EpiScan: Online seizure detection for epilepsy monitoring units

Hartmann, M.; Furbas, F.; Perko, Hannes; Skupch, A.M.; Lackmayer, Katharina; Baumgartner, Christoph; Kluge, Tilmann

doi:10.1109/iembs.2011.6091506

Cited by 30 publications

(23 citation statements)

References 10 publications

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“…This method was developed over several years by a team of physicians, mathematicians and medical experts (Schachinger et al, 2006;Perko et al, 2007;Kluge et al, 2009;Hartmann et al, 2011;Fürbass et al, 2012). It is intended to analyze the EEG ad-hoc and to act as an online detection system.…”

Section: Discussionmentioning

confidence: 99%

Prospective multi-center study of an automatic online seizure detection system for epilepsy monitoring units

Fürbass

Ossenblok

Hartmann

et al. 2015

Clinical Neurophysiology

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Section: Discussionmentioning

confidence: 99%

Prospective multi-center study of an automatic online seizure detection system for epilepsy monitoring units

Fürbass

Ossenblok

Hartmann

et al. 2015

Clinical Neurophysiology

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“…Hartmann et al developed an online seizure‐detection algorithm (EpiScan) based on a periodic waveform analysis detecting rhythmic EEG patterns and an adaptation module automatically adjusting the algorithm to patient‐specific EEG properties. The algorithm was evaluated on 4300 hours of unselected EEG recordings from 48 patients with TLE containing 186 seizures.…”

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

confidence: 99%

Seizure detection using scalp‐EEG

2018

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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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“…AIT has developed a high performance epilepsy seizure detection (ESD) system for long-term EEG monitoring [1]. The algorithm is based on a frequency domain method called Periodic Waveform Analysis (PWA) and the time domain analysis of epileptiform sequences (EWS) [2].…”

Section: A Epilepsy and Automatic Seizure Detectionmentioning

confidence: 99%

“…The derivation of the false alarm rate is more complex because seizure alerts are clustered to sub-alerts with duration of 30 seconds. Each subalert that does not intersect with a true seizure marker (basic [1]. The overall performance across all patients is done by averaging the patient-wise sensitivities and false alarm rates.…”

Section: A Cost Functionmentioning

confidence: 99%

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Automatic optimization of parameters for seizure detection systems

Dollfuss

Hartmann

Skupch

et al. 2013

2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

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A parameter optimization method for an automatic seizure detection algorithm using the Nelder Mead algorithm is presented. A suitable cost function for joint optimization of sensitivity and false alarm rate is proposed. The optimization is done using EEG datasets from 23 patients and validated on datasets from another 23 patients. The resulting sensitivity was 82.3% with a false alarm rate of 0.24 FA/h. This is a reduction of the false alarm rate by 1.58 FA/h with an acceptable loss of sensitivity of 4.3%.

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EpiScan: Online seizure detection for epilepsy monitoring units

Cited by 30 publications

References 10 publications

Prospective multi-center study of an automatic online seizure detection system for epilepsy monitoring units

Prospective multi-center study of an automatic online seizure detection system for epilepsy monitoring units

Seizure detection using scalp‐EEG

Automatic optimization of parameters for seizure detection systems

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