Automatic detection of interictal ripples on scalp EEG to evaluate the effect and prognosis of ACTH therapy in patients with infantile spasms

Epilepsia ◽  
2021 ◽  
Author(s):  
Wei Wang ◽  
Hua Li ◽  
Jiaqing Yan ◽  
Herui Zhang ◽  
Xiaonan Li ◽  
...  
Keyword(s):  
Automatic Detection ◽  
Infantile Spasms ◽  
Scalp Eeg ◽  
Acth Therapy

scholarly journals Automated Detection of Ripple Oscillations in Long-Term Scalp EEG from Patients with Infantile Spasms

2020 ◽  
Author(s):  
Colin M. McCrimmon ◽  
Aliza Riba ◽  
Cristal Garner ◽  
Amy L. Maser ◽  
Daniel W. Shrey ◽  
...  
Keyword(s):  
Spatial Distribution ◽  
Visual Analysis ◽  
Infantile Spasms ◽  
Scalp Eeg ◽  
Automated Algorithm ◽  
Automated Method ◽  
Eeg Data ◽  
Acth Therapy ◽  
The Difference

AbstractObjectiveScalp high frequency oscillations (HFOs) are a promising biomarker of epileptogenicity in infantile spasms (IS) and many other epilepsy syndromes, but prior studies have relied on visual analysis of short segments of data due to the prevalence of artifacts in EEG. Therefore, we set out to develop a fully automated method of HFO detection that can be applied to large datasets, and we sought to robustly characterize the rate and spatial distribution of HFOs in IS.MethodsWe prospectively collected long-term scalp EEG data from 13 subjects with IS and 18 healthy controls. For patients with IS, recording began prior to diagnosis and continued through initiation of treatment with adenocorticotropic hormone (ACTH). The median analyzable EEG duration was 18.2 hours for controls and 83.9 hours for IS subjects (∼1300 hours total). Ripples (80-250 Hz) were detected in all EEG data using an automated algorithm.ResultsHFO rates were substantially higher in patients with IS compared to controls. In IS patients, HFO rates were higher during sleep compared to wakefulness (median 5.5/min and 2.9/min, respectively; p =0.002); controls did not exhibit a difference in HFO rate between sleep and wakefulness (median 0.98/min and 0.82/min, respectively). Spatially, the difference between IS patients and controls was most salient in the central/posterior parasaggital region, where very few HFOs were detected in controls. In IS subjects, ACTH therapy significantly decreased the rate of HFOs.DiscussionHere we show for the first time that a fully automated algorithm can be used to detect HFOs in long-term scalp EEG, and the results are accurate enough to clearly discriminate healthy subjects from those with IS. We also provide a detailed characterization of the spatial distribution and rates of HFOs associated with infantile spasms, which may have relevance for diagnosis and assessment of treatment response.

THE OUTCOME OF ACTH THERAPY FOR INFANTILE SPASMS

2006 ◽  
Vol 37 (S 1) ◽  
Author(s):  
YH Zhang ◽  
GL Chen ◽  
J Qin ◽  
X Wu
Keyword(s):  
Infantile Spasms ◽  
Acth Therapy

scholarly journals ACTH therapy in infantile spasms: side effects.

1980 ◽  
Vol 55 (9) ◽  
pp. 664-672 ◽  
Author(s):  
R Riikonen ◽  
M Donner
Keyword(s):  
Side Effects ◽  
Infantile Spasms ◽  
Acth Therapy

scholarly journals Automated detection of ripple oscillations in long-term scalp EEG from patients with infantile spasms

2020 ◽  
Author(s):  
Colin Matthew McCrimmon ◽  
Aliza Riba ◽  
Cristal Garner ◽  
Amy L Maser ◽  
Donald J Phillips ◽  
...  
Keyword(s):  
Infantile Spasms ◽  
Automated Detection ◽  
Scalp Eeg

SEQUENTIAL CT STUDIES OF 24 CHILDREN WITH INFANTILE SPASMS ON ACTH THERAPY

2008 ◽  
Vol 25 (4) ◽  
pp. 475-480 ◽  
Author(s):  
Masatoshi Ito ◽  
Tastuo Takao ◽  
Takehiko Okuno ◽  
Haruki Mikawa
Keyword(s):  
Infantile Spasms ◽  
Acth Therapy ◽  
Ct Studies

scholarly journals Endocrine complications of ACTH therapy of infantile spasms

1984 ◽  
Vol 18 (11) ◽  
pp. 1225-1225
Author(s):  
J Perheentupa ◽  
R Riikonen ◽  
L Dunkel ◽  
O Simell
Keyword(s):  
Infantile Spasms ◽  
Acth Therapy ◽  
Endocrine Complications

scholarly journals Automatic detection of generalized paroxysmal fast activity in Lennox-Gastaut syndrome using a bi-modal EEG time-frequency feature

2020 ◽  
Author(s):  
Amir Omidvarnia ◽  
Aaron E.L. Warren ◽  
Linda J. Dalic ◽  
Mangor Pedersen ◽  
John S. Archer ◽  
...  
Keyword(s):  
Automatic Detection ◽  
Time Frequency ◽  
Scalp Eeg ◽  
Epileptiform Discharges ◽  
Fast Activity ◽  
Brain Maps ◽  
Detection Approach ◽  
Interictal Eeg ◽  
Eeg Recordings ◽  
Frequency Feature

AbstractObjectiveMark-up of generalized interictal epileptiform discharges (IEDs) on EEG is an important step in the diagnosis and characterization of epilepsy. However, manual EEG mark-up is a time-consuming, subjective, and highly specialized task where the human reviewer needs to visually inspect a large amount of data to facilitate accurate clinical decisions. The objective of this study was to develop a framework for automated detection of generalized paroxysmal fast activity (GPFA), which is a characteristic type of generalized IED seen in scalp EEG recordings of patients with Lennox-Gastaut syndrome (LGS), a severe form of drug-resistant generalized epilepsy.MethodsWe studied 13 children with LGS who had GPFA events in their interictal EEG recordings. Time-frequency information derived from manually marked IEDs across multiple EEG channels was used to automatically detect similar events in each patient’s interictal EEG. We validated true positives and false positives of the proposed spike detection approach using both standalone scalp EEG and simultaneous EEG-functional MRI (EEG-fMRI) recordings.ResultsGPFA events displayed a consistent low-high frequency arrangement in the time-frequency domain. This ‘bi-modal’ spectral feature was most prominent over frontal EEG channels. Our automatic detection approach using this feature identified likely epileptic events with similar time-frequency properties to the manually marked GPFAs. Brain maps of EEG-fMRI signal change during these automatically detected IEDs were comparable to the EEG-fMRI brain maps derived from manual IED mark-up.ConclusionGPFA events have a characteristic bi-modal time-frequency feature that can be automatically detected from scalp EEG recordings in patients with LGS. Validity of this time-frequency feature is demonstrated by EEG-fMRI analysis of automatically detected events, which recapitulates the brain maps we have previously shown to underlie generalized IEDs in LGS.SignificanceThis study provides a novel methodology that paves the way for quick, automated, and objective inspection of generalized IEDs in LGS. The proposed framework may be extendable to a wider range of epilepsy syndromes in which monitoring the burden of epileptic activity can aid clinical decision-making. For example, automated quantification of generalized discharges may permit faster assessment of treatment response and estimation of future seizure risk.

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