Shape Analysis of Consecutive Beats May Help in the Automated Detection of Atrial Fibrillation

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68081731%3A_____%2F18%3A00509016" target="_blank" >RIV/68081731:_____/18:00509016 - isvavai.cz</a>

Result on the web

<a href="http://dx.doi.org/10.22489/CinC.2018.036" target="_blank" >http://dx.doi.org/10.22489/CinC.2018.036</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.22489/CinC.2018.036" target="_blank" >10.22489/CinC.2018.036</a>

Result language

angličtina

Original language name

Shape Analysis of Consecutive Beats May Help in the Automated Detection of Atrial Fibrillation

Original language description

Background: Atrial fibrillation (AF) is associated with a higher risk of heart failure or death. AF may be episodic and patients with suspected AF are equipped with Holter ECG devices for several days. However, automated detection of AF in an ECG signal remains problematic, as was shown by the results of the PhysioNet Challenge 2017. Here, we introduce a simple yet robust logistic regression model for AF detection. nMethod: The detrended signal is filtered (1-35 Hz) and normalized. QRS detection based on envelograms (10-35 Hz) reveals QRS complexes. Five features are exfracted from the ECG signal describing RR stability as well as the shape stability of areas preceding QRS complexes. Features were exfracted for 1,517 recordings from the PhysioNet Challenge 2017 public dataset (758 AF recordings and 759 recordings with normal rhythm, other arrhythmia or noisy signal). The recordings were split in a 70/30 % ratio for the purposes of training and testing. nResults: The results showed a sensitivity and specificity of 93 % and 90 %, respectively (AUC 0.96). The presented model was also tested on the MIT-AFDB public database, showing sensitivity and specificity of 89 % and 88 %, respectively. However, tests on an independent private dataset revealed lower specificity when pathologies which are not widely present in the training dataset are common in the tested ECG signal.

Czech name

—

Czech description

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Type

D - Article in proceedings

CEP classification

—

OECD FORD branch

20601 - Medical engineering

Project

<a href="/en/project/LO1212" target="_blank" >LO1212: ALISI - Centre of advanced diagnostic methods and technologies</a><br>

Continuities

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Publication year

2018

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Article name in the collection

Computing in Cardiology 2018

ISBN

—

ISSN

2325-887X

e-ISSN

—

Number of pages

4

Pages from-to

8743764

Publisher name

IEEE

Place of publication

New York

Event location

Maastricht

Event date

Sep 23, 2018

Type of event by nationality

WRD - Celosvětová akce

UT code for WoS article

000482598700075