Multi-stage SVM approach for cardiac arrhythmias detection in short single-lead ECG recorded by a wearable device

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68081731%3A_____%2F18%3A00495091" target="_blank" >RIV/68081731:_____/18:00495091 - isvavai.cz</a>

Alternative codes found

RIV/00216305:26220/18:PU128932 RIV/65269705:_____/18:00070330

Result on the web

<a href="http://dx.doi.org/10.1088/1361-6579/aad9e7" target="_blank" >http://dx.doi.org/10.1088/1361-6579/aad9e7</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1088/1361-6579/aad9e7" target="_blank" >10.1088/1361-6579/aad9e7</a>

Result language

angličtina

Original language name

Multi-stage SVM approach for cardiac arrhythmias detection in short single-lead ECG recorded by a wearable device

Original language description

Objective: Use of wearable ECG devices for arrhythmia screening is limited due to poor signal quality, small number of leads and short records, leading to incorrect recognition of pathological events. This paper introduces a novel approach to classification (normal/'N', atrial fibrillation/'A', other/'O', and noisy/'P') of short single-lead ECGs recorded by wearable devices. Approach: Various rhythm and morphology features are derived from the separate beats ('local' features) as well as the entire ECGs ('global' features) to represent short-term events and general trends respectively. Various types of atrial and ventricular activity, heart beats and, finally, ECG records are then recognised by a multi-level approach combining a support vector machine (SVM), decision tree and threshold-based rules. Main results: The proposed features are suitable for the recognition of 'A'. The method is robust due to the noise estimation involved. A combination of radial and linear SVMs ensures both high predictive performance and effective generalisation. Cost-sensitive learning, genetic algorithm feature selection and thresholding improve overall performance. The generalisation ability and reliability of this approach are high, as verified by cross-validation on a training set and by blind testing, with only a slight decrease of overall F1-measure, from 0.84 on training to 0.81 on the tested dataset. 'O' recognition seems to be the most difficult (test F1-measures: 0.901'N', 0.81/'A' and 0.721'O') due to high inter-patient variability and similarity with 'N'. Significance: These study results contribute to multidisciplinary areas, focusing on creation of robust and reliable cardiac monitoring systems in order to improve diagnosis, reduce unnecessary time-consuming expert ECG scoring and, consequently, ensure timely and effective treatment.

Czech name

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Czech description

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Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

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OECD FORD branch

20601 - Medical engineering

Project

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Continuities

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Publication year

2018

Confidentiality

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

Name of the periodical

Physiological Measurement

ISSN

0967-3334

e-ISSN

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Volume of the periodical

39

Issue of the periodical within the volume

9

Country of publishing house

GB - UNITED KINGDOM

Number of pages

14

Pages from-to

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UT code for WoS article

000444733400002

EID of the result in the Scopus database

2-s2.0-85054749892