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A novel approach to predict sudden cardiac death (SCD) using nonlinear and time-frequency analyses from HRV signals
Elias Ebrahimzadeh1, Mohammad Pooyan1, Ahmad Bijar1
1Department of Biomedical Engineering, Shahed University, Tehran, Iran.
Insights
Sudden cardiac death (SCD) prediction is improved by analyzing heart rate variability (HRV) signals. Combining Time-Frequency and Nonlinear features accurately identifies individuals at risk of SCD, aiding early intervention.
Area of Science:
- Cardiology
- Biomedical Engineering
- Signal Processing
Background:
- Sudden cardiac death (SCD) is a leading cause of mortality worldwide.
- Early detection and prediction of SCD are crucial for reducing mortality rates.
- Current methods require enhancement for improved accuracy in identifying at-risk individuals.
Purpose of the Study:
- To develop an accurate method for predicting sudden cardiac death (SCD) using heart rate variability (HRV) analysis.
- To investigate the effectiveness of different feature extraction techniques (Linear, Time-Frequency, Nonlinear) from ECG signals for SCD prediction.
- To compare the performance of k-Nearest Neighbor (k-NN) and Multilayer Perceptron Neural Network (MLP) classifiers.
Main Methods:
- Extracted Linear, Time-Frequency (TF), and Nonlinear features from heart rate variability (HRV) derived from ECG signals.
- Classified individuals into healthy and at-risk of SCD groups using k-Nearest Neighbor (k-NN) and Multilayer Perceptron Neural Network (MLP) algorithms.
- Evaluated classification performance by comparing separate and combined feature sets, focusing on TF and Nonlinear features.
Main Results:
- HRV signals exhibit distinct features preceding SCD, enabling differentiation between at-risk and healthy individuals.
- The combination of Time-Frequency and Nonlinear features significantly enhances prediction accuracy.
- High prediction accuracies were achieved: 99.73%, 96.52%, 90.37%, and 83.96% for 1- to 4-minute intervals before SCD.
Conclusions:
- The integration of Time-Frequency and Nonlinear HRV features offers a highly accurate approach for predicting sudden cardiac death.
- This method demonstrates potential for clinical application in identifying individuals susceptible to SCD.
- Accurate prediction of SCD using HRV analysis can facilitate timely medical intervention and improve patient outcomes.
Abstract:
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.
