Artificial Neural Network for Atrial Fibrillation Identification in Portable Devices
Atrial fibrillation (AF) is a common cardiac disorder that can cause severe complications. AF diagnosis is typically based on the electrocardiogram (ECG) evaluation in hospitals or in clinical facilities. The aim of the present work is to propose a new artificial neural network for reliable AF ident...
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| Published in: | Sensors (Basel, Switzerland) Vol. 20; no. 12; p. 3570 |
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| Abstract | Atrial fibrillation (AF) is a common cardiac disorder that can cause severe complications. AF diagnosis is typically based on the electrocardiogram (ECG) evaluation in hospitals or in clinical facilities. The aim of the present work is to propose a new artificial neural network for reliable AF identification in ECGs acquired through portable devices. A supervised fully connected artificial neural network (RSL_ANN), receiving 19 ECG features (11 morphological, 4 on F waves and 4 on heart-rate variability (HRV)) in input and discriminating between AF and non-AF classes in output, was created using the repeated structuring and learning (RSL) procedure. RSL_ANN was created and tested on 8028 (training: 4493; validation: 1125; testing: 2410) annotated ECGs belonging to the “AF Classification from a Short Single Lead ECG Recording” database and acquired with the portable KARDIA device by AliveCor. RSL_ANN performance was evaluated in terms of area under the curve (AUC) and confidence intervals (CIs) of the received operating characteristic. RSL_ANN performance was very good and very similar in training, validation and testing datasets. AUC was 91.1% (CI: 89.1–93.0%), 90.2% (CI: 86.2–94.3%) and 90.8% (CI: 88.1–93.5%) for the training, validation and testing datasets, respectively. Thus, RSL_ANN is a promising tool for reliable identification of AF in ECGs acquired by portable devices. |
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| AbstractList | Atrial fibrillation (AF) is a common cardiac disorder that can cause severe complications. AF diagnosis is typically based on the electrocardiogram (ECG) evaluation in hospitals or in clinical facilities. The aim of the present work is to propose a new artificial neural network for reliable AF identification in ECGs acquired through portable devices. A supervised fully connected artificial neural network (RSL_ANN), receiving 19 ECG features (11 morphological, 4 on F waves and 4 on heart-rate variability (HRV)) in input and discriminating between AF and non-AF classes in output, was created using the repeated structuring and learning (RSL) procedure. RSL_ANN was created and tested on 8028 (training: 4493; validation: 1125; testing: 2410) annotated ECGs belonging to the “AF Classification from a Short Single Lead ECG Recording” database and acquired with the portable KARDIA device by AliveCor. RSL_ANN performance was evaluated in terms of area under the curve (AUC) and confidence intervals (CIs) of the received operating characteristic. RSL_ANN performance was very good and very similar in training, validation and testing datasets. AUC was 91.1% (CI: 89.1–93.0%), 90.2% (CI: 86.2–94.3%) and 90.8% (CI: 88.1–93.5%) for the training, validation and testing datasets, respectively. Thus, RSL_ANN is a promising tool for reliable identification of AF in ECGs acquired by portable devices. Atrial fibrillation (AF) is a common cardiac disorder that can cause severe complications. AF diagnosis is typically based on the electrocardiogram (ECG) evaluation in hospitals or in clinical facilities. The aim of the present work is to propose a new artificial neural network for reliable AF identification in ECGs acquired through portable devices. A supervised fully connected artificial neural network (RSL_ANN), receiving 19 ECG features (11 morphological, 4 on F waves and 4 on heart-rate variability (HRV)) in input and discriminating between AF and non-AF classes in