Abstract
The main goal of this study is to precisely determine the optimum anesthetic dosage required for surgical procedures by evaluating the degree of deep sedation in patients. Anesthesiologists can control the right dose of sedative to guarantee patient comfort and safety during a medical treatment by being aware of the brain waves connected to deep sedation. The electroencephalogram, or EEG, is used as a tool to assess brain activity to identify the deep phase of sedation. The detection method involves analyzing EEG signals imported from an open database in order to extract significant features, followed by preprocessing to remove artifacts related to internal and external factors that can disturb the signal, such as muscle movement, heartbeat, etc., by using Butterworth bandpass filters, the filtered signal is then divided into four separate frequency bands (beta, alpha, theta, and delta). The FFT approach is used to identify the dominating band as the last stage, enabling researchers to extract pertinent information and choose appropriate sedative levels. These procedures are carried out with the aid of LabVIEW software, which has a dedicated Toolkit for the biomedical industry and enables the visualization of the results on a practical and effective graphical interface. In addition, the use of LabVIEW as a processing tool emphasizes the significance of the field of physiological signal processing and demonstrates its potential to streamline and enhance research methodologies in related studies. The outcomes of this study demonstrated that the delta wave, which represents the profound state of sedation, dominates the other waves with a dominance rate of 98.59%. These findings may help to develop sedation monitoring methods in clinical settings. Healthcare providers can improve the dose of a sedative lowering the risk of under- or over-sedation, and enhancing the patient's experience during surgical procedures by precisely detecting the deep phase of sedation using EEG analysis.
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EL Hadiri, A., Bahatti, L., El Magri, A., Lajouad, R. (2024). Profound Sedation Detection Based on Brain Waves Analysis. In: Ezziyyani, M., Kacprzyk, J., Balas, V.E. (eds) International Conference on Advanced Intelligent Systems for Sustainable Development (AI2SD’2023). AI2SD 2023. Lecture Notes in Networks and Systems, vol 905. Springer, Cham. https://doi.org/10.1007/978-3-031-52385-4_1
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