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PREDICTING THE DEPTH OF ANESTHESIA FOR OPERATING PATIENT USING MUSIC-BASED SPECTRAL FEATURES OF EEG SIGNALS

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In modern practice of major surgery using anesthesia is entirely mandatory. But due to the failure of optimal dose of anesthetic dose delivery it is also common to the patients to face intraoperative and postoperative complications. The main cause of the imbalance dose of anesthesia is not being...

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Main Author: RAHMAN, M. N. NASHID
Format: Thesis
Language:English
Published: Department of Biomedical Engineering, MIST 2024
Subjects:
Anesthetic Depth; Electroencephalogram (EEG); Multiple Signal Classification (MUSIC); Artificial Neural Network (ANN).
Online Access:http://dspace.mist.ac.bd:8080/xmlui/handle/123456789/818
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Summary:In modern practice of major surgery using anesthesia is entirely mandatory. But due to the failure of optimal dose of anesthetic dose delivery it is also common to the patients to face intraoperative and postoperative complications. The main cause of the imbalance dose of anesthesia is not being sure to assess the depth of sleep of the patient or the depth of anesthesia or. Therefore, precise prediction of the depth of anesthesia or the proper assessment of transitional sleep state (from deep sleep to awake) could be a way out to set the optimal anesthetic dose by the anesthesiologist. In this work, a different approach of feature extraction and classification method is proposed to predict three different sleep states during surgery from the EEG signal. This work used an open-source database containing the EEG data of anesthetic patients during surgery. The data were separated into three states: into the deep-sleep state (IntoDeep), the deep-sleep state (InDeep), and the awake state (InAwake). The raw EEG signals were filtered and their power spectral (PSD) densities were calculated using MUSIC (multiple signal classification) model, a parametric method. These MUSIC based PSD values are taken as the features of the EEG signal. An artificial neural network model was trained to develop a machine learning based predictive model with the MUSIC based PSD features. Finally, the predictive model was verified by the data separated for testing and evaluated the prediction accuracy in subject-dependent and subject-independent approach. Eventually, it is found that the results are better than the existing works those worked on the same dataset.

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