From the Guest Editors ©1989-97 TECHPOOL STUDIOS, INC. Digital Processing of EEG Signals Tgram (EEG) signal data is the essential he digitization of electroencephalo- Paul B. Colditz1, Chris J. Burke2, Patrick Celka3 1 Perinatal Research Centre, University of Queensland, Royal Women’s Hospital 2 Neuroscience Department, Royal Children’s Hospital 3 Signal Processing Research Centre Queensland University of Technology first step in using computers to analyse and manipulate EEG data. Much research on EEG signal analysis was carried out before the widespread use of computers (e.g., compressed spectral arrays using fast Fourier transforms), but with the widespread use of commercial digital EEG recording equipment, this sort of analysis has now become part of routine practice [1]. How can currently available techniques for EEG analysis help the clinician in everyday practice and what new insights into neurological disorders does computerized analysis of EEG provide or promise for the future? First, computerized EEG analysis has always offered the possibility of providing objective data to supplement the EEG reader’s subjective interpretation of EEG recordings. Second, it can also help in data storage/retrieval and visualization. Third, the use of EEG includes event classification, particularly where this allows a clinical diagnosis to be made; prediction, such as of events like seizures; and monitoring of treatment. The EEG signals are inherently complicated due to their non-Gaussian, nonstationary, and often nonlinear nature as shown by most of the articles of this special issue (see also [3-5]). On top of that, the small amplitude of these signals reinforce their sensitivity to various artifacts and noise sources. The aim of this special issue is to shed light onto the recent digital techniques for processing EEG signals ranging from storage and artifact removal to event detection/classification and prediction issues. Compression Computerized handling of EEG data has of necessity led to developments in data storage, data reduction, and data display. Indeed, thousands of patients’ brains are monitored in neurological intensive-care units every year, producing a huge amount of data. Parsimonious representation of the EEG is therefore of paramount importance for a rapid evaluation of the patient state. The article by Agarwal and Gotman deals with this issue and proposes September/October 2001 IEEE ENGINEERING IN MEDICINE AND BIOLOGY an original approach for a parsimonious representation of EEG signals. Artifacts The EEG is a signal that derives from the activity of complex neuronal generators predominantly near the surface of the cerebral hemispheres. The EEG recorded from the surface of the brain (the “cortical EEG”) is altered as it passes through the skull and scalp. By the time the EEG signal arrives at the scalp surface to be transduced by an electrode, it is at a microvolt level and prone to environmental electrical noise and artifacts. Much time is spent in the acquisition of EEG with a technician and server needing to record other signals such as electrocardiogram (ECG) and respiration, as well as visual observation of gross body movement in order to determine manually whether the EEG signal contains artifacts or not. Particularly because of the low signal-to-noise ratio, the issue of artifact recognition is an important one that deserves further attention and refinement. The article by Celka et al. is devoted to the automatic detection and “intelligent” removal of short-time and high-amplitude events using Gaussian and non-Gaussian adaptive algorithms. Detection/Classification Event-detection strategies have been partly successful in developing systems for automatic seizure detection [6]. Methods still require refining to allow accurate classification. The main problems in seizure classification relate to artifacts, which may often feature bursts of activity that emulate seizures and the fact that seizure features may merge with normal features of background activity. Detection of seizures is part of a broader classification issue where interictal activity may be as important as the ictal activity itself; e.g., following hypoxic ischemic injury. Event-detection strategies are further complicated in infancy due to the major changes that occur in EEG activity between preterm, term, and older infants. In older children and adults, EEG abnormalities usually involve abnormal waveforms and features of the background activity. 0739-5175/01/$10.00©2001IEEE 21 Authorized licensed use limited to: Indian Institute of Technology - BHUBANESWAR. Downloaded on January 24,2024 at 13:52:54 UTC from IEEE Xplore. Restrictions apply. With neonatal EEG, because of the rapid developmental changes that occur in the EEG, abnormalities may also include alterations in the developmental features of the EEG. Furthermore, although neonatal EEGs have been studied for several decades and several features have been classified as normal or abnormal, the full range of normal variations in neonatal EEG is still uncertain [7]. Research is ongoing in these areas with data handling and EEG classification, and this is illustrated in the articles in this issue by Hoyer et al., Durka and Blinowska, and Boashash and Mesbah. Monitoring and Therapies