EEG Signal Processing for Monitoring Depth of Anesthesia

Abstract

Among the various functional organ systems within the human body, the central nervous system (CNS) is affected maximum by anesthetic drug. Since electroencephalography (EEG) is a phenomenon of cerebral cortex, this signal is the best indicator of anesthesia. The raw EEG signal recorded from the patient in the operation theatre has many undesirable constituents or artifacts which don't allow it to be used directly for predicting the depth of anesthesia. These artifacts include scalp muscle interferences, presence of power line frequency carrier, electrocardiographic (ECG) and eye-blinking effects. This paper describes the methods used to remove these artifacts and enumerates various parameters of EEG signal which undergo significant changes with anesthetic dose. Data processing of ‘clean’ EEG signal may be carried out using statistical methods to extract those EEG parameters which discriminate maximum between awake and anesthetized states of human patients.

Additional information

Notes on contributors

Amod Kumar

Amod Kumar received ME degree in Electronics Engineering from Punjab University, Chandigarh in 1985. He is currently working as senior scientist in Central Scientific Instruments Organisation, Chandigarh. He has about 27 years experience in Research and Development in the field of instrumentation. So far he has successfully developed Ultrasonic Flowmeter, Ultrasonic Viscometer, Rheometer, Turbiditymeter, Stack Opacity monitor, Myoelectric Arm, Depth of Anaesthesia Monitor and many other instruments. He has 11 publications in reputed journals and also presented 5 papers in National conferences. He visited Germany under DAAD fellowhip in 1987–88 His areas of interest are Digital Signal Processing and Soft Computing.

Sneh Anand

Sneh Anand did MTech in Controls and Instrumentation and PhD in Biomedical Engineering from IIT Delhi. At present, she is senor Professor in Centre for Biomedical Engineering at IIT Delhi. She has more than 30 years experience in teaching. She has worked extensively in the area of rehabilitation for disabled and has won 9 awards. Know-how of 6 instruments developed by her has gone to the industry. She has 85 publications in reputed national and international journals. Besides, there are 4 patents to her credit. Her areas of interest are rehabilitation engineering, reproductive medicine and non-conventional systems of medicine.