Automatic multimodal detection for long-term seizure documentation in epilepsy

Fürbass, Franz; Kampusch, Stefan; Kaniušas, Eugenijus; Koren, Johannes; Pirker, Susanne; Hopfengärtner, R.; Stefan, Hermann; Kluge, Tilmann; Baumgartner, Christoph

doi:10.1016/j.clinph.2017.05.013

Cited by 50 publications

(50 citation statements)

References 15 publications

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“…Changes in excitation, slow, and fast inhibition were found a few tens of seconds before the seizure, which was considered as pre‐onset period (Wendling et al ., ). Recent EEG‐based algorithms are able to detect seizures 10 s (Fürbass et al ., ) to even 1 min (Hocepied et al ., ) before their onset. Therefore, we relate I rise to the seizure onset if I rise locates within the period from 30 s before onset to offset (i.e., between the first vertical solid line and the second vertical dashed line in Fig.…”

Section: Methodsmentioning

confidence: 99%

Dynamics underlying interictal to ictal transition in temporal lobe epilepsy: insights from a neural mass model

Fan

Gaspard

Legros

et al. 2018

Eur J of Neuroscience

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We propose an approach that combines a neural mass model and clinical intracranial electroencephalographic (iEEG) recordings to explore the potential pathophysiological mechanisms (at the neuronal population level) of ictogenesis. Thirty iEEG recordings from 10 temporal lobe epilepsy (TLE) patients around seizure onset were investigated. Physiologically meaningful parameters [average excitatory (A ), slow (B), and fast (G) inhibitory synaptic gain] were identified during interictal to ictal transition. Four ratios (A /G, A /B, A /(B + G), and B/G) were derived from these parameters, and their evolution over time was analyzed. The excitation/inhibition ratio increased around seizure onset and decreased before seizure offset, indicating the impairment and re-emergence of excitation/inhibition balance around seizure onset and before seizure offset, respectively. Moreover, the slow inhibition may have an earlier effect on excitation/inhibition imbalance. We confirm the decrease in excitation/inhibition ratio upon seizure termination in human temporal lobe epilepsy, as revealed by optogenetic approaches both in vivo in animal models and in vitro. The increase in excitation/inhibition ratio around seizure occurrence could be an indicator to detect seizures.

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Section: Methodsmentioning

confidence: 99%

Dynamics underlying interictal to ictal transition in temporal lobe epilepsy: insights from a neural mass model

Fan

Gaspard

Legros

et al. 2018

Eur J of Neuroscience

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“…• Detection algorithms generally perform better on seizures of temporal lobe origin as compared to those of extratemporal origin • In an outpatient setting, use of scalp-EEG-based seizure-detection is limited because patients won't tolerate wearing EEG electrode arrays in everyday life reduction of the number of electrodes is desirable. [24][25][26] Use of only 8 frontal and temporal electrodes yielded an average detection sensitivity of 79%; and use of only 7 temporal, parietal, and occipital electrodes an average detection sensitivity of 68%, as compared to a sensitivity of 84% for all 21 channels of the International 10-20-system. 25 After data acquisition, all algorithms apply some kind of artifact rejection.…”

Section: Key Pointsmentioning

confidence: 95%

“…[24][25][26] Use of only 8 frontal and temporal electrodes yielded an average detection sensitivity of 79%; and use of only 7 temporal, parietal, and occipital electrodes an average detection sensitivity of 68%, as compared to a sensitivity of 84% for all 21 channels of the International 10-20-system. 25 After data acquisition, all algorithms apply some kind of artifact rejection. Thereafter, detection of EEG-seizure patterns is usually based on characteristic electrographic changes during seizures with respect to frequency, amplitude, and/or rhythmicity.…”

Section: Key Pointsmentioning

confidence: 95%

“…When using multiple EEG channels, the montage can be of critical importance for performance of the algorithm . Furthermore, both for computational reasons and for patient comfort (especially in outpatient settings), a selection and reduction of the number of electrodes is desirable . Use of only 8 frontal and temporal electrodes yielded an average detection sensitivity of 79%; and use of only 7 temporal, parietal, and occipital electrodes an average detection sensitivity of 68%, as compared to a sensitivity of 84% for all 21 channels of the International 10‐20‐system …”

Section: Algorithms For Scalp‐eeg–based Seizure Detectionmentioning

confidence: 99%

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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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“…EMG was included into multimodal systems for detection of TCS, along with accelerometers in wearable devices, and along with EEG and ECG signals in the EMUs . Although the small‐scale studies reported an improved performance in the multimodal setting as compared to the unimodal one, the sensitivity and FAR of these multimodal TCS detectors was not superior to what the 2 large‐scale multicenter studies reported on the unimodal, EMG‐based devices.…”

Section: Emg‐based Automated Detection Of Convulsive Seizuresmentioning

confidence: 99%

Detection of convulsive seizures using surface electromyography

2018

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Bilateral (generalized) tonic-clonic seizures (TCS) increase the risk of sudden unexpected death in epilepsy (SUDEP), especially when patients are unattended. In sleep, TCS often remain unnoticed, which can result in suboptimal treatment decisions. There is a need for automated detection of these major epileptic seizures, using wearable devices. Quantitative surface electromyography (EMG) changes are specific for TCS and characterized by a dynamic evolution of low- and high-frequency signal components. Algorithms targeting increase in high-frequency EMG signals constitute biomarkers of TCS; they can be used both for seizure detection and for differentiating TCS from convulsive nonepileptic seizures. Two large-scale, blinded, prospective studies demonstrated the accuracy of wearable EMG devices for detecting TCS with high sensitivity (76%-100%). The rate of false alarms (0.7-2.5/24 h) needs further improvement. This article summarizes the pathophysiology of muscle activation during convulsive seizures and reviews the published evidence on the accuracy of EMG-based seizure detection.

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Automatic multimodal detection for long-term seizure documentation in epilepsy

Cited by 50 publications

References 15 publications

Dynamics underlying interictal to ictal transition in temporal lobe epilepsy: insights from a neural mass model

Dynamics underlying interictal to ictal transition in temporal lobe epilepsy: insights from a neural mass model

Seizure detection using scalp‐EEG

Detection of convulsive seizures using surface electromyography

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