Government Engenering college, kerela india
Benzy V.K Worked as Lecturer in MES College of Engineering in Department of Applied electronics and Instrumentation from 01/01/04 to 31/8/2006. She Worked as an Assistant Professor at Prime College of Engineering, Palakkad Department of Electronics and Communication from 24/06/2011 to 17/10/2012.
She has done PhD in Engineering from Govt. Engineering College, at Calicut University during 2012-2015. She completed her M. Tech in Technology Management in at University of Kerala during 2002-2004 and B. Tech in Applied Electronics and Instrumentation Engineering at M.E.S College of Engineering, Kerala during 1996 – 2000
Modern depth of anaesthesia monitors use frontal EEG signal to derive DoA measures. The anesthetic drugs acts mainly on the Central Nervous System (CNS) hence, EEG signal processing during anesthesia is useful to monitor the patient’s depth of anesthesia. This study aims to measure Depth of Anesthesia (DoA) using approximate entropy of EEG signals and classify them according to the DoA . Approximate Entropy of the EEG signal is extracted as a measure of DoA from the EEG signals collected during the four phases of general anesthesia called awake, induction, maintenance and recovery. Approximate entropy is a time domain algorithm that measures the regularity and randomness of the EEG signals during different phases of anesthesia, where EEG signal is considered as a time series data. A low value of approximate entropy indicates anesthetized state where as high value indicates that the patient is awake. Approximate Entropy values is high in awake because of the increased randomness in the EEG signal. EEG shows regularity when depth of anesthesia increases. Induction phase EEG signals are more regular compared to all other EEG signals. Therefore the approximate entropy in the Induction phase shows low values. Finally these approximate entropy features are compared with with commercially available BIS and got 81 percent correlation.
Artificial neural network (ANN) is used in this study to classify EEG signal according to different anaesthetic stages. A feed forward back propagation ANN is used to implement the classification. The activation function employed for all the neuron units in the network is tansig. Approximate Entropy extracted during the four phases are applied as input to the artificial neural network. The whole data set is divided in to two groups training data set and testing data set. Training data sets trains the network where as the testing data set would check the effectiveness of the classifier. In this study, there were four output classes: awake state, light anaesthesia state , moderate anaesthesia state and deep anaesthesia state. The classification accuracy is 91.6 percent. Present study helps to assist Anaesthesiologist in anaesthesia decision making and management.