| Yazarlar (6) |
Erhan Arıkan
Bilecik Şeyh Edebali Üniversitesi, Türkiye |
|
Faik Özel
Bilecik Şeyh Edebali Üniversitesi, Türkiye |
|
Ramazan Aslan
Bilecik Şeyh Edebali Üniversitesi, Türkiye |
|
Murat Özmen
Atatürk Üniversitesi, Türkiye |
Arş. Gör. Ahmet ARDAHANLI
Kafkas Üniversitesi, Türkiye |
|
Isa Ardahanlı
Bilecik Şeyh Edebali Üniversitesi, Türkiye |
| Özet |
| Introduction: Early detection of subclinical left ventricular (LV) dysfunction in hypertensive patients presenting to the emergency department (ED) is of critical importance. We aimed to evaluate the performance of artificial intelligence (AI)-assisted Poincaré plot analysis of electrocardiogram (ECGs) to identify subclinical LV dysfunction rapidly. Methods: 60 hypertensive patients and 55 normotensive controls were prospectively enrolled in the ED. After stabilisation, all participants underwent 5-minute ECG recordings. Heart rate variability (HRV) measurements were calculated, and Poincaré plots were generated. A convolutional neural network (CNN) model was trained to classify the Poincaré plot images. Transthoracic echocardiography was performed within 24 hours to measure left ventricular ejection fraction (LVEF) and |
| Anahtar Kelimeler |
| Makale Türü | Özgün Makale |
| Makale Alt Türü | Uluslararası alan indekslerindeki dergilerde yayınlanan tam makale |
| Dergi Adı | Cardiovasc J Afr |
| Dergi Tarandığı Indeksler | |
| Makale Dili | İngilizce |
| Basım Tarihi | 01-2025 |
| Cilt No | 36 |
| Sayfalar | 491 / 498 |
