From 12 to 1 ECG lead: multiple cardiac condition detection mixing a hybrid machine learning approach with a one-versus-rest classification strategy
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From 12 to 1 ECG lead: multiple cardiac condition detection mixing a hybrid machine learning approach with a one-versus-rest classification strategy

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From 12 to 1 ECG lead: multiple cardiac condition detection mixing a hybrid machine learning approach with a one-versus-rest classification strategy

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dc.contributor.author Jiménez Serrano, Santiago
dc.contributor.author Rodrigo, Miguel
dc.contributor.author Calvo Saiz, Conrado Javier
dc.contributor.author Millet, José
dc.contributor.author Castells, Francesc
dc.date.accessioned 2023-05-22T13:22:11Z
dc.date.available 2023-05-22T13:22:11Z
dc.date.issued 2022
dc.identifier.citation Jiménez Serrano, Santiago Rodrigo, Miguel Calvo Saiz, Conrado Javier Millet, José Castells, Francesc 2022 From 12 to 1 ECG lead: multiple cardiac condition detection mixing a hybrid machine learning approach with a one-versus-rest classification strategy Physiological Measurement 43 6
dc.identifier.uri https://hdl.handle.net/10550/86823
dc.description.abstract Objective. Detecting different cardiac diseases using a single or reduced number of leads is still challenging. This work aims to provide and validate an automated method able to classify ECG recordings. Performance using complete 12-lead systems, reduced lead sets, and single-lead ECGs is evaluated and compared.Approach. Seven different databases with 12-lead ECGs were provided during thePhysioNet/Computing in Cardiology Challenge2021, where 88 253 annotated samples associated with none, one, or several cardiac conditions among 26 different classes were released for training, whereas 42 896 hidden samples were used for testing. After signal preprocessing, 81 features per ECG-lead were extracted, mainly based on heart rate variability, QRST patterns and spectral domain. Next, a One-versus-Rest classification approach made of independent binary classifiers for each cardiac condition was trained. This strategy allowed each ECG to be classified as belonging to none, one or several classes. For each class, a classification model among two binary supervised classifiers and one hybrid unsupervised-supervised classification system was selected. Finally, we performed a 3-fold cross-validation to assess the system's performance.Main results. Our classifiers received scores of 0.39, 0.38, 0.39, 0.38, and 0.37 for the 12, 6, 4, 3 and 2-lead versions of the hidden test set with the Challenge evaluation metric (CM). Also, we obtained a meanG-score of 0.80, 0.78, 0.79, 0.79, 0.77 and 0.74 for the 12, 6, 4, 3, 2 and 1-lead subsets with the public training set during our 3-fold cross-validation.Significance. We proposed and tested a machine learning approach focused on flexibility for identifying multiple cardiac conditions using one or more ECG leads. Our minimal-lead approach may be beneficial for novel portable or wearable ECG devices used as screening tools, as it can also detect multiple and concurrent cardiac conditions.
dc.language.iso eng
dc.relation.ispartof Physiological Measurement, 2022, vol. 43, num. 6
dc.subject Infermeria cardiovascular
dc.title From 12 to 1 ECG lead: multiple cardiac condition detection mixing a hybrid machine learning approach with a one-versus-rest classification strategy
dc.type journal article
dc.date.updated 2023-05-22T13:22:11Z
dc.identifier.doi 10.1088/1361-6579/ac72f5
dc.identifier.idgrec 158803
dc.rights.accessRights open access

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