Next Article in Journal
A Scoping Review of Digital Tools to Reduce Sedentary Behavior or Increase Physical Activity in Knowledge Workers
Previous Article in Journal
Development of a Responsible Policy Index to Improve Statutory and Self-Regulatory Policies that Protect Children’s Diet and Health in the America’s Region
Previous Article in Special Issue
Healthcare Associated Infections: An Interoperable Infrastructure for Multidrug Resistant Organism Surveillance
Open AccessArticle

Analysis of Relevant Features from Photoplethysmographic Signals for Atrial Fibrillation Classification

Telematics Engineering Research Group, Telematics Department, Universidad Del Cauca (Unicauca), Popayán 190002, Colombia
*
Author to whom correspondence should be addressed.
Int. J. Environ. Res. Public Health 2020, 17(2), 498; https://doi.org/10.3390/ijerph17020498
Received: 19 November 2019 / Revised: 23 December 2019 / Accepted: 24 December 2019 / Published: 13 January 2020
Atrial Fibrillation (AF) is the most common cardiac arrhythmia found in clinical practice. It affects an estimated 33.5 million people, representing approximately 0.5% of the world’s population. Electrocardiogram (ECG) is the main diagnostic criterion for AF. Recently, photoplethysmography (PPG) has emerged as a simple and portable alternative for AF detection. However, it is not completely clear which are the most important features of the PPG signal to perform this process. The objective of this paper is to determine which are the most relevant features for PPG signal analysis in the detection of AF. This study is divided into two stages: (a) a systematic review carried out following the Preferred Reporting Items for a Systematic Review and Meta-analysis of Diagnostic Test Accuracy Studies (PRISMA-DTA) statement in six databases, in order to identify the features of the PPG signal reported in the literature for the detection of AF, and (b) an experimental evaluation of them, using machine learning, in order to determine which have the greatest influence on the process of detecting AF. Forty-four features were found when analyzing the signal in the time, frequency, or time–frequency domains. From those 44 features, 27 were implemented, and through machine learning, it was found that only 11 are relevant in the detection process. An algorithm was developed for the detection of AF based on these 11 features, which obtained an optimal performance in terms of sensitivity (98.43%), specificity (99.52%), and accuracy (98.97%). View Full-Text
Keywords: atrial fibrillation; AF; photoplethysmography; PPG; feature selection atrial fibrillation; AF; photoplethysmography; PPG; feature selection
Show Figures

Figure 1

MDPI and ACS Style

Millán, C.A.; Girón, N.A.; Lopez, D.M. Analysis of Relevant Features from Photoplethysmographic Signals for Atrial Fibrillation Classification. Int. J. Environ. Res. Public Health 2020, 17, 498.

Show more citation formats Show less citations formats
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop