Ictal time-irreversible intracranial EEG signals as markers of the epileptogenic zone

Schindler, Kaspar; Rummel, Christian; Andrzejak, Ralph G.; Goodfellow, Marc; Zubler, Frédéric; Abela, Eugenio; Wiest, Roland; Pollo, Claudio; Steimer, Andreas; Gast, Heidemarie

doi:10.1016/j.clinph.2016.07.001

Cited by 33 publications

(36 citation statements)

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“…Here a LBP code reflects the relational aspects between consecutive values of the iEEG signals, i.e., whether their amplitudes increase or decrease. We find that LBP codes are more efficient than other symbolization methods, e.g., directed horizontal graphs [15] that assign an integer input and output degree to each time point.…”

Section: A Symbolization Using Local Binary Patterns (Lbp)mentioning

confidence: 93%

“…A class of data analysis methods is referred to as symbolization, which describes the process of transforming experimental measurements into a series of discrete symbols. Symbolization is particularly interesting for iEEG analysis [14], [15], because it faithfully preserves dominant dynamical signal characteristics while significantly increasing the efficiency of detecting and quantifying information contained in real-world time series [13]. Symbolization may be efficiently achieved by mapping a sequence of iEEG samples into a bit string, i.e.…”

Section: A Symbolization Using Local Binary Patterns (Lbp)mentioning

confidence: 99%

“…Another challengecommon to all methods-is to reliably detect seizures from a small number of examples due to the uneven distribution with much longer interictal than ictal time periods, and the patientspecific seizure dynamics [7], [12]. To address these challenges, one promising option is to develop computationally efficient and fast learning algorithms by combining methods from symbolic dynamics [5], [13], [14], [15], [1] and brain-inspired hyperdimensional computing [16], [17]. In this paper, we describe the following contributions:…”

Section: Introductionmentioning

confidence: 99%

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Laelaps: An Energy-Efficient Seizure Detection Algorithm from Long-term Human iEEG Recordings without False Alarms

Burrello

Cavigelli

Schindler

et al. 2019

2019 Design, Automation &Amp; Test in Europe Conference &Amp; Exhibition (DATE)

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We propose Laelaps, an energy-efficient and fast learning algorithm with no false alarms for epileptic seizure detection from long-term intracranial electroencephalography (iEEG) signals. Laelaps uses end-to-end binary operations by exploiting symbolic dynamics and brain-inspired hyperdimensional computing. Laelaps's results surpass those yielded by state-of-the-art (SoA) methods [1], [2], [3], including deep learning, on a new very large dataset containing 116 seizures of 18 drug-resistant epilepsy patients in 2656 hours of recordings-each patient implanted with 24 to 128 iEEG electrodes. Laelaps trains 18 patient-specific models by using only 24 seizures: 12 models are trained with one seizure per patient, the others with two seizures. The trained models detect 79 out of 92 unseen seizures without any false alarms across all the patients as a big step forward in practical seizure detection. Importantly, a simple implementation of Laelaps on the Nvidia Tegra X2 embedded device achieves 1.7×-3.9× faster execution and 1.4×-2.9× lower energy consumption compared to the best result from the SoA methods. Our source code and anonymized iEEG dataset are freely available at http://ieeg-swez.ethz.ch.

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Section: A Symbolization Using Local Binary Patterns (Lbp)mentioning

confidence: 93%

Section: A Symbolization Using Local Binary Patterns (Lbp)mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Laelaps: An Energy-Efficient Seizure Detection Algorithm from Long-term Human iEEG Recordings without False Alarms

Burrello

Cavigelli

Schindler

et al. 2019

2019 Design, Automation &Amp; Test in Europe Conference &Amp; Exhibition (DATE)

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“…Dass zeitliche Asymmetrie typisch für epileptiforme Signale ist, wurde schon vor 40 Jahren von Pierre Gloor beschrieben [20]. Zeitliche Asymmetrie ist ein Hinweis auf einen nicht-linearen Signalgenerator, und nichtlineare Signale konnten als Biomarker für epileptogenes Gewebe identifiziert werden [19,21].…”

Section: Netzwerkanalysen Einzelner Ieeg Signaleunclassified

Intrakranielle EEG Netzwerkanalysen bei fokalen Epilepsien

Schindler

2018

Klin Neurophysiol

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ZusammenfassungDie Theorie der Netzwerke hat sich zu einem bedeutsamen Werkzeug bei der Analyse komplexer Systeme entwickelt und ihre Methoden werden erfolgreich angewendet, um die Dynamik epileptischer Anfälle zu verstehen. In diesem Artikel wird eine nicht-technische Einführung in die Konzepte der Netzwerktheorie gegeben. Ausgehend von intrakraniellen EEG Signalen wird demonstriert wie daraus funktionale Netzwerke hergeleitet werden können und wie sich diese Netzwerke visualisieren und analysieren lassen. Wichtige Begriffe wie „Knoten“, „Verbindung“ und „Knoten-Zentralität“ werden erklärt und ein experimentell prüfbares Modell der Netzwerkdynamik fokaler Anfälle wird vorgestellt. Dieses Modell impliziert, dass die hierarchische und modulare Netzwerkstruktur unseres Gehirns dazu prädestiniert, dass lokale neurogliale Aktivität unkontrollierbar wird. Die „Neuro-Netzwerkwissenschaft“ dürfte in naher Zukunft zu besserer Diagnostik und Therapie für Epilepsiepatienten führen.

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“…Despite a number of approaches to test for nonlinearity [39,40], compelling support for nonlinearity in brain dynamics on the meso-and macro-scale as well as for any brain (dys)functioning has as yet not been provided. A prominent counterexample is epilepsy, for which abundant evidence for nonlinear brain dynamics not only during seizures but also during the seizure-free interval has been produced (see, e.g., [41][42][43][44][45][46][47][48][49][50][51][52][53][54][55]).…”

Section: Introductionmentioning

confidence: 99%

Capturing time-varying brain dynamics

Lehnertz

Geier

Rings

et al. 2017

EPJ Nonlinear Biomed. Phys.

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The human brain is a complex network of interacting nonstationary subsystems, whose complicated spatial-temporal dynamics is still poorly understood. Deeper insights can be gained from recent improvements of time-series-analysis techniques to assess strength and direction of interactions together with methodologies for deriving and characterizing evolving networks from empirical time series. We here review these developments, and by taking the example of evolving epileptic brain networks, we discuss the progress that has been made in capturing and understanding brain dynamics that varies on time scales ranging from seconds to years.

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Ictal time-irreversible intracranial EEG signals as markers of the epileptogenic zone

Cited by 33 publications

References 36 publications

Laelaps: An Energy-Efficient Seizure Detection Algorithm from Long-term Human iEEG Recordings without False Alarms

Laelaps: An Energy-Efficient Seizure Detection Algorithm from Long-term Human iEEG Recordings without False Alarms

Intrakranielle EEG Netzwerkanalysen bei fokalen Epilepsien

Capturing time-varying brain dynamics

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