2 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
Generalization and Regularization for Inverse Cardiac Estimators
Electrocardiographic Imaging (ECGI) aims to
estimate the intracardiac potentials noninvasively, hence
allowing the clinicians to better visualize and understand
many arrhythmia mechanisms. Most of the estimators of
epicardial potentials use a signal model based on an estimated
spatial transfer matrix together with Tikhonov regularization
techniques, which works well specially in simulations,
but it can give limited accuracy in some real data.
Based on the quasielectrostatic potential superposition
principle, we propose a simple signal model that supports
the implementation of principled out-of-sample algorithms
for several of the most widely used regularization criteria
in ECGI problems, hence improving the generalization
capabilities of several of the current estimation methods.
Experiments on simple cases (cylindrical and Gaussian
shapes scrutinizing fast and slow changes, respectively)
and on real data (examples of torso tank measurements
available from Utah University, and an animal torso and
epicardium measurements available from Maastricht University,
both in the EDGAR public repository) show that
the superposition-based out-of-sample tuning of regularization
parameters promotes stabilized estimation errors of
the unknown source potentials, while slightly increasing the re-estimation error on the measured data, as natural
in non-overfitted solutions. The superposition signal model
can be used for designing adequate out-of-sample tuning of
Tikhonov regularization techniques, and it can be taken into
account when using other regularization techniques in current
commercial systems and research toolboxes on ECG