Analyzing the EEG Signals in Order to Estimate the Depth of Anesthesia using Wavelet and Fuzzy Neural Networks

TitleAnalyzing the EEG Signals in Order to Estimate the Depth of Anesthesia using Wavelet and Fuzzy Neural Networks
Publication TypeJournal Article
Year of Publication2016
AuthorsEsmaeilpour, M., and A. R. A. Mohammadi
JournalInternational Journal of Interactive Multimedia and Artificial Intelligence
ISSN1989-1660
IssueRegular Issue
Volume4
Number2
Date Published12/2016
Pagination12-15
Abstract

Estimating depth of Anesthesia in patients with the objective to administer the right dosage of drug has always attracted the attention of specialists. To study Anesthesia, researchers analyze brain waves since this is the place which is directly affected by the drug. This study aimed to estimate the depth of Anesthesia using electroencephalogram (EGG) signals, wavelet transform, and adaptive Neuro Fuzzy inference system (ANFIS). ANFIS can estimate the depth of Anesthesia with high accuracy. A set of EEG signals regarding consciousness, moderate Anesthesia, deep Anesthesia, and iso-electric point were collected from the American Society of Anesthesiologists (ASA) and PhysioNet. First, the extracted features were combined using wavelet and spectral analysis after which the target features were selected. Later, the features were classified into four categories. The results obtained revealed that the accuracy of the proposed method was 98.45%. Since the visual analysis of EEG signals is difficult, the proposed method can significantly help anesthesiologists estimate the depth of Anesthesia. Further, the results showed that ANFIS could significantly increase the accuracy of Anesthesia depth estimation. Finally, the system was deemed to be advantageous since it was also capable of updating in real-time situations as well.

KeywordsClassification, Fuzzy, Medicine, Neural Network, Wavelet
DOI10.9781/ijimai.2016.422
URLhttp://www.ijimai.org/journal/sites/default/files/files/2016/11/ijimai20164_2_2_pdf_16114.pdf
AttachmentSize
IJIMAI20164_2_2.pdf2.63 MB