Developing a novel epileptic discharge localization algorithm for electroencephalogram infantile spasms during hypsarrhythmia

Epileptic spasms: A South African overview of aetiologies, interventions, and outcomes

AIM

To better understand the aetiologies of epileptic spasms in infants, as well as the safety and efficacy of high dose corticosteroids in tuberculosis and human immunodeficiency virus (HIV) endemic resource-limited settings.

METHOD

This was a retrospective analysis of infants with epileptic spasms managed at the tertiary referral centres in the Western Cape, South Africa.

RESULTS

Of 175 children with epileptic spasms, the median age at onset was 6 months (interquartile range 4-8 months). Structural aetiologies were most common (115 out of 175 [66%]), with two-thirds related to perinatal insults. A lead time to treatment (LTTT) of less than 1 month was more likely in the epileptic encephalopathy/developmental and epileptic encephalopathy (DEE) group: 58 out of 92 (63%), compared to 28 out of 76 (37%) of those with developmental encephalopathy (p = 0.001). Failure to recognize preceding developmental delay was common. Ninety-nine children (57%) received first line hormonal therapy such as adrenocorticotropic hormone. A total of 111 out of 172 children (65%) from the developmental encephalopathy and epileptic encephalopathy/DEE groups had clinical and/or electroencephalogram resolution of spasms within 14 days. In our population, children in whom an aetiology could not be identified were statistically more likely to have moderate to profound developmental delay at 1 year of age: 33 out of 44 (p = 0.001). Based on reported incidence of epileptic spasms, 23 to 58 cases per annum would be expected but a far smaller proportion presented to our centres.

INTERPRETATION

Whilst this is the largest cohort of infants with epileptic spasms from sub-Saharan Africa, the study size is less than expected; this may reflect misdiagnosis and failure of referral pathways. Despite a reported shorter LTTT, infants with DEE had worse developmental outcomes compared to international studies. Hormonal therapy was safe and effective in our setting, despite exposure to high levels of tuberculosis and HIV.

WHAT THIS PAPER ADDS

The number of unreferred cases of epileptic spasms in South Africa remains high. Caregivers and health care workers in primary care facilities often fail to recognize developmental delay. The burden of disease from hypoxic-ischaemic encephalopathy remains high in our resource-limited setting. Hormonal treatment (e.g. adrenocorticotropic hormone) was safe and effective despite the high prevalence of human immunodeficiency virus and tuberculosis.

Discrimination of secondary hypsarrhythmias to Zika virus congenital syndrome and west syndrome based on joint moments and entropy measurements

Hypsarrhythmia is a specific chaotic morphology, present in the interictal period of the electroencephalogram (EEG) signal in patients with West Syndrome (WS), a severe form of childhood epilepsy and that, recently, was also identified in the examinations of patients with Zika Virus Congenital Syndrome (ZVCS). This innovative work proposes the development of a computational methodology for analysis and differentiation, based on the time-frequency domain, between the chaotic pattern of WS and ZVCS hypsarrhythmia. The EEG signal time-frequency analysis is carried out from the Continuous Wavelet Transform (CWT). Four joint moments-joint mean-[Formula: see text], joint variance-[Formula: see text], joint skewness-[Formula: see text], and joint kurtosis-[Formula: see text]-and four entropy measurements-Shannon, Log Energy, Norm, and Sure-are obtained from the CWT to compose the representative feature vector of the EEG hypsarrhythmic signals under analysis. The performance of eight classical types of machine learning algorithms are verified in classification using the k-fold cross validation and leave-one-patient-out cross validation methods. Discrimination results provided 78.08% accuracy, 85.55% sensitivity, 73.21% specificity, and AUC = 0.89 for the ANN classifier.

