Key result
A novel methodology combining empirical mode decomposition, nonlinear measurements, and a neural network predicted sudden cardiac death 25 minutes before occurrence with 94% accuracy.
Why the study?
Sudden cardiac death causes 10% of deaths worldwide, necessitating new methodologies capable of predicting the event at the earliest possible stage.
A novel machine learning methodology using raw ECG data can predict sudden cardiac death episodes 25 minutes in advance with 94% accuracy.
May aid early SCD risk detection; leaves open prospective validation before clinical use.
Heart diseases are among the most common death causes in the population. Particularly, sudden cardiac death (SCD) is the cause of 10% of the deaths around the world. For this reason, it is necessary to develop new methodologies that can predict this event in the earliest possible stage. This work presents a novel methodology to predict when a person can develop an SCD episode before it occurs. It is based on the adroit combination of the empirical mode decomposition, nonlinear measurements, such as the Higuchi fractal and permutation entropy, and a neural network. The obtained results show that the proposed methodology is capable of detecting an SCD episode 25 min before it appears with a 94% accuracy. The main benefits of the proposal are: (1) an improved detection time of 25% compared with previously published works, (2) moderate computational complexity since only two features are used, and (3) it uses the raw ECG without any preprocessing stage, unlike recent previous works.
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Vargas-Lopez et al. (2019) studied Sudden cardiac death. Methodology based on empirical mode decomposition, nonlinear measurements, and a neural network vs. Previously published works was evaluated on Prediction of sudden cardiac death episode. A novel methodology combining empirical mode decomposition, nonlinear measurements, and a neural network predicted sudden cardiac death 25 minutes before occurrence with 94% accuracy.
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