Brain State Inference While under Anesthesia using CNN-Based Deep Learning Models

Brain State Inference While under Anesthesia using CNN-Based Deep Learning Models

Publication Date : 2023-11-09
Author(s) :

P. Lavanya, M. Madhumalini

           
Article Name :

Brain State Inference While under Anesthesia using CNN-Based Deep Learning Models

Abstract :

Anaesthesia is a critical procedure for surgeons because it allows them to operate on unconscious and pain-free patients. This paper proposes using CNN-based deep learning models to infer brain state while under anesthesia. Both neuroscience research and clinical situations can help determine how deeply unconscious a patient is during anesthesia. The electroencephalogram (EEG) is a real-time, objective method for detecting anesthetic-induced changes in brain states of arousal and/or cognition. To pre-process the signal, the adaptive median filter is used. The median filter is a signal-filtering method used to remove noise from signals. The feature extraction process is carried out using wavelet decomposition, which allows for perfect signal reconstruction. To classify anesthetized brain states, this paper employs a CNN-based technique.

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