Electroencephalogram utilization and psychiatric comorbidities among children and adolescents with epilepsy in rural Southwestern Uganda

Objective This study aimed at describing routine electroencephalogram (EEG) findings among children and adolescents with a clinical diagnosis of epilepsy and determines how interictal EEG abnormalities vary with the psychiatric comorbidities. Methods We conducted a cross-sectional study among children and adolescents with epilepsy aged 5–18 years receiving care from a regional referral hospital in Southwestern Uganda. Psychiatric comorbidities were assessed using an adapted parent version of Child and Adolescent Symptom Inventory-5. Thirty-minute EEG samples were taken from routine EEG recordings that were locally performed and remotely interpreted for all participants. Results Of the 140 participants, 71 (50.7%) had normal EEG findings and 51 (36.4%) had epileptiform abnormalities while 18 (12.9%) had non-epileptiform. Of those who had epileptiform abnormalities on EEG, 23 (45.1%) were focal, 26 (51.0%) were generalized, and 2 (3.9%) were focal with bilateral spread. There was no significant association between the different psychiatric comorbidities and the interictal EEG abnormalities. Conclusions Among children and adolescents with a clinical diagnosis of epilepsy in Southwestern Uganda, only 36% showed epileptiform abnormalities on their EEG recordings. There was no association between the interictal EEG abnormalities and psychiatric comorbidities.

Permutation Entropy-Based Interpretability of Convolutional Neural Network Models for Interictal EEG Discrimination of Subjects with Epileptic Seizures vs. Psychogenic Non-Epileptic Seizures

The differential diagnosis of epileptic seizures (ES) and psychogenic non-epileptic seizures (PNES) may be difficult, due to the lack of distinctive clinical features. The interictal electroencephalographic (EEG) signal may also be normal in patients with ES. Innovative diagnostic tools that exploit non-linear EEG analysis and deep learning (DL) could provide important support to physicians for clinical diagnosis. In this work, 18 patients with new-onset ES (12 males, 6 females) and 18 patients with video-recorded PNES (2 males, 16 females) with normal interictal EEG at visual inspection were enrolled. None of them was taking psychotropic drugs. A convolutional neural network (CNN) scheme using DL classification was designed to classify the two categories of subjects (ES vs. PNES). The proposed architecture performs an EEG time-frequency transformation and a classification step with a CNN. The CNN was able to classify the EEG recordings of subjects with ES vs. subjects with PNES with 94.4% accuracy. CNN provided high performance in the assigned binary classification when compared to standard learning algorithms (multi-layer perceptron, support vector machine, linear discriminant analysis and quadratic discriminant analysis). In order to interpret how the CNN achieved this performance, information theoretical analysis was carried out. Specifically, the permutation entropy (PE) of the feature maps was evaluated and compared in the two classes. The achieved results, although preliminary, encourage the use of these innovative techniques to support neurologists in early diagnoses.

Low frequency novel interictal EEG biomarker for localizing seizures and predicting outcomes

Localizing hyperexcitable brain tissue to treat focal seizures remains challenging. We want to identify the seizure onset zone from interictal EEG biomarkers. We hypothesize that a combination of interictal EEG biomarkers, including a novel low frequency marker, can predict mesial temporal involvement and can assist in prognosis related to surgical resections. Interictal direct current wide bandwidth invasive EEG recordings from 83 patients implanted with 5,111 electrodes were retrospectively studied. Logistic regression was used to classify electrodes and patient outcomes. A feed-forward neural network was implemented to understand putative mechanisms. Interictal infraslow frequency EEG activity was decreased for seizure onset zone electrodes while faster frequencies such as delta (2–4 Hz) and beta-gamma (20–50 Hz) activity were increased. These spectral changes comprised a novel interictal EEG biomarker that was significantly increased for mesial temporal seizure onset zone electrodes compared to non-seizure onset zone electrodes. Interictal EEG biomarkers correctly classified mesial temporal seizure onset zone electrodes with a specificity of 87% and positive predictive value of 80%. These interictal EEG biomarkers also correctly classified patient outcomes after surgical resection with a specificity of 91% and positive predictive value of 87%. Interictal infraslow EEG activity is decreased near the seizure onset zone while higher frequency power is increased, which may suggest distinct underlying physiologic mechanisms. Narrowband interictal EEG power bands provide information about the seizure onset zone and can help predict mesial temporal involvement in seizure onset. Narrowband interictal EEG power bands may be less useful for predictions related to non-mesial temporal electrodes. Together with interictal epileptiform discharges and high frequency oscillations, these interictal biomarkers may provide prognostic information prior to surgical resection. Computational modeling suggests changes in neural adaptation may be related to the observed low frequency power changes.