output, was created using the repeated structuring and learning (RSL) procedure. RSL_ANN was created and tested on 8028 (training: 4493; validation: 1125; testing: 2410) annotated ECGs belonging to the "AF Classification from a Short Single Lead ECG Recording" database and acquired with the portable KARDIA device by AliveCor. RSL_ANN performance was evaluated in terms of area under the curve (AUC) and confidence intervals (CIs) of the received operating characteristic. RSL_ANN performance was very good and very similar in training, validation and testing datasets. AUC was 91.1% (CI: 89.1-93.0%), 90.2% (CI: 86.2-94.3%) and 90.8% (CI: 88.1-93.5%) for the training, validation and testing datasets, respectively. Thus, RSL_ANN is a promising tool for reliable identification of AF in ECGs acquired by portable devices.Atrial fibrillation (AF) is a common cardiac disorder that can cause severe complications. AF diagnosis is typically based on the electrocardiogram (ECG) evaluation in hospitals or in clinical facilities. The aim of the present work is to propose a new artificial neural network for reliable AF identification in ECGs acquired through portable devices. A supervised fully connected artificial neural network (RSL_ANN), receiving 19 ECG features (11 morphological, 4 on F waves and 4 on heart-rate variability (HRV)) in input and discriminating between AF and non-AF classes in output, was created using the repeated structuring and learning (RSL) procedure. RSL_ANN was created and tested on 8028 (training: 4493; validation: 1125; testing: 2410) annotated ECGs belonging to the "AF Classification from a Short Single Lead ECG Recording" database and acquired with the portable KARDIA device by AliveCor. RSL_ANN performance was evaluated in terms of area under the curve (AUC) and confidence intervals (CIs) of the received operating characteristic. RSL_ANN performance was very good and very similar in training, validation and testing datasets. AUC was 91.1% (CI: 89.1-93.0%), 90.2% (CI: 86.2-94.3%) and 90.8% (CI: 88.1-93.5%) for the training, validation and testing datasets, respectively. Thus, RSL_ANN is a promising tool for reliable identification of AF in ECGs acquired by portable devices. |
| Author | Marcantoni, Ilaria Burattini, Laura Marinucci, Daniele Swenne, Cees A. Morettini, Micaela Sbrollini, Agnese |
| AuthorAffiliation | 2 Cardiology Department, Leiden University Medical Center, P.O. Box 9600, 2300 RC Leiden, The Netherlands; c.a.swenne@lumc.nl 1 Department of Information Engineering, Università Politecnica delle Marche, via Brecce Bianche 12, 60131 Ancona, Italy; daniele.marinucci90@gmail.com (D.M.); a.sbrollini@pm.univpm.it (A.S.); i.marcantoni@pm.univpm.it (I.M.); m.morettini@univpm.it (M.M.) |
| AuthorAffiliation_xml | – name: 1 Department of Information Engineering, Università Politecnica delle Marche, via Brecce Bianche 12, 60131 Ancona, Italy; daniele.marinucci90@gmail.com (D.M.); a.sbrollini@pm.univpm.it (A.S.); i.marcantoni@pm.univpm.it (I.M.); m.morettini@univpm.it (M.M.) – name: 2 Cardiology Department, Leiden University Medical Center, P.O. Box 9600, 2300 RC Leiden, The Netherlands; c.a.swenne@lumc.nl |
| Author_xml | – sequence: 1 givenname: Daniele surname: Marinucci fullname: Marinucci, Daniele – sequence: 2 givenname: Agnese orcidid: 0000-0002-9152-7216 surname: Sbrollini fullname: Sbrollini, Agnese – sequence: 3 givenname: Ilaria surname: Marcantoni fullname: Marcantoni, Ilaria – sequence: 4 givenname: Micaela orcidid: 0000-0002-8327-8379 surname: Morettini fullname: Morettini, Micaela – sequence: 5 givenname: Cees A. orcidid: 0000-0001-9801-2760 surname: Swenne fullname: Swenne, Cees A. – sequence: 6 givenname: Laura orcidid: 0000-0002-9474-7046 surname: Burattini fullname: Burattini, Laura |
