Anesthesia Depth Monitoring Based on Anesthesia Monitor with the Help of Artificial Intelligence / 中国医疗器械杂志
Chinese Journal of Medical Instrumentation
;
(6): 43-46, 2023.
Artículo
en Chino
| WPRIM
| ID: wpr-971301
ABSTRACT
OBJECTIVE@#To use the low-cost anesthesia monitor for realizing anesthesia depth monitoring, effectively assist anesthesiologists in diagnosis and reduce the cost of anesthesia operation.@*METHODS@#Propose a monitoring method of anesthesia depth based on artificial intelligence. The monitoring method is designed based on convolutional neural network (CNN) and long and short-term memory (LSTM) network. The input data of the model include electrocardiogram (ECG) and pulse wave photoplethysmography (PPG) recorded in the anesthesia monitor, as well as heart rate variability (HRV) calculated from ECG, The output of the model is in three states of anesthesia induction, anesthesia maintenance and anesthesia awakening.@*RESULTS@#The accuracy of anesthesia depth monitoring model under transfer learning is 94.1%, which is better than all comparison methods.@*CONCLUSIONS@#The accuracy of this study meets the needs of perioperative anesthesia depth monitoring and the study reduces the operation cost.
Texto completo:
Disponible
Índice:
WPRIM (Pacífico Occidental)
Asunto principal:
Inteligencia Artificial
/
Redes Neurales de la Computación
/
Fotopletismografía
/
Electrocardiografía
/
Frecuencia Cardíaca
/
Anestesia
Idioma:
Chino
Revista:
Chinese Journal of Medical Instrumentation
Año:
2023
Tipo del documento:
Artículo
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