Prediction of Sudden Cardiac Death Risk with a Support Vector Machine Based on Heart Rate Variability and Heartprint Indices

Prediction of Sudden Cardiac Death Risk with a Support Vector Machine Based on Heart Rate Variability and Heartprint Indices

Most methods for sudden cardiac death (SCD) prediction require long-term (24 h) electrocardiogram recordings to measure heart rate variability (HRV) indices or premature ventricular complex indices (with the heartprint method). This work aimed to identify the best combinations of HRV and heartprint...

Full description

Bibliographic Details
Main Authors: Marisol Martinez-Alanis, Erik Bojorges-Valdez, Niels Wessel, Claudia Lerma
Format: Article
Language:English
Published: MDPI AG 2020-09-01
Series:Sensors
Subjects:
sudden cardiac death
heart rate variability
heartprint
support vector machine
Online Access:https://www.mdpi.com/1424-8220/20/19/5483

Internet

https://www.mdpi.com/1424-8220/20/19/5483

Similar Items