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| Cites_doi | 10.1093/eurheartj/ehz834 10.1093/ehjci/eux136.008 10.3390/s19235079 10.18637/jss.v008.i02 10.1177/0267659118808703 10.1053/euhj.1998.1050 10.1016/j.amjcard.2018.01.035 10.1007/s10916-020-01565-y 10.3390/s18020405 10.1161/01.CIR.0000018443.44005.D8 10.3390/ijerph17020498 10.2196/11606 10.1016/j.future.2019.09.012 10.1016/j.medengphy.2016.03.011 10.1186/s12938-019-0630-9 10.22489/CinC.2018.066 10.1016/S0893-6080(05)80056-5 10.1016/j.compbiomed.2020.103726 10.22489/CinC.2018.099 10.1007/978-3-642-35289-8 10.1109/TBME.2012.2208112 10.1109/JBHI.2017.2688473 10.1016/j.bspc.2019.101753 10.1161/CIRCULATIONAHA.113.005119 10.3346/jkms.2019.34.e64 10.1109/TBME.1985.325532 10.1088/1361-6579/aac552 10.1161/01.CIR.101.23.e215 10.22489/CinC.2017.065-469 10.1109/EMBC.2018.8512761 |
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| References | Goldberger (ref_22) 2000; 101 Lahdenoja (ref_10) 2018; 22 Lee (ref_9) 2012; 60 Chugh (ref_3) 2013; 129 ref_13 Murat (ref_33) 2020; 120 ref_34 ref_32 Rozen (ref_7) 2018; 121 ref_30 Wang (ref_15) 2020; 102 (ref_29) 1993; 6 ref_19 ref_18 Bettoni (ref_28) 2002; 105 Athif (ref_17) 2018; 39 Schmitz (ref_4) 2018; 34 Yang (ref_12) 1994; 32 King (ref_31) 2003; 8 Breithardt (ref_2) 1998; 19 Nicolet (ref_14) 2020; 57 ref_25 ref_24 Sbrollini (ref_21) 2019; 18 ref_20 Krivoshei (ref_5) 2017; 19 Ghosh (ref_16) 2020; 44 Chong (ref_8) 2015; 19 Erdenebayar (ref_11) 2019; 34 Mortelmans (ref_6) 2017; 19 Pan (ref_23) 1985; 32 ref_27 Li (ref_35) 2019; 7 Agostinelli (ref_26) 2016; 38 Jones (ref_1) 2019; 41 |
| References_xml | – volume: 32 start-page: 615 year: 1994 ident: ref_12 article-title: Artificial neural networks for the diagnosis of atrial fibrillation publication-title: Med. Boil. Eng. – volume: 41 start-page: 1075 year: 2019 ident: ref_1 article-title: Screening for atrial fibrillation: A call for evidence publication-title: Eur. Hear. J. doi: 10.1093/eurheartj/ehz834 – volume: 19 start-page: 16 year: 2017 ident: ref_6 article-title: Validation of a new smartphone application for the diagnosis of atrial fibrillation in primary care publication-title: Europace doi: 10.1093/ehjci/eux136.008 – ident: ref_20 doi: 10.3390/s19235079 – volume: 8 start-page: 137 year: 2003 ident: ref_31 article-title: Logistic Regression in rare events data publication-title: J. Stat. Softw. doi: 10.18637/jss.v008.i02 – volume: 19 start-page: 815 year: 2015 ident: ref_8 article-title: Arrhythmia discrimination using a smart phone publication-title: IEEE J. Biomed. Heal. Inf. – volume: 34 start-page: 174 year: 2018 ident: ref_4 article-title: Book Review: Braunwald’s heart disease: A textbook of cardiovascular medicine publication-title: Perfusion doi: 10.1177/0267659118808703 – volume: 19 start-page: 1294 year: 1998 ident: ref_2 article-title: Atrial fibrillation: Current knowledge and recommendations for management *1 publication-title: Eur. Hear. J. doi: 10.1053/euhj.1998.1050 – volume: 121 start-page: 1187 year: 2018 ident: ref_7 article-title: Diagnostic accuracy of a novel mobile phone application for the detection and monitoring of atrial fibrillation publication-title: Am. J. Cardiol. doi: 10.1016/j.amjcard.2018.01.035 – volume: 44 start-page: 114 year: 2020 ident: ref_16 article-title: Detection of atrial fibrillation from single lead ECG signal using multirate cosine filter bank and deep neural network publication-title: J. Med. Syst. doi: 10.1007/s10916-020-01565-y – ident: ref_24 doi: 10.3390/s18020405 – volume: 105 start-page: 2753 year: 2002 ident: ref_28 article-title: Autonomic tone variations before the onset of paroxysmal atrial fibrillation publication-title: Circulation doi: 10.1161/01.CIR.0000018443.44005.D8 – ident: ref_13 doi: 10.3390/ijerph17020498 – ident: ref_18 – volume: 7 start-page: e11606 year: 2019 ident: ref_35 article-title: The current state of mobile phone apps for monitoring heart rate, heart rate variability, and atrial fibrillation: Narrative review publication-title: JMIR mHealth uHealth doi: 10.2196/11606 – volume: 102 start-page: 670 year: 2020 ident: ref_15 article-title: A deep learning approach for atrial fibrillation signals classification based on convolutional and modified Elman neural network