Computational characteristics of interictal EEG as objective markers of epileptic spasms

Abstract

ObjectiveFavorable neurodevelopmental outcomes in epileptic spasms (ES) are tied to early diagnosis and prompt treatment, but uncertainty in the identification of the disease can delay this process. Therefore, we investigated five computational electroencephalographic (EEG) measures as markers of ES.MethodsWe measured 1) amplitude, 2) power spectra, 3) entropy, 4) long-range temporal correlations, via detrended fluctuation analysis (DFA) and 5) functional connectivity of EEG data from ES patients (n=40 patients) and healthy controls (n=20 subjects), with multiple blinded measurements during wakefulness and sleep for each patient.ResultsIn ES patients, EEG amplitude was significantly higher in all electrodes. Shannon and permutation entropy were lower in ES patients than control subjects, while DFA intercept values in ES patients were significantly higher than control subjects. DFA exponent values were not significantly different between the groups. EEG functional connectivity networks in ES patients were significantly stronger than controls. Using logistic regression, a multi-attribute classifier was derived that accurately distinguished cases from controls (area under curve of 0.96).ConclusionsComputational EEG features successfully distinguish ES patients from controls in a large, blinded study.SignificanceThese objective EEG markers, in combination with other clinical factors, may speed the diagnosis and treatment of the disease, thereby improving long-term outcomes.HighlightsObjective computational EEG features may aid diagnosis of epileptic spasms (ES)ES EEG has increased delta and theta power and decreased entropy relative to controlsStronger functional connectivity networks differentiate ES patients from controls

Infant functional networks are modulated by state of consciousness and circadian rhythm

“…First, we calculated the degree for each node by summing the weights of the connections incident to that node ( Bullmore & Sporns, 2009 ; Rubinov & Sporns, 2010 ). The degree is related to our measurement of network strength, which is an important marker for distinguishing sleep and wakefulness ( Smith et al, 2020 ) and can also be an indicator of pathological networks ( Shrey et al, 2018 ). However, the weighted calculation of degree has an advantage over strength, as it does not require a threshold to binarize the network.…”

“…In adults, functional networks measured during wakefulness exhibited higher density and lower clustering coefficient than those measured during sleep ( Chu et al, 2012 ), such that networks during sleep exhibit small-world properties ( Ferri et al, 2008 ). In contrast, infant networks exhibit greater strength during sleep compared to wakefulness, for both healthy subjects and those with epilepsy ( Smith et al, 2020 ). Moreover, the network characteristics are a function of the specific sleep stage.…”

“…Most have relied on short clips, ∼3–20 min in duration, recorded during daytime sleep. Only one study ( Smith et al, 2020 ) included a direct comparison of networks during sleep and wakefulness. Filling this knowledge gap is critical for understanding the role that functional networks play in health and disease.…”