1 research outputs found
Machine Learning approach for TWA detection relying on ensemble data design
Background and objective: T-wave alternans (TWA) is a fluctuation of the ST–T complex of
the surface electrocardiogram (ECG) on an every–other–beat basis. It has been shown to be
clinically helpful for sudden cardiac death stratification, though the lack of a gold standard to
benchmark detection methods limits its application and impairs the development of alternative
techniques. In this work, a novel approach based on machine learning for TWA detection is
proposed. Additionally, a complete experimental setup is presented for TWA detection methods
benchmarking.
Methods: The proposed experimental setup is based on the use of open-source databases to
enable experiment replication and the use of real ECG signals with added TWA episodes. Also,
intra-patient overfitting and class imbalance have been carefully avoided. The Spectral Method
(SM), the Modified Moving Average Method (MMA), and the Time Domain Method (TM) are used
to obtain input features to the Machine Learning (ML) algorithms, namely, K Nearest Neighbor,
Decision Trees, Random Forest, Support Vector Machine and Multi-Layer Perceptron.
Results: There were not found large differences in the performance of the different ML algorithms.
Decision Trees showed the best overall performance (accuracy 0.88 ± 0.04, precision 0.89 ± 0.05,
Recall 0.90± 0.05, F1 score 0.89± 0.03). Compared to the SM (accuracy 0.79, precision 0.93, Recall
0.64, F1 score 0.76) there was an improvement in every metric except for the precision.
Conclusions: In this work, a realistic database to test the presence of TWA using ML algorithms
was assembled. The ML algorithms overall outperformed the SM used as a gold standard. Learning
from data to identify alternans elicits a substantial detection growth at the expense of a small
increment of the false alarm.Universidad de Alcal