Computer-aided quantification of EEG spike and sharp wave characteristics

Ktonas, P.Y.; Luoh, W.M; Kejariwal, M.; Reilly, Edward L.; Seward, M.A

doi:10.1016/0013-4694(81)90137-1

Cited by 26 publications

(8 citation statements)

References 10 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The smallest database is a 320 seconds segment from one epileptic patient [9] while the largest is 864 hours of recording from 65 patients [12]. Some are relatively artefact free [13] while some contain difficult waveforms [14]. A more recent approach sees the use of segments as training set and long-term recordings as test sets [15].…”

Section: Assessment Of Computerized Analysis Algorithms For One Dimenmentioning

confidence: 99%

Assessment of computerized analysis algorithms for one dimension physiologic data with multiple databases

Lee

Bister

Salleh

et al. 2008

2008 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society

View full text Add to dashboard Cite

Software technology enables computerized analysis to offer second opinion in various screening and diagnostic tasks to assist the clinicians. Yet, the performance of these computerized methods for medical images is questioned by experts in CAD research, owing to the use of different databases and criteria for evaluating the computer results for comparison. This paper intends to substantiate this statement by illustrating the effects of such issues with the use of 1D physiologic data and multiple databases. For this purpose, the detection of desaturation events in Sp02 and spike events in EEG are used. This is the first time that comparison between different algorithms on a common basis is carried out on an individual effort. The appraisal for all the algorithms is made on the same databases and criteria. It is surprising to find that issues for 2/3D images concur with those found in 1D data here. In evaluating the accuracy of a new algorithm, a single independent database gives results fast. This paper reveals weaknesses of such an approach. It is hoped that the supportive evidence shown here is enough for researchers to innovate a better platform for credibility in reporting performance comparison of computerized analysis algorithms.

show abstract

Section: Assessment Of Computerized Analysis Algorithms For One Dimenmentioning

confidence: 99%

Assessment of computerized analysis algorithms for one dimension physiologic data with multiple databases

Lee

Bister

Salleh

et al. 2008

2008 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society

View full text Add to dashboard Cite

show abstract

“…In these contexts, summative methods of digital quantification of tonic EEG events might permit a gain in both the efficiency and the reliability of data reduction (see refs. [17][18][19][20][21][22][23][24]. One such method described in Hoffmann et al (2S) does not recognize phasic events as events per se but preserves their contribution to the overall variance of period and amplitude measurements in the classic frequency bands (beta, sigma, alpha, theta, and delta).…”

Section: Discussionmentioning

confidence: 99%

Quantitative Description of Sleep Stage Electrophysiology Using Digital Period Analytic Techniques

Moffitt

Wells

Sussman

et al. 1984

Sleep

View full text Add to dashboard Cite

Summary:The purpose of the current study was to assess the overlap in variance of two procedures for the quantification of sleep electrophysiology: conventional stage scoring and computer quantification of tonic activity. Data were collected on 24 nights from eight subjects and were scored according to a modified set of Rechtschaffen and Kales criteria and submitted to a period-analytic computer analysis. Following this, discriminant function analyses were performed on the data for each night to predict the visual stage scores from the computer-generated data. The results indicate a very high degree of predictive accuracy (91.05%) supporting the contention that the computer-quantified data set includes the variance normally captured by stage scoring. The implications of computer quantification of sleep electrophysiology are discussed. Key Words: Sleep stagesPeriod analysis-Electroencephalography-Psychophysiology.Traditional methodologies used for the study of sleep electrophysiology and psychophysiology have principally concerned the reduction of data to stages so that the vast amounts of data collected can be reduced to a point where statistical comparisons between nights and/or subjects can be made. Historically these quantification procedures have relied on visual examination of the data and have thus emphasized easily observable visual events such as spindles, K-complexes, electromyographic (EMG) reduction, and electroencephalographic (EEG) desynchronization (1). However, recent advances in computer technology and application have suggested new ways in which such data sets can be evaluated. One direction this work has followed has been the development of machinery to automate the stage scoring process or to detect relevant phasic events (2)(3)(4)(5)(6)(7)(8).A different approach uses computers to quantify the electrophysiological data to explore psychophysiological variations within stage as well as comparing different nights or subjects using techniques other than stage scoring (9)(10)(11)(12)(13)(14)(15). In some cases these later endeavors do not directly quantify phasic events but rather include them in evaluations of tonic activity.

show abstract

“…The following provides a short summary of the most common methods to the spike detection problem in the literature (Gotman, 1999;Wilson & Emerson, 2002 Mimetic methods are based on the hypothesis that the process of identifying a transient waveform in EEG recordings as spike could be divided into well-defined steps representing the reasons and expertise of a neurophysiologist (Gotman, 1982;Gotman & Gloor, 1976;Guedes de Oliveira, 1983;Ktonas, 1983;Ktonas, Luoh, Kejariwal, Reilly, & Seward, 1981). Distinctive attributes of the spikes such as slope, height, duration and sharpness are compared with values provided by the neurophysiologists.…”

Section: Automated Spike Detection Analysismentioning

confidence: 99%