Machine Learning approach for TWA detection relying on ensemble data design

Fernández–Calvillo, Miriam Gutiérrez; Goya–Esteban, Rebeca; Cruz–Roldán, Fernando; Hernández‐Madrid, Antonio; Blanco–Velasco, Manuel

doi:10.1016/j.heliyon.2023.e12947

Cited by 2 publications

(1 citation statement)

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“…In [27], new statistical and spectral detectors, the modified matched pairs t test, the extended spectral method and the modified spectral method are proposed for T-wave alternans (TWA) detection. In [28], a novel approach based on machine learning for TWA detection is proposed. 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 algorithms, namely, K Nearest Neighbor, Decision Trees, Random Forest, Support Vector Machine and Multi-Layer Perceptron.…”

Section: Scd By Twamentioning

confidence: 99%

Sudden Cardiac Death detection by using an hybrid method based on TWA and dictionary learning: A data experimentation

Flores-Calero

Almeida

2023

IEEE Access

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Sudden Cardiac Death (SCD) is considered one of the main causes of mortality worldwide. Understanding the origin of this heart disease continues to be a challenge for the scientific community. T-wave alternans (TWA) is the term used to describe changes in the T wave's amplitude or shape that are seen. According to the literature review, T wave alternans has been considered an important, non-invasive indicator to detect and stratify the risk of sudden cardiac death. On the other hand, dictionary learning is a digital signal processing technique that allows identify the main characteristics of a signal using a sparse representation. In this context, a new non-invasive method is proposed by mixing TWA spectral methods and dictionary learning. The method identifies the main characteristics of ECG signal by obtaining a sparse representation that adapts a matrix (dictionary) in order to use it for highlighting the TWA characteristics and then use these characteristics for detecting SCD. Experimental results show an improvement of 32% compared to the Physionet TWAnalyser program by using synthetic data set and an improvement of 20% over public databases.

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Section: Scd By Twamentioning

confidence: 99%