Trends in the use of automated algorithms for the detection of high‐frequency oscillations associated with human epilepsy

Sindhu, Kavyakantha Remakanthakurup; Staba, Richard J.; Lopour, Beth A.

doi:10.1111/epi.16622

Cited by 37 publications

(40 citation statements)

References 101 publications

(192 reference statements)

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“…When comparing our neuromorphic SNN with other HFO detectors proposed in the literature 35 , 36 , several differences and commonalities become evident. As a conceptual difference, the SNN proposed here models many of the features found in biological neural processing systems, such as the temporal dynamics of the neuron and synapse elements, or the variability in their time constants, refractory periods, and synaptic weights.…”

Section: Discussionmentioning

confidence: 92%

An electronic neuromorphic system for real-time detection of high frequency oscillations (HFO) in intracranial EEG

et al. 2021

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The analysis of biomedical signals for clinical studies and therapeutic applications can benefit from embedded devices that can process these signals locally and in real-time. An example is the analysis of intracranial EEG (iEEG) from epilepsy patients for the detection of High Frequency Oscillations (HFO), which are a biomarker for epileptogenic brain tissue. Mixed-signal neuromorphic circuits offer the possibility of building compact and low-power neural network processing systems that can analyze data on-line in real-time. Here we present a neuromorphic system that combines a neural recording headstage with a spiking neural network (SNN) processing core on the same die for processing iEEG, and show how it can reliably detect HFO, thereby achieving state-of-the-art accuracy, sensitivity, and specificity. This is a first feasibility study towards identifying relevant features in iEEG in real-time using mixed-signal neuromorphic computing technologies.

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Section: Discussionmentioning

confidence: 92%

An electronic neuromorphic system for real-time detection of high frequency oscillations (HFO) in intracranial EEG

et al. 2021

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“…Different from epileptic spikes, HFOs do not propagate, which is an advantage if the EZ needs to be localized precisely (9, 16). A growing number of studies relates the presence of HFOs to surgical outcome (20, 28). A prospective, automated definition of HFOs renders HFO analysis more generalizable (7, 18, 19).…”

Section: Discussionmentioning

confidence: 99%

“…Investigations in the clinical relevance of the HFOs have been facilitated by automated or semi-automated detection algorithms (20). Here we apply a fully automated definition of HFOs, which we previously optimized on visual markings in a dataset of the Montreal Neurological Institute (21) and then validated on independently recorded data from Zurich (7).…”

Section: Introductionmentioning

confidence: 99%

Blinded study: prospectively defined high frequency oscillations predict seizure outcome in individual patients

Dimakopoulos

Mégevand

Boran

et al. 2020

Preprint

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BackgroundInterictal high frequency oscillations (HFO) are discussed as biomarkers for epileptogenic brain tissue that should be resected in epilepsy surgery to achieve seizure freedom. The prospective classification of tissue sampled by individual electrode contacts remains a challenge. We have developed an automated, prospective definition of clinically relevant HFO in intracranial EEG (iEEG) from MNI Montreal and tested it in iEEG from Zurich. We here validate the algorithm on iEEG recorded in an independent epilepsy center so that HFO analysis was blinded to seizure outcome.MethodsWe selected consecutive patients from Geneva University Hospitals who underwent resective epilepsy surgery with postsurgical follow-up > 12 months. We analyzed long-term iEEG recordings during non-rapid eye movement (NREM) sleep that we segmented into intervals of 5 min. HFOs were defined in the ripple (80-250 Hz) and the fast ripple (FR, 250-500 Hz) frequency band. Contacts with the highest rate of ripples co-occurring with FR (FRandR) designated the HFO area. If the HFO area was not fully resected and the patient suffered from recurrent seizures (ILAE 2-6), this was classified as a true positive (TP) prediction.ResultsWe included iEEG recordings from 16 patients (median age 32 y, range [18-53]) with stereotactic depth electrodes and/or with subdural electrode grids (median follow-up 27 mo, range [12-55]). The HFO area had high test-retest reliability across intervals (median dwell time 95%). We excluded two patients with dwell time < 50% from further analysis.The HFO area was fully included in the resected volume in 2/4 patients who achieved postoperative seizure freedom (ILAE 1, specificity 50%) and was not fully included in 9/10 patients with recurrent seizures (ILAE > 1, sensitivity 90%), leading to an accuracy of 79%.ConclusionsWe validated the automated procedure to delineate the clinical relevant HFO area in individual patients of an independently recorded dataset and achieved the same good accuracy as in our previous studies.SignificanceThe reproducibility of our results across datasets is promising for a multicienter study testing the clinical application of HFO detection to guide epilepsy surgery.

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“…Automated detection of HFOs remains challenging, as it produces inconsistent results across methods, though several automatic detectors have been developed ( Crépon et al, 2010 ; Zelmann et al, 2010 , 2012 ; Cimbalnik et al, 2018 ; Donos et al, 2020 ; Lachner-Piza et al, 2020 ; Lai et al, 2020 ; for review, see Remakanthakurup Sindhu et al, 2020 ). One significant challenge that remains for automatic detectors is distinguishing HFOs from artifactual spikes as well as epileptiform EEG spikes, because their spectrograms overlap.…”

Section: Introductionmentioning

confidence: 99%

HFOApp: A MATLAB Graphical User Interface for High-Frequency Oscillation Marking

Zhou¹,

Noto²,

Sharma³

et al. 2021

eNeuro

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Trends in the use of automated algorithms for the detection of high‐frequency oscillations associated with human epilepsy

Cited by 37 publications

References 101 publications

An electronic neuromorphic system for real-time detection of high frequency oscillations (HFO) in intracranial EEG

An electronic neuromorphic system for real-time detection of high frequency oscillations (HFO) in intracranial EEG

Blinded study: prospectively defined high frequency oscillations predict seizure outcome in individual patients

HFOApp: A MATLAB Graphical User Interface for High-Frequency Oscillation Marking

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