A Novel Machine Learning Approach to Classify and Detect Atrial Fibrillation Using Optimized Implantable Electrocardiogram Sensor
There are some constraints such as external electrodes, a failure to capture most paroxysmal atrial fibrillation (AFib), low power transfer efficiency (PTE) for 24/7 charging technology, a short period of monitoring, and automatic detection of AFib in conventional electrocardiogram (ECG) sensors. To...
Guardado en:
Autores principales: | Seyed Jamaleddin Mostafavi Yazdi, Hee-Joon Park, Chang-Sik Son, Jong-Ha Lee |
---|---|
Formato: | article |
Lenguaje: | EN |
Publicado: |
IEEE
2021
|
Materias: | |
Acceso en línea: | https://doaj.org/article/8e3d9f2415e248318e102e69717eb862 |
Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
Ejemplares similares
-
Single-Stage Wireless Battery Charging Circuit with Coupling Coefficient Prediction
por: Sang-Won Lee, et al.
Publicado: (2021) -
Design of GIS switch state detection system based on wireless power transfer
por: Feng Wen, et al.
Publicado: (2021) -
Predictors of Atrial Fibrillation Recurrences after a First Radiofrequency Catheter Ablation Intervention for Paroxysmal Atrial Fibrillation—Experience of a Low Volume Ablation Centre
por: Lavinia-Lucia Matei, et al.
Publicado: (2021) -
Efficient Wireless Power Transfer via Magnetic Resonance Coupling Using Automated Impedance Matching Circuit
por: Esraa Mousa Ali, et al.
Publicado: (2021) -
Dynamics of Holter electrocardiogram monitoring in patients with chronic heart failure and atrial fibrillation on the background of cardiac contractility modulation
por: Alfiya A. Safiullina, et al.
Publicado: (2021)