Simultaneous Cardiac and Neurological Monitoring to Assess Chemical Exposures and Drug Toxicity in Xenopus Laevis

Simultaneous monitoring of electrocardiogram (ECG) and electroencephalogram (EEG) under chemical exposure requires innovative engineering techniques that can capture minute physiological changes in studied animal models. However, this is often administered with a bulky system that may cause signal distortions and discomfort for animals. We develop an integrated bioelectronic sensing system to provide simultaneous ECG and EEG assessment in real-time under chemical exposure for Xenopus laevis. The microelectrode array (MEA) membrane with integrated ECG and EEG sensing offers an opportunity to achieve multichannel noninvasive electrophysiological monitoring with favorable dimensions and spatial resolution. To validate the performance of our system, we assessed the ECG and EEG of Xenopus under exposure of Pentylenetetrazol (PTZ), an epilepsy-inducing drug. Effects of PTZ were detected with clear ECG and EEG alterations, including frequent ictal and interictal EEG events, 30 dB average EEG amplitude elevations, abnormal ECG morphology, and heart rate changes. Overall, our Xenopus-based real-time electrophysiology monitoring system holds high potential for many applications in drug screening and remote environmental toxicity monitoring.

Temporal Onset Focal Seizures Induced by Intermittent Photic Stimulation

The study aimed to review the clinical, radiological, and pathological findings and electroencephalogram (EEG) of pediatric epilepsy patients with temporal onset focal seizures induced by intermittent photic stimulation (IPS). Four patients with temporal onset photosensitivity focal seizures were analyzed. Three (75%) of the four patients were female. The average age of seizure-onset was 4.4 years. The interictal EEG showed both generalized and focal spike and waves in one patient and focal or multifocal spike and waves alone in three patients. Photoparoxysmal response (PPR) was evoked in all patients and showed generalized discharges (patients 2–4), both generalized and posterior discharges (patient 1). Both generalized and focal discharges could coexist in interictal discharges and PPR. The sensitive frequencies of PPR and photoconvulsive response (PCR) were 12–30 and 10–16 Hz, respectively, which were close to the occipital rhythm. In all patients, the onset of PCR was recorded, namely, the left anterior and mesial temporal lobe (TL), the left posterior TL, and the whole left TL, which showed two forms: the seizure of two patients was the onset of slow waves in the temporal area without spreading generalized discharges (patients 1 and 4), and the other one was fast rhythmic activities in the temporal area, spreading to the occipital area or gradually evolving into the generalized discharges (patients 2 and 3). During follow-up, except for patient 3 who had occasional seizures, the seizures of the remaining patients were under control. Temporal onset focal seizures could be induced by IPS. Temporal onset photosensitivity seizures were mostly easy to control with antiseizure drugs.

EEG Patterns in Patients with Prader–Willi Syndrome

Prader–Willi syndrome (PWS) is a rare disease determined by the loss of the paternal copy of the 15q11-q13 region, and it is characterized by hypotonia, hyperphagia, obesity, short stature, hypogonadism, craniofacial dysmorphisms, and cognitive and behavioral disturbances. The aims of this retrospective study were to analyze interictal EEG findings in a group of PWS patients and to correlate them with genetic, clinical, and neuroimaging data. The demographic, clinical, genetic, EEG, and neuroimaging data of seventy-four patients were collected. Associations among the presence of paroxysmal EEG abnormalities, genotype, and clinical and neuroimaging features were investigated. Four patients (5.4%) presented drug-sensitive epilepsy. Interictal paroxysmal EEG abnormalities—focal or multifocal—were present in 25.7% of the cases, and the normalization of the EEG occurred in about 25% of the cases. In 63.2% of the cases, the paroxysmal abnormalities were bilaterally localized over the middle–posterior regions. Brain magnetic resonance imaging (MRI) was performed on 39 patients (abnormal in 59%). No relevant associations were found between paroxysmal EEG abnormalities and all of the other variables considered. Interictal paroxysmal EEG abnormalities—in particular, with a bilateral middle–posterior localization—could represent an important neurological feature of PWS that is not associated with genotype, cognitive or behavioral endophenotypes, MRI anomalies, or prognosis.

Low frequency interictal EEG biomarker for localizing seizures

Objective: We want to identify seizure onset zone (SOZ) from interictal EEG biomarkers. We hypothesize that a combination of interictal EEG biomarkers, including a novel low frequency marker, can predict mesial temporal involvement and can assist in prognosis related to surgical resections. Methods: Interictal direct current wide bandwidth invasive EEG recordings from 83 patients implanted with 5,111 electrodes were retrospectively studied. Logistic regression was used to classify electrodes and patient outcomes. A feed-forward neural network was implemented to understand putative mechanisms. Results: Interictal infraslow frequency EEG activity was decreased for SOZ electrodes while faster frequencies such as delta (2-4 Hz) and beta-gamma (20-50 Hz) activity were increased. These spectral changes comprised a novel interictal EEG biomarker that was significantly increased for mesial temporal SOZ electrodes compared to non-SOZ electrodes. Interictal EEG biomarkers correctly classified mesial temporal SOZ electrodes with a specificity of 87% and positive predictive value of 80%. These interictal EEG biomarkers also correctly classified patient outcomes after surgical resection with a specificity of 91% and positive predictive value of 87%. Interpretation: Interictal infraslow EEG activity is decreased near the SOZ while higher frequency power is increased, suggesting distinct underlying physiologic mechanisms. Decreased interