دورية أكاديمية
Hybrid machine learning to localize atrial flutter substrates using the surface 12-lead electrocardiogram
| العنوان: | Hybrid machine learning to localize atrial flutter substrates using the surface 12-lead electrocardiogram |
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| المؤلفون: | Luongo, Giorgio, Vacanti, Gaetano, Nitzke, Vincent, Nairn, Deborah, Nagel, Claudia, Kabiri, Diba, Almeida, Tiago P., Soriano, Diogo C., Rivolta, Massimo W., Ng, Ghulam André, Dössel, Olaf, Luik, Armin, Sassi, Roberto, Schmitt, Claus, Loewe, Axel |
| المصدر: | Europace : European pacing, arrhythmias, and cardiac electrophysiology : journal of the working groups on cardiac pacing, arrhythmias, and cardiac cellular electrophysiology of the European Society of Cardiology, 24 (7), 1186–1194 ; ISSN: 1099-5129, 1532-2092 |
| بيانات النشر: | Oxford University Press |
| سنة النشر: | 2022 |
| المجموعة: | KITopen (Karlsruhe Institute of Technologie) |
| مصطلحات موضوعية: | ddc:620, Engineering & allied operations, info:eu-repo/classification/ddc/620 |
| الوصف: | Aims Atrial flutter (AFlut) is a common re-entrant atrial tachycardia driven by self-sustainable mechanisms that cause excitations to propagate along pathways different from sinus rhythm. Intra-cardiac electrophysiological mapping and catheter ablation are often performed without detailed prior knowledge of the mechanism perpetuating AFlut, likely prolonging the procedure time of these invasive interventions. We sought to discriminate the AFlut location [cavotricuspid isthmus-dependent (CTI), peri-mitral, and other left atrium (LA) AFlut classes] with a machine learning-based algorithm using only the non-invasive signals from the 12-lead electrocardiogram (ECG). Methods and results Hybrid 12-lead ECG dataset of 1769 signals was used (1424 in silico ECGs, and 345 clinical ECGs from 115 patients—three different ECG segments over time were extracted from each patient corresponding to single AFlut cycles). Seventy-seven features were extracted. A decision tree classifier with a hold-out classification approach was trained, validated, and tested on the dataset randomly split after selecting the most informative features. The clinical test set comprised 38 patients (114 clinical ECGs). The classifier yielded 76.3% accuracy on the clinical test set with a sensitivity of 89.7%, 75.0%, and 64.1% and a positive predictive value of 71.4%, 75.0%, and 86.2% for CTI, peri-mitral, and other LA class, respectively. Considering majority vote of the three segments taken from each patient, the CTI class was correctly classified at 92%. Conclusion Our results show that a machine learning classifier relying only on non-invasive signals can potentially identify the location of AFlut mechanisms. This method could aid in planning and tailoring patient-specific AFlut treatments. |
| نوع الوثيقة: | article in journal/newspaper |
| وصف الملف: | application/pdf |
| اللغة: | English |
| العلاقة: | info:eu-repo/semantics/altIdentifier/wos/000829114000015; info:eu-repo/semantics/altIdentifier/issn/1099-5129; info:eu-repo/semantics/altIdentifier/issn/1532-2092; https://publikationen.bibliothek.kit.edu/1000149469Test; https://publikationen.bibliothek.kit.edu/1000149469/149119345Test; https://doi.org/10.5445/IR/1000149469Test |
| DOI: | 10.5445/IR/1000149469 |
| الإتاحة: | https://doi.org/10.5445/IR/1000149469Test https://doi.org/10.1093/europace/euab322Test https://publikationen.bibliothek.kit.edu/1000149469Test https://publikationen.bibliothek.kit.edu/1000149469/149119345Test |
| حقوق: | https://creativecommons.org/licenses/by-nc/4.0/deed.deTest ; info:eu-repo/semantics/openAccess |
| رقم الانضمام: | edsbas.ED2AF530 |
| قاعدة البيانات: | BASE |
| DOI: | 10.5445/IR/1000149469 |
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