A Real-Time Depth of Anesthesia Monitoring System Based on Deep Neural Network With Large EDO Tolerant EEG Analog Front-End.

In this article, we present a real-time electroencephalogram (EEG) based depth of anesthesia (DoA) monitoring system in conjunction with a deep learning framework, AnesNET. An EEG analog front-end (AFE) that can compensate ±380-mV electrode DC offset using a coarse digital DC servo loop is implemented in the proposed system. The EEG-based MAC, EEGMAC, is introduced as a novel index to accurately predict the DoA, which is designed for applying to patients anesthetized by both volatile and intravenous agents. The proposed deep learning protocol consists of four layers of convolutional neural network and two dense layers. In addition, we optimize the complexity of the deep neural network (DNN) to operate on a microcomputer such as the Raspberry Pi 3, realizing a cost-effective small-size DoA monitoring system. Fabricated in 110-nm CMOS, the prototype AFE consumes 4.33 µW per channel and has the input-referred noise of 0.29 µVrms from 0.5 to 100 Hz with the noise efficiency factor of 2.2. The proposed DNN was evaluated with pre-recorded EEG data from 374 subjects administrated by inhalational anesthetics under surgery, achieving an average squared and absolute errors of 0.048 and 0.05, respectively. The EEGMAC with subjects anesthetized by an intravenous agent also showed a good agreement with the bispectral index value, confirming the proposed DoA index is applicable to both anesthetics. The implemented monitoring system with the Raspberry Pi 3 estimates the EEGMAC within 20 ms, which is about thousand-fold faster than the BIS estimation in literature.

Classifying depth of anesthesia using EEG features, a comparison.

Various EEG features have been used in depth of anesthesia (DOA) studies. The objective of this study was to find the excellent features or combination of them than can discriminate between different anesthesia states. Conducting a clinical study on 22 patients we could define 4 distinct anesthetic states: awake, moderate, general anesthesia, and isoelectric. We examined features that have been used in earlier studies using single-channel EEG signal processing method. The maximum accuracy (99.02%) achieved using approximate entropy as the feature. Some other features could well discriminate a particular state of anesthesia. We could completely classify the patterns by means of 3 features and Bayesian classifier.

Cambios hemodinámicos de la anestesia inhalatoria con isoflurano y sevoflurano / Hemodynamics changes of the inhalatory anesthesia with isoflurance and sevoflurane

Se realizó un estudio de anéstesia inhalatoria con dos agentes inhalatorios de reciente uso en nuestro hospital como son el Isoflurano y el Sevoflurano se valoró el comportamiento hemodinámico durante la inducción, mantenimiento y recuperación de la anéstesia, así como las reacciones adversas másfrecuentes que se nos presentaron durante el estudio. Todos los pacientes se premedicaron con Midazolán. El estudio estuvo comprendido por 59 pacientes distribuidos de la siguiente manera: Isoflurano n=30 y para el Sevoflurano n=29. En cadagrupo se monitorizo la PAS, PAD y FC así como la saturación de oxigeno durante un período de 10 minutos (se realizaron tomas minuto a minuto) posteriormente se fue valorado cada cinco minutos y se fueron registrando en una ficha de recolección de datos previamente elaborada. A todos los pacientes se les administró un relajante muscular para facilitar la intubación orotraqueal y el mantenimiento fue con pancuronio. Al final de la cirugía aproximadamente 30 minutos antes se le administraba 100 mcg de Fentanil IV y luego pasaba a sala de recuperación en donde se valoraban por dos horas. Durante su estancia en recuperación se fue valorando además de los parámetros hemodinámicos, el tiempo en que el paciente recuperaba su consciencia; valorado por medio de preguntas y respuestas tales como: ¿Cuál es su nombre? ¿Qué edad tiene? etc