Quantitative EEG biomarkers for epilepsy and their relation to chemical biomarkers

The electroencephalogram (EEG) is the most important method to diagnose epilepsy. In clinical settings, it is evaluated by experts who identify patterns visually. Quantitative EEG is the application of digital signal processing to clinical recordings in order to automatize diagnostic procedures, and to make patterns visible that are hidden to the human eye. The EEG is related to chemical biomarkers, as electrical activity is based on chemical signals. The most well-known chemical biomarkers are blood laboratory tests to identify seizures after they have happened. However, research on chemical biomarkers is much less extensive than research on quantitative EEG, and combined studies are rarely published, but highly warranted. Quantitative EEG is as old as the EEG itself, but still, the methods are not yet standard in clinical practice. The most evident application is an automation of manual work, but also a quantitative description and localization of interictal epileptiform events as well as seizures can reveal important hints for diagnosis and contribute to presurgical evaluation. In addition, the assessment of network characteristics and entropy measures were found to reveal important insights into epileptic brain activity. Application scenarios of quantitative EEG in epilepsy include seizure prediction, pharmaco-EEG, treatment monitoring, evaluation of cognition, and neurofeedback. The main challenges to quantitative EEG are poor reliability and poor generalizability of measures, as well as the need for individualization of procedures. A main hindrance for quantitative EEG to enter clinical routine is also that training is not yet part of standard curricula for clinical neurophysiologists.

Quantitative Characteristics of Hypsarrhythmia in Infantile Spasms

Infantile spasms is a type of epilepsy characterized by clinical seizures termed "spasms" and often an electroencephalographic (EEG) pattern known as hypsarrhythmia. Multiple studies have shown that the interrater reliability for human visual recognition of hypsarrhythmia is poor. Quantitative measurements of this EEG pattern would provide objective basis for identification; however, the basic temporal and spectral characteristics of hypsarrhythmia have never been assessed. Thus, we measured EEG amplitude and power spectra in 21 infantile spasms patients before and after treatment, as well as 21 control subjects. The hypsarrhythmia EEG pattern was associated with (1) high broadband amplitude, especially in frontal and central brain regions, (2) high median power in the delta and alpha frequency bands, and (3) low spectral edge frequency. Our results indicate that hypsarrhythmia can be quantitatively distinguished from data without hypsarrhythmia. Introduction of these quantitative measures into clinical practice may increase diagnostic accuracy, expediting proper treatment and improving outcomes.

Identification of Hypsarrhythmia in Children with Microcephaly Infected by Zika Virus

Hypsarrhythmia is an electroencephalographic pattern specific to some epileptic syndromes that affect children under one year of age. The identification of this pattern, in some cases, causes disagreements between experts, which is worrisome since an inaccurate diagnosis can bring complications to the infant. Despite the difficulties in visually identifying hypsarrhythmia, options of computerized assistance are scarce. Aiming to collaborate with the recognition of this electropathological pattern, we propose in this paper a mathematical index that can help electroencephalography experts to identify hypsarrhythmia. We performed hypothesis tests that indicated significant differences in the groups under analysis, where the p-values were found to be extremely small.

Novel characterization method of impedance cardiography signals using time-frequency distributions

The purpose of this document is to describe a methodology to select the most adequate time-frequency distribution (TFD) kernel for the characterization of impedance cardiography signals (ICG). The predominant ICG beat was extracted from a patient and was synthetized using time-frequency variant Fourier approximations. These synthetized signals were used to optimize several TFD kernels according to a performance maximization. The optimized kernels were tested for noise resistance on a clinical database. The resulting optimized TFD kernels are presented with their performance calculated using newly proposed methods. The procedure explained in this work showcases a new method to select an appropriate kernel for ICG signals and compares the performance of different time-frequency kernels found in the literature for the case of ICG signals. We conclude that, for ICG signals, the performance (P) of the spectrogram with either Hanning or Hamming windows (P = 0.780) and the extended modified beta distribution (P = 0.765) provided similar results, higher than the rest of analyzed kernels. Graphical abstract Flowchart for the optimization of time-frequency distribution kernels for impedance cardiography signals.