| Autor: |
N, Aguirre, L J, Cymberknop, I, Farro, C, Americo, F, Martinez, E, Grall, N, Lluberas, G, Parma, J, Aramburu, R L, Armentano |
| Rok vydání: |
2021 |
| Předmět: |
|
| Zdroj: |
2021 43rd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC). |
| DOI: |
10.1109/embc46164.2021.9629812 |
| Popis: |
Left ventricular (LV) interaction with the arterial system (arterial-ventricular coupling, AVC) is a central determinant of cardiovascular performance and cardiac energetics. Stress Echocardiography (SE) constitutes a valuable clinical tool in both diagnosis and risk stratification of patients with suspected and established coronary artery disease. Cluster Analysis (CA), an unsupervised Machine Learning technique, defines an exploratory statistical method which can be used to uncover natural groups within data.To evaluate the capacity of CA to identify uncoupled groups with ischemic condition based on SE baseline information.CA was applied to SE data acquired at baseline and peak exercise (PE) conditions. Obtained clusters were evaluated in terms of coupling conditions and LV wall motility alterations.Inter cluster significant AVC differences were obtained in terms of baseline data and changes in wall motility, confirmed by CA applied to PE data.AVC impairment was evidenced in both normal and ischemic subjects by applying CA. |
| Databáze: |
OpenAIRE |
| Externí odkaz: |
|
