Detection of recurrent activation patterns across focal seizures: Application to seizure onset zone identification

Vila-Vidal, Manel; Príncipe, Alessandro; Ley, Miguel; Deco, Gustavo; Campo, Adrià Tauste; Rocamora, Rodrigo

doi:10.1016/j.clinph.2017.03.040

Cited by 11 publications

(21 citation statements)

References 21 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…These results are not a special case for the particular seizure used as an example, and we found similar patterns regarding the structure of the reconstruction for all the other seizures. This consistency of the connectivity patterns across seizures is in agreement with the consistency of relative activation patterns that it was found for the same patient in [41]. In Fig.…”

Section: Application To Eeg Datasupporting

confidence: 90%

See 1 more Smart Citation

Inferring directed networks using a rank-based connectivity measure

et al. 2019

Self Cite

View full text Add to dashboard Cite

Inferring the topology of a network using the knowledge of the signals of each of the interacting units is key to understanding real-world systems. One way to address this problem is using datadriven methods like cross-correlation or mutual information. However, these measures lack the ability to distinguish the direction of coupling. Here, we use a rank-based nonlinear interdependence measure originally developed for pairs of signals. This measure not only allows one to measure the strength but also the direction of the coupling. Our results for a system of coupled Lorenz dynamics show that we are able to consistently infer the underlying network for a subrange of the coupling strength and link density. Furthermore, we report that the addition of dynamical noise can benefit the reconstruction. Finally, we show an application to multichannel electroencephalographic recordings from an epilepsy patient.

show abstract

Section: Application To Eeg Datasupporting

confidence: 90%

“…We now show an application of our method to EEG recordings from an epilepsy patient (patient 1 in [41,42]). These recordings were performed prior to and independently from our study in the context of this patient's pre-surgical diagnostics in Hospital del Mar, Barcelona, Spain.…”

Section: Application To Eeg Datamentioning

confidence: 99%

Inferring directed networks using a rank-based connectivity measure

et al. 2019

Self Cite

View full text Add to dashboard Cite

show abstract

“…The scientific community agrees in the importance of spectral quantities both in temporal and frequency domain as descriptors of the neurological signal. Relative amount of signal power in frequency bands of interests and temporal events characterized by critical amplitudes are indeed considered pathological biomarkers [19,20,1,15]. In Truong [19] et al the authors analyze the Melbourne dataset using standard spectral analysis methods as Fast Fourier Transform (FFT) to measure correlations across channels, in order to infer which are the mostly involved in the seizure generation.…”

Section: Related Workmentioning

confidence: 99%

“…In Truong [19] et al the authors analyze the Melbourne dataset using standard spectral analysis methods as Fast Fourier Transform (FFT) to measure correlations across channels, in order to infer which are the mostly involved in the seizure generation. In the work of Vila et al [20] some criteria for the localization of Seizure Onset Zone (SOZ) are established using spectral measures. The study is performed on seven patients, and characterizes the transition between interictal and ictal states.…”

Section: Related Workmentioning

confidence: 99%

Classification of Epileptic Activity Through Temporal and Spatial Characterization of Intracranial Recordings

D’Amario

Arnulfo

Nobili

et al. 2020

Computational Intelligence Methods for Bioinformatics and Biostatistics

View full text Add to dashboard Cite

Focal epilepsy is a chronic condition characterized by hyper-activity and abnormal synchronization of a specific brain region. For pharmacoresistant patients, the surgical resection of the critical area is considered a valid clinical solution, therefore, an accurate localization is crucial to minimize neurological damage. In current clinical routine the characterization of the epileptogenic zone (EZ) is performed using invasive methods, such as Stereo-Electroencephalography (SEEG). Medical experts perform the tag of neural electrophysiological recordings by visually inspecting the acquired data, a highly time consuming and subjective procedure. Here we show the results of an automatic multi-modal classification method for the evaluation of critical areas in focal epileptic patients. The proposed method represents an attempt in the characterization of brain areas which integrates the anatomical information on neural tissue, inferred using Magnetic Resonance Imaging (MRI) in combination with spectral features extracted from SEEG recordings.

show abstract

“…Our particular type of analysis can therefore be useful in the SOZ lateralization of patients undergoing invasive seizure monitoring. Some of these techniques are used for the characterization of seizure activity, the so-called ictal activity (9)(10)(11)(12)(13)(14)(15)(16)(17)(18)(19)(20)(21)(22), while others are used for the analysis of the seizure-free interval . The characterization of the seizure-free interval, often referred to as interictal interval, can reveal aspects of brain dynamics that may help in the localization of the SOZ without the need to wait for seizures to occur.…”

Section: Introductionmentioning

confidence: 99%

Seizure Onset Zone Lateralization Using a Non-linear Analysis of Micro vs. Macro Electroencephalographic Recordings During Seizure-Free Stages of the Sleep-Wake Cycle From Epilepsy Patients

et al. 2020

View full text Add to dashboard Cite

The application of non-linear signal analysis techniques to biomedical data is key to improve our knowledge about complex physiological and pathological processes. In particular, the use of non-linear techniques to study electroencephalographic (EEG) recordings can provide an advanced characterization of brain dynamics. In epilepsy these dynamics are altered at different spatial scales of neuronal organization. We therefore apply non-linear signal analysis to EEG recordings from epilepsy patients derived with intracranial hybrid electrodes, which are composed of classical macro contacts and micro wires. Thereby, these electrodes record EEG at two different spatial scales. Our aim is to test the degree to which the analysis of the EEG recorded at these different scales allows us to characterize the neuronal dynamics affected by epilepsy. For this purpose, we retrospectively analyzed long-term recordings performed during five nights in three patients during which no seizures took place. As a benchmark we used the accuracy with which this analysis allows determining the hemisphere that contains the seizure onset zone, which is the brain area where clinical seizures originate. We applied the surrogate-corrected non-linear predictability score (ψ), a non-linear signal analysis technique which was shown previously to be useful for the lateralization of the seizure onset zone from classical intracranial EEG macro contact recordings. Higher values of ψ were found predominantly for signals recorded from the hemisphere containing the seizure onset zone as compared to signals recorded from the opposite hemisphere. These differences were found not only for the EEG signals recorded with macro contacts, but also for those recorded with micro wires. In conclusion, the information obtained from the analysis of classical macro EEG contacts can be complemented by the one of micro wire EEG recordings. This combined approach may therefore help to further improve the degree to which quantitative EEG analysis can contribute to the diagnostics in epilepsy patients.

show abstract

Detection of recurrent activation patterns across focal seizures: Application to seizure onset zone identification

Abstract: The present method may be used to improve epileptogenic identification as well as pinpoint additional regions that are functionally altered during ictal events.

Cited by 11 publications

References 21 publications

Inferring directed networks using a rank-based connectivity measure

Inferring directed networks using a rank-based connectivity measure

Classification of Epileptic Activity Through Temporal and Spatial Characterization of Intracranial Recordings

Seizure Onset Zone Lateralization Using a Non-linear Analysis of Micro vs. Macro Electroencephalographic Recordings During Seizure-Free Stages of the Sleep-Wake Cycle From Epilepsy Patients

Contact Info

Product

Resources

About