Academic Journal
Identifying Ventricular Dysfunction Indicators in Electrocardiograms via Artificial Intelligence-Driven Analysis
| العنوان: | Identifying Ventricular Dysfunction Indicators in Electrocardiograms via Artificial Intelligence-Driven Analysis |
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| المؤلفون: | Hisaki Makimoto, Takayuki Okatani, Masanori Suganuma, Tomoyuki Kabutoya, Takahide Kohro, Yukiko Agata, Yukiyo Ogata, Kenji Harada, Redi Llubani, Alexandru Bejinariu, Obaida R. Rana, Asuka Makimoto, Elisabetha Gharib, Anita Meissner, Malte Kelm, Kazuomi Kario |
| المصدر: | Bioengineering, Vol 11, Iss 11, p 1069 (2024) |
| بيانات النشر: | MDPI AG, 2024. |
| سنة النشر: | 2024 |
| المجموعة: | LCC:Technology LCC:Biology (General) |
| مصطلحات موضوعية: | electrocardiogram, ventricular dysfunction, artificial intelligence, Technology, Biology (General), QH301-705.5 |
| الوصف: | Recent studies highlight artificial intelligence’s ability to identify ventricular dysfunction via electrocardiograms (ECGs); however, specific indicative waveforms remain unclear. This study analysed ECG and echocardiography data from 17,422 cases in Japan and Germany. We developed 10-layer convolutional neural networks to detect left ventricular ejection fractions below 50%, using four-fold cross-validation. Model performance, evaluated among different ECG configurations (3 s strips, single-beat, and two-beat overlay) and segments (PQRST, QRST, P, QRS, and PQRS), showed two-beat ECGs performed best, followed by single-beat models, surpassing 3 s models in both internal and external validations. Single-beat models revealed limb leads, particularly I and aVR, as most indicative of dysfunction. An analysis indicated segments from QRS to T-wave were most revealing, with P segments enhancing model performance. This study confirmed that dual-beat ECGs enabled the most precise ventricular function classification, and segments from the P- to T-wave in ECGs were more effective for assessing ventricular dysfunction, with leads I and aVR offering higher diagnostic utility. |
| نوع الوثيقة: | article |
| وصف الملف: | electronic resource |
| اللغة: | English |
| تدمد: | 2306-5354 |
| Relation: | https://www.mdpi.com/2306-5354/11/11/1069; https://doaj.org/toc/2306-5354 |
| DOI: | 10.3390/bioengineering11111069 |
| URL الوصول: | https://doaj.org/article/02b11615898b47118076054187abba0e |
| رقم الانضمام: | edsdoj.02b11615898b47118076054187abba0e |
| قاعدة البيانات: | Directory of Open Access Journals |
Full Text Finder | تدمد: | 23065354 |
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| DOI: | 10.3390/bioengineering11111069 |