Long-term EEG-video-audio monitoring: computer detection of focal EEG seizure patterns

Pauri, Flavia; Pierelli, Francesco; Chatrian, Gian Emilio; Erdly, William W.

doi:10.1016/0013-4694(92)90175-h

Cited by 71 publications

(31 citation statements)

References 6 publications

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“…biological signals.) Our results compare favorably with those obtained in similar studies (29)(30)(31)(32)(33) in terms of accuracy, speed of detection, and localization, while allowing, for the first time, quantitation of seizure intensity and duration. The size of our sample lends internal validity (34) to the results.…”

Section: Table 1 Interrelationship Between Detection Parameters T Ansupporting

confidence: 84%

Real‐Time Automated Detection and Quantitative Analysis of Seizures and Short‐Term Prediction of Clinical Onset

Frei²,

1998

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Summary: Purpose:We describe an algorithm for rapid realtime detection, quantitation, localization of seizures, and prediction of their clinical onset.Methods: Advanced digital signal processing techniques used in time-frequency localization, image processing, and identification of time-varying stochastic systems were used to develop the algorithm, which operates in generic or adaptable "modes." The "generic mode" was tested on (a) 125 partial seizures (each contained in a 10-min segment) involving the mesial temporal regions and recorded using depth electrodes from 16 subjects, and (b) 205 ten-minute segments of randomly selected interictal (nonseizure) data. The performance of the algorithm was compared with expert visual analysis, the current "gold standard." Results:The generic algorithm achieved perfect sensitivity and specificity (no false-positive and no false-negative detections) over the entire data set. Seizure intensity, a novel measure that seems clinically relevant, ranged between 35.7 and 6129. Detection was sufficiently rapid to allow prediction of clinical onset in 92% of seizures by a mean of 15.5 s.Conclusions: This algorithm, which was implemented with a personal computer, represents a definitive step toward rapid and accurate detection and prediction of seizures. It may also enable development of intelligent devices for automated seizure warning and treatment and stimulate new study of the dynamics of seizures and of the epileptic brain. Key Words: Epilepsy-Seizure-Detection-Prediction-Real time.The membership of the American Epilepsy Society recently ranked ' 'seizure prediction, early recognition, and blockage of seizures" as its primary research priority (1). The importance of accurate, automated, real-time detection, quantitation, and localization of seizures, a singular change in a highly complex, nonstationq time series [the EEG/electrocorticogram, (ECoG)], parallels its elusiveness (2-7). The inability to detect and quantify these changes rapidly, accurately, and automatically and to analyze such long-time series efficiently has limited the understanding of epilepsy and other dynamic diseases (8,9) and possibly the development of more effective and tolerable therapies as well. To be accurate, a solution must distinguish seizure signal changes from those caused by interictal epileptiform discharges,* or activity of extracerebral origin (artifacts) whose spectral domain often overlaps that of seizures. To be in real time, a solution must be highly computationally efficient, allowing on-line prospective rapid identification and quantitation of relevant changes, using short time windows, with limited a priori subject-specific data. We report a method that provides rapid automatic detection, quantitative analysis, and spatiotemporal localization of seizures. This algorithm, in its generic form, achieved sensitivity and specificity equal to that of expert visual analysis, the current ''gold standard," which it surpasses in its ability to quantitate seizure intensity objectively. In addition,...

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Section: Table 1 Interrelationship Between Detection Parameters T Ansupporting

confidence: 84%

Real‐Time Automated Detection and Quantitative Analysis of Seizures and Short‐Term Prediction of Clinical Onset

Frei²,

1998

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“…Reasons for FAR include normal and abnormal brain activity including sleep activity, rhythmic nonepileptiform activity like frontal intermittent rhythmic delta activity (FIRDA) or temporal intermittent rhythmic delta activity (TIRDA), or various artifacts (including chewing, tooth brushing, repetitive movements, eye movements, and so on) . Furthermore, the electrodes used (especially basal temporal electrodes) and the montages applied (multichannel seizure‐detection montage referenced against the average of Fz‐Cz‐Pz) can influence both sensitivity and specificity …”