Brain rescue therapy trials have recently begun in babies. Selecting those with a poor prognosis is vital to prevent trialing new therapies in babies who are not in need of them; i.e., babies that had a good prognosis anyway. These same EEG prognostication markers are likely to prove to have important roles in monitoring response to therapy and in specifically aiding clinical management such as by determining how long the therapy should be continued. The article by Hoyer et al. provides a framework for neonates’ brain monitoring using spectral-band power features and artificial neural network classifiers. Brain Rhythms Generators The major new thrust of research work in EEG analysis is to extract information from EEGs that is not available by visual analysis of the raw recording. While new ideas about mechanisms of EEG generation both in normal situations and in pathological states such as epileptic seizures [8] may point to the use of mathematical and signal processing methodologies, it is also the case that the pragmatic and expeditious use of signal processing techniques such as time-frequency (see the articles by Boashash and Mesbah, Celka et al., and Durka and Blinowska) and nonlinear methods (see articles by Paluš et al. and van Putten and Stam) may cast light on new models of EEG generation. It is this challenge that will test even further the ability of multiple disciplines to come together to break through professional barriers in the quest to understand the EEG signal from the level of its basic origin through to its potential to be used in therapy. Progress in this regard has been slow to date and there are many fronts from basic signal acquisition through to expert systems that need to be harnessed. However, the potential for benefit is huge. An example of a clinical need with immediate benefit is in accurately identifying those infants who have a poor prognosis after hypoxic-ischemic brain injury who may benefit from new experimental brain rescue therapies. References [1] P.K. Wang, Digital EEG in Clinical Practice. Philadelphia, PA: Lippincott Raven, 1996. [2] K. Lehnertz, J. Arnhold, P. Grassberger, and C.E. Elger, Chaos in Brain?, Singapore: World Scientific, 2000. [3] R.M. Dasheiff and DJ Vincent, “Frontier science in EEG,” Continuous Waveform Analysis. (EEG Suppl. 45), Elsevier Science, 1996. [4] N. Pradhan, P.E. Rapp, R. Sreenivasan, Nonlinear Dynamics and Brain Functioning. Commack, NY: Nova, 1999. [5] J. Gotman, “The use of computers in analysis and display of EEG and evoked potentials,” in Current Practice of Clinical EEG, D.D. Daly, T.A. Pedley, Eds. New York: Raven Press, 1999. [6] E.M. Mizrahi and P. Kellaway, Diagnosis and Management of Neontal Seizures. Philadelphia, PA: Lippincott-Raven, 1998. [7] F. Lopes da Silva, “EEG: analysis, theory, and practice,” in EEG Basic Principles, Clinical Application, and Related Fields. 4th ed., E. Neidermeyer, F. Lopes da Silva, Eds. Philadelphia, PA: Lippincott and Williams, 1999. IEEE ENGINEERING IN MEDICINE AND BIOLOGY MAGAZINE Editor Associate Editors Mark D. Wiederhold Science Applications International Corporation Book Reviews Paul King Vanderbilt University Industrial Affairs Barry N. Feinberg Center for Engineering Analysis, Northbrook, IL Government Activities Joseph D. Bronzino Trinity College Genomics Harold (Skip) Garner University of Texas Southwestern Medical Center Clinical Engineering Michael S. Bernstein Clarian Health Partners Editorial Board Howard I. Bassen Food and Drug Administration Krzysztof J. Cios University of Colorado at Denver Eugene Veklerov Lawrence Berkeley Laboratory Limin Luo Southeast University, Nanjing Max Viergever University Hospital Utrecht Edward J. Ciaccio Columbia University Jasjit Suri Marconi Medical Systems, Inc. Issues in Ethics John Fielder Villanova University Faces and Places John Enderle University of Connecticut International News Swamy Laxminarayan New Jersey Institute of Technology Patents Maurice M. Klee Fairfield, CT Student Activities Smita Sampath Johns Hopkins University Cellular & Tissue Engineering Maria Papadaki Massachusetts Institute of Technology Editorial Correspondence: Address to Mark D. Wiederhold, Science Applications International Corporation, 1200 Prospect St., Suite 400, La Jolla, CA 92037. Tel: +1 858 826 4128. Fax: +1 858 826 6648. Email: emb-magazine@ieee.org Indexed in: Current Contents (Clinical Practice), Engineering Index (Bioengineering Abstracts), Inspec, Excerpta Medica, Index Medicus, MEDLINE, and listed in Citation Index. All materials in this publication represent the views of the authors only, and not those of EMBS or IEEE. 22 IEEE ENGINEERING IN MEDICINE AND BIOLOGY IEEE PeriodicalsMagazines Department Managing Editor Brian Benbrook Art Director Janet Dudar Assoc. Art Director Debbie Cantillo Business Development Manager Susan Schneiderman +1 732 562 3946 sschneid@mag.ieee.org Fax: +1 732 981 1855 Senior Advertising Production Coordinator Cathline Tanis Production Director Robert Smrek Editorial Director Dawn Melley Staff Director, Publishing Operations Fran Zappulla September/October 2001 Authorized licensed use limited to: Indian Institute of Technology - BHUBANESWAR. Downloaded on January 24,2024 at 13:52:54 UTC from IEEE Xplore. Restrictions apply.
