White matter alterations in focal to bilateral tonic-clonic seizures

Maher, Christina; D’Souza, Arkiev; Zeng, Rui; Barnett, Michael; Kavehei, Omid; Nikpour, Armin; Wang, Chenyu

doi:10.1101/2021.10.21.21265249

Cited by 3 publications

(5 citation statements)

References 56 publications

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“…Figure 3 portrays a theoretical process for using EEG and dMRI to obtain the optimal electrode placement. Items (a) and (b) were imaging pipelines explored in our previous work [93] while (c) and (d) illustrate a schematic workflow for the addition of EEG data, resulting in (e), identifying the optimal implant site for a long-term sub-scalp electrode or device.…”

Section: Combining Electroencephalography With Diffusion Magnetic Res...mentioning

confidence: 99%

Seizure detection with reduced electroencephalogram channels: research trends and outlook

et al. 2023

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Epilepsy is a prevalent condition characterized by recurrent, unpredictable seizures. Monitoring with surface electroencephalography (EEG) is the gold standard for diagnosing epilepsy, but a time-consuming, uncomfortable and sometimes ineffective process for patients. Further, using EEG over a brief monitoring period has variable success, dependent on patient tolerance and seizure frequency. The availability of hospital resources and hardware and software specifications inherently restrict the options for comfortable, long-term data collection, resulting in limited data for training machine-learning models. This mini-review examines the current patient journey, providing an overview of the current state of EEG monitoring with reduced electrodes and automated channel reduction methods. Opportunities for improving data reliability through multi-modal data fusion are suggested. We assert the need for further research in electrode reduction to advance brain monitoring solutions towards portable, reliable devices that simultaneously offer patient comfort, perform ultra-long-term monitoring and expedite the diagnosis process.

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Section: Combining Electroencephalography With Diffusion Magnetic Res...mentioning

confidence: 99%

Seizure detection with reduced electroencephalogram channels: research trends and outlook

et al. 2023

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“…Image acquisition was described previously [12]. Briefly, all scans were acquired on the same GE Discovery TM MR750 3T scanner (GE Medical Systems, Milwaukee, WI).…”

Section: Image Acquisitionmentioning

confidence: 99%

“…Diffusion-weighted MRI (dMRI) is an in-vivo neuroimaging technique that allows extraction of analytical features commonly used to examine the white matter in the brain [8]. White matter abnormalities observed in dMRI-derived metrics (such as fractional anisotropy and mean diffusivity) and graph theory measures have been linked to focal epilepsy and FBTCS [9,10,11,12]. Structural connectomes derived from dMRI can be used to represent the presence and strength of connections between two discrete brain regions (represented as "nodes") via the white matter tracts (represented as "edges").…”

Section: Introductionmentioning

confidence: 99%

Deep learning distinguishes focal epilepsy groups using connectomes: Feasibility and clinical implications

Maher

Tang

D’Souza

et al. 2023

Preprint

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The application of deep learning models to evaluate connectome data is gaining interest in epilepsy research. Deep learning may be a useful initial tool to partition connectome data into network subsets for further analysis. Few prior works have used deep learning to examine structural connectomes from patients with focal epilepsy. We evaluated whether a deep learning model applied to whole-brain connectomes could classify 28 participants with focal epilepsy from 20 controls and identify nodal importance for each group. Participants with epilepsy were further grouped based on whether they had focal seizures that evolved into bilateral tonic-clonic seizures (17 with, 11 without). The trained neural network classified patients from controls with an accuracy of 72.92%, while the seizure subtype groups achieved a classification accuracy of 67.86%. In the patient subgroups, the nodes and edges deemed important for accurate classification were also clinically relevant, indicating the model's interpretability. The current work expands the evidence for the potential of deep learning to extract relevant markers from clinical datasets. Our findings offer a rationale for further research interrogating structural connectomes to obtain features that can be biomarkers and aid the diagnosis of seizure subtypes.

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“…The number of required electrodes and electrode placement can be guided by a patient's magnetic resonance imaging (MRI) scan and EEG. We recently examined the role of structural connectivity in seizure propagation in focal epilepsy patients [6]. Diffusion MRI can be a valuable aid for patient-specific determination of electrode placement.…”

Section: The Need For Electrode Reductionmentioning

confidence: 99%

“…2 portrays a theoretical process for using both EEG and MRI to obtain the optimal electrode placement. Items (a) and (b) were explored in our previous work [6] whilst (c) and (d), circled in red, are examined in Section 4 of this paper. where seizure detection and logging may take place.…”

Section: The Need For Electrode Reductionmentioning

confidence: 99%

Towards long term monitoring: Seizure detection with reduced electroencephalogram channels

Maher

Yang

Truong

et al. 2021

Preprint

Self Cite

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Epilepsy is a prevalent condition characterised by recurrent, unpredictable seizures. The diagnosis of epilepsy is by surface electroencephalography (EEG), a time-consuming and uncomfortable process for patients. The diagnosis of seizures using EEG over a brief monitoring period has variable success, dependent on patient tolerance and seizure frequency. Further, the availability of hospital resources, and hardware and software specifications inherently limit the capacity to perform long-term data collection whilst maintaining patient comfort. The application and maintenance of the standard number of electrodes restrict recording time to a maximum of approximately ten days. This limited monitoring period also results in limited data for machine learning models for seizure detection and classification. This work examines the literature on the impact of reduced electrodes on data accuracy and reliability in seizure detection. We present two electrode ranking models that demonstrate the decline in seizure detection performance associated with reducing electrodes. We assert the need for further research in electrode reduction to advance solutions toward portable, reliable devices that can simultaneously provide patient comfort, long-term monitoring and contribute to multimodal patient care solutions.

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White matter alterations in focal to bilateral tonic-clonic seizures

Cited by 3 publications

References 56 publications

Seizure detection with reduced electroencephalogram channels: research trends and outlook

Seizure detection with reduced electroencephalogram channels: research trends and outlook

Deep learning distinguishes focal epilepsy groups using connectomes: Feasibility and clinical implications

Towards long term monitoring: Seizure detection with reduced electroencephalogram channels

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