Section: Discussionmentioning

confidence: 99%

“…Using Gotman's algorithm, Pauri et al investigated 461 hours of EEG of 12 patients with medically refractory focal epilepsy containing 253 EEG focal seizures. By testing different parameter settings, they obtained an optimum detection sensitivity of 81.4% and a FAR of 5.38/h.…”

Section: Non–patient‐specific Algorithms Tested In Clinical Settingsmentioning

confidence: 99%

Seizure detection using scalp‐EEG

Baumgartner

Koren

2018

Epilepsia

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Scalp electroencephalography (EEG)-based seizure-detection algorithms applied in a clinical setting should detect a broad range of different seizures with high sensitivity and selectivity and should be easy to use with identical parameter settings for all patients. Available algorithms provide sensitivities between 75% and 90%. EEG seizure patterns with short duration, low amplitude, circumscribed focal activity, high frequency, and unusual morphology as well as EEG seizure patterns obscured by artifacts are generally difficult to detect. Therefore, detection algorithms generally perform worse on seizures of extratemporal origin as compared to those of temporal lobe origin. Specificity (false-positive alarms) varies between 0.1 and 5 per hour. Low false-positive alarm rates are of critical importance for acceptance of algorithms in a clinical setting. Reasons for false-positive alarms include physiological and pathological interictal EEG activities as well as various artifacts. To achieve a stable, reproducible performance (especially concerning specificity), algorithms need to be tested and validated on a large amount of EEG data comprising a complete temporal assessment of all interictal EEG. Patient-specific algorithms can further improve sensitivity and specificity but need parameter adjustments and training for individual patients. Seizure alarm systems need to provide on-line calculation with short detection delays in the order of few seconds. Scalp-EEG-based seizure detection systems can be helpful in an everyday clinical setting in the epilepsy monitoring unit, but at the current stage cannot replace continuous supervision of patients and complete visual review of the acquired data by specially trained personnel. In an outpatient setting, application of scalp-EEG-based seizure-detection systems is limited because patients won't tolerate wearing widespread EEG electrode arrays for long periods in everyday life. Recently developed subcutaneous EEG electrodes may offer a solution in this respect.

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“…For scalp EEG, the applications for seizure detection include automatic labeling of seizures in the EEG for faster reporting 33,35,39 and activating radioactive tracer injection for improved seizure focus localization using SPECT. 7,42 Application of these methods to scalp EEG has been a difficult task given the many artifacts found in scalp EEG, 16,39 and this has meant that automated seizure detection is not widely used in standard clinical practice.…”

Section: Introductionmentioning

confidence: 99%

Seizure Detection Using Seizure Probability Estimation: Comparison of Features Used to Detect Seizures

et al. 2009

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This paper analyses seizure detection features and their combinations using a probability-based scalp EEG seizure detection framework developed by Marc Saab and Jean Gotman. Our method was evaluated on 525 h of data, including 88 seizures in 21 patients. The individual performances of the three features used by Saab and Gotman were compared to six alternative features, and combinations of these nine features were analyzed in order to find a superior detector. On a testing set with the combination of their three features, Saab and Gotman reported a sensitivity of 0.78, a false positive rate of 0.86/h, and a median detection delay of 9.8 s. Based on 10-fold cross-validation the testing performance of our implementation of their method achieved a sensitivity of 0.79, a false positive rate of 0.62/h, and a median detection delay of 21.3 s. A detector based on an alternative combination of features achieved sensitivity of 0.81, a false positive rate of 0.60/h, and a median detection delay of 16.9 s. By including filtering techniques, it was possible to achieve performance levels similar to Saab and Gotman using our implementation of their method, although this involved increases in detection delays. Of the seizure detection measures investigated, relative average amplitude, relative power, relative derivative, and coefficent of variation of amplitude provided the best performing combinations. These better-performing features can be employed together to make robust and reliable seizure detectors.

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Long-term EEG-video-audio monitoring: computer detection of focal EEG seizure patterns

Cited by 71 publications

References 6 publications

Real‐Time Automated Detection and Quantitative Analysis of Seizures and Short‐Term Prediction of Clinical Onset

Real‐Time Automated Detection and Quantitative Analysis of Seizures and Short‐Term Prediction of Clinical Onset

Seizure detection using scalp‐EEG

Seizure Detection Using Seizure Probability Estimation: Comparison of Features Used to Detect Seizures

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