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