EView--a workstation-based viewer for intensive clinical electroencephalography

Polysomnographic pattern recognition for automated classification of sleep-waking states in infants

A robust, automated pattern recognition system for polysomnography data targeted to the sleep-waking state and stage identification is presented. Five patterns were searched for: slow-delta and theta wave predominance in the background electro-encephalogram (EEG) activity; presence of sleep spindles in the EEG; presence of rapid eye movements in an electro-oculogram; and presence of muscle tone in an electromyogram. The performance of the automated system was measured indirectly by evaluating sleep staging, based on the experts' accepted methodology, to relate the detected patterns in infants over four months of post-term age. The set of sleep-waking classes included wakefulness, REM sleep and non-REM sleep stages I, II, and III-IV. Several noise and artifact rejection methods were implemented, including filters, fuzzy quality indices, windows of variable sizes and detectors of limb movements and wakefulness. Eleven polysomnographic recordings of healthy infants were studied. The ages of the subjects ranged from 6 to 13 months old. Six recordings counting 2665 epochs were included in the training set. Results on a test set (2,369 epochs from five recordings) show an overall agreement of 87.7% (kappa 0.840) between the automated system and the human expert. These results show significant improvements compared with previous work.

Design and evolution of a system for long-term electroencephalographic and video monitoring of epilepsy patients

The impact of technology, especially computer technology, on clinical neurophysiology instrumentation has yielded more than automation of paper-based electroencephalography. The new features now available as standard components of digital EEG/video have actually transformed practice (e.g., post hoc remontaging or filtering). At the same time, however, the fact that the signals are digital has led to a panoply of "tools in search of a problem." To incorporate appropriate tools in a practical and cost-effective fashion is a challenge that requires detailed, interdisciplinary knowledge, as well as concentrated effort. Although it is in the nature of research and development to follow a few blind alleys, too many projects are never productively implemented because excessive attention is focused on the obscure and not enough on whether and how the technique can actually help patients. In addition, a long-term view requires making the correct technology choices, to benefit from the consumer cost cycle and to ensure a continuous migration pathway. The lessons learned and principles established while bringing together the space, the people, and the equipment into an efficient, high-volume epilepsy monitoring unit are instructive.

Cerebral bioimpedance pattern as a function of psychiatric signs and symptoms

A new way of inferring and presenting psychiatric signs and symptoms as a correlate of cerebral bioimpedance pattern is proposed. The basal principles of bioimpedance have been applied through promising techniques already developed and used. The biophysical electrical shunt model meets the fundamental criteria for static and dynamic regional extracellular fluid volume alterations and ionic density, velocity and compositional changes in the same spatial and temporal units. Bioimpedance technology applied by a suitable method can potentially utilize bioimpedance essentials to display high sensitivity and specificity resolution revealing regional extracellular iono-liquid disturbances in the brain. We suggest that the theoretical model for utilizing bioimpedance as a function for estimating psychiatric signs and symptoms may open new horizons in the neuroscience of biotyping mental disorders.

Extraction of spatio-temporal signatures from depth EEG seizure signals based on objective matching in warped vectorial observations

In the field of epilepsy, the analysis of stereoelectroencephalographic (SEEG) signals recorded with depth electrodes provides major information on interactions between brain structures during seizures. A comprehensive methodology of comparing SEEG seizure recordings is presented. It proceeds in three steps: 1) segmentation of SEEG signals; 2) characterization and labeling of segments; and 3) comparison of observations coded as sequences of symbol vectors. The third step reports a vectorial extension of the Wagner and Fischer's algorithm to first, quantify similarities between observations and second, extract invariant sequences of events, referred to as spatiotemporal signatures. The study shows that two observations of nonequal duration can be matched by deforming the first one to optimally fit the second, under cost constraints. Results show that the methodology allows to exhibit signatures occurring during epileptic seizures and to point out different types of seizure patterns. The study brings objective results on reproducible interactions between brain structures during ictal periods and may help in the understanding of epileptogenic networks.

Status of quantitative EEG (QEEG) in clinical practice, 1994

Clinical quantitative EEG (qEEG) is a complex specialty that may include not only standard EEG but also digital ("paperless") EEG, topographic mapping, spectral analysis, spectral coherence, long latency and event related potentials (EP), significance probability mapping (SPM), dipole source localization methodology (DLM), and discriminant function analysis. There are three basic clinical uses: non-specific detection of organicity/encephalopathy, specific categorization of disease or clinical condition, and epileptic source localization. Extreme variations exist in the competency of laboratories practicing clinical qEEG; universally agreed upon standards of practice have not been established but there are a number of efforts to do so. As expected, the clinical value of qEEG to patients varies similarly. Criticisms of qEEG have now been answered: Color displays need not be deceptive. Statistical "capitalization upon chance" can be easily avoided. By training and with newer analytic procedures, artifacts can be recognized and often removed. Data based upon spectral analysis and EP can reliably classify clinical conditions thereby demonstrating a greater sensitivity to EEG/EP data than possible by conventional visual inspection. QEEG is clearly of clinical value when performed in concert with standard EEG and analyzed by clinicians with demonstrated competency in standard EEG followed by specialized training and demonstrated competency in qEEG. QEEG is not a simple substitute for conventional EEG and cannot be seen as a substitute for clinical competence. Although continuing to develop, qEEG technology has matured sufficiently and is now well established. Concerns regarding its clinical use have primarily resulted from its misapplication and misinterpretation stemming, largely, from inadequate personnel training and expertise.