The combination of Time-Frequency and Nonlinear features extracted from HRV signals predicted sudden cardiac death with an accuracy of 99.73% one minute before the event.
Case-Control (n=70)
Does a prediction model using combined Time-Frequency and Nonlinear features from HRV signals accurately predict sudden cardiac death in at-risk patients?
A machine learning approach combining time-frequency and nonlinear HRV features can predict sudden cardiac death with high accuracy up to 4 minutes before occurrence.
Investigations show that millions of people all around the world die as the result of sudden cardiac death (SCD). These deaths can be reduced by using medical equipment, such as defibrillators, after detection. We need to propose suitable ways to assist doctors to predict sudden cardiac death with a high level of accuracy. To do this, Linear, Time-Frequency (TF) and Nonlinear features have been extracted from HRV of ECG signal. Finally, healthy people and people at risk of SCD are classified by k-Nearest Neighbor (k-NN) and Multilayer Perceptron Neural Network (MLP). To evaluate, we have compared the classification rates for both separate and combined Nonlinear and TF features. The results show that HRV signals have special features in the vicinity of the occurrence of SCD that have the ability to distinguish between patients prone to SCD and normal people. We found that the combination of Time-Frequency and Nonlinear features have a better ability to achieve higher accuracy. The experimental results show that the combination of features can predict SCD by the accuracy of 99.73%, 96.52%, 90.37% and 83.96% for the first, second, third and forth one-minute intervals, respectively, before SCD occurrence.
Ebrahimzadeh et al. (2014) conducted a case-control in Sudden Cardiac Death (n=70). Combinational feature vector (Linear, Time-Frequency, and Nonlinear) with Multilayer Perceptron (MLP) classifier vs. Classic linear features was evaluated on Classification accuracy for predicting SCD 1 minute before occurrence. The combination of Time-Frequency and Nonlinear features extracted from HRV signals predicted sudden cardiac death with an accuracy of 99.73% one minute before the event.