publication-title: Futur. Gener. Comput. Syst. doi: 10.1016/j.future.2019.09.012 – volume: 38 start-page: 560 year: 2016 ident: ref_26 article-title: Segmented beat modulation method for electrocardiogram estimation from noisy recordings publication-title: Med. Eng. Phys. doi: 10.1016/j.medengphy.2016.03.011 – volume: 18 start-page: 15 year: 2019 ident: ref_21 article-title: Serial electrocardiography to detect newly emerging or aggravating cardiac pathology: A deep-learning approach publication-title: Biomed. Eng. Online doi: 10.1186/s12938-019-0630-9 – ident: ref_27 doi: 10.22489/CinC.2018.066 – ident: ref_25 – volume: 6 start-page: 525 year: 1993 ident: ref_29 article-title: A scaled conjugate gradient algorithm for fast supervised learning publication-title: Neural Networks doi: 10.1016/S0893-6080(05)80056-5 – volume: 120 start-page: 103726 year: 2020 ident: ref_33 article-title: Application of deep learning techniques for heartbeats detection using ECG signals-analysis and review publication-title: Comput. Boil. Med. doi: 10.1016/j.compbiomed.2020.103726 – volume: 19 start-page: 753 year: 2017 ident: ref_5 article-title: Smart detection of atrial fibrillation† publication-title: Europace – ident: ref_32 doi: 10.22489/CinC.2018.099 – ident: ref_30 doi: 10.1007/978-3-642-35289-8 – volume: 60 start-page: 203 year: 2012 ident: ref_9 article-title: Atrial fibrillation detection using an iPhone 4S publication-title: IEEE Trans. Biomed. Eng. doi: 10.1109/TBME.2012.2208112 – volume: 22 start-page: 108 year: 2018 ident: ref_10 article-title: Atrial fibrillation detection via accelerometer and gyroscope of a smartphone publication-title: IEEE J. Biomed. Heal. Informatics doi: 10.1109/JBHI.2017.2688473 – volume: 57 start-page: 101753 year: 2020 ident: ref_14 article-title: Classification of intracavitary electrograms in atrial fibrillation using information and complexity measures publication-title: Biomed. Signal. Process. Control. doi: 10.1016/j.bspc.2019.101753 – volume: 129 start-page: 837 year: 2013 ident: ref_3 article-title: Worldwide epidemiology of atrial fibrillation: A Global Burden of Disease 2010 Study publication-title: Circulation doi: 10.1161/CIRCULATIONAHA.113.005119 – volume: 34 start-page: e64 year: 2019 ident: ref_11 article-title: Automatic prediction of atrial fibrillation based on convolutional neural network using a short-term normal electrocardiogram signal publication-title: J. Korean Med. Sci. doi: 10.3346/jkms.2019.34.e64 – volume: 32 start-page: 230 year: 1985 ident: ref_23 article-title: A real-time QRS detection algorithm publication-title: IEEE Trans. Biomed. Eng. doi: 10.1109/TBME.1985.325532 – volume: 39 start-page: 064002 year: 2018 ident: ref_17 article-title: Detecting atrial fibrillation from short single lead ECGs using statistical and morphological features publication-title: Physiol. Meas. doi: 10.1088/1361-6579/aac552 – volume: 101 start-page: e215 year: 2000 ident: ref_22 article-title: PhysioBank, PhysioToolkit, and PhysioNet publication-title: Circulation doi: 10.1161/01.CIR.101.23.e215 – ident: ref_19 doi: 10.22489/CinC.2017.065-469 – ident: ref_34 doi: 10.1109/EMBC.2018.8512761 |
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| Snippet | Atrial fibrillation (AF) is a common cardiac disorder that can cause severe complications. AF diagnosis is typically based on the electrocardiogram (ECG)... |
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| SubjectTerms | Algorithms artificial neural networks atrial fibrillation Atrial Fibrillation - diagnosis Cardiac arrhythmia Datasets Electrocardiography Electrocardiography - instrumentation Heart Heart Rate Humans Identification machine learning algorithms Medical equipment Morphology Neural networks Neural Networks, Computer Noise Population portable devices Signal processing |
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| Title | Artificial Neural Network for Atrial Fibrillation Identification in Portable Devices |
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