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

Identifikátory výsledku

  • Kód výsledku v 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>

  • Nalezeny alternativní kódy

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

  • Výsledek na webu

    <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>

Alternativní jazyky

  • Jazyk výsledku

    angličtina

  • Název v původním jazyce

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

  • Popis výsledku v původním jazyce

    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.

  • Název v anglickém jazyce

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

  • Popis výsledku anglicky

    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.

Klasifikace

  • Druh

    J<sub>imp</sub> - Článek v periodiku v databázi Web of Science

  • CEP obor

  • OECD FORD obor

    20601 - Medical engineering

Návaznosti výsledku

  • Projekt

  • Návaznosti

    I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Ostatní

  • Rok uplatnění

    2018

  • Kód důvěrnosti údajů

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

Údaje specifické pro druh výsledku

  • Název periodika

    Physiological Measurement

  • ISSN

    0967-3334

  • e-ISSN

  • Svazek periodika

    39

  • Číslo periodika v rámci svazku

    9

  • Stát vydavatele periodika

    GB - Spojené království Velké Británie a Severního Irska

  • Počet stran výsledku

    14

  • Strana od-do

  • Kód UT WoS článku

    000444733400002

  • EID výsledku v databázi Scopus

    2-s2.0-85054749892