the HRV signal and used as classical linear features. Then the Wigner Ville transform is applied to the HRV signal, and 11 extra features in the time-frequency (TF) domain are obtained. In order to improve the performance of classification, the principal component analysis (PCA) is applied to the obtained features vector. Finally a neural network classifier is applied to the reduced features. The obtained results show that the TF method can classify normal and SCD subjects, more efficiently than the classical methods. A MIT-BIH ECG database was used to evaluate the proposed method. The proposed method was implemented using MLP classifier and had 74.36% and 99.16% correct detection rate (accuracy) for classical features and TF method, respectively. Also, the accuracy of the KNN classifier were 73.87% and 96.04%.
Ebrahimzadeh et al. (Sat,) conducted a case-control in Sudden Cardiac Death (n=70). Time-frequency (TF) method with PCA and MLP classifier vs. Classical linear features method was evaluated on Correct detection rate (accuracy) of sudden cardiac death using 1-minute ECG signals. The time-frequency method combined with principal component analysis and a multilayer perceptron classifier achieved a 99.16% accuracy in predicting sudden cardiac death from one-minute ECG signals, compared to 74.36% for classical linear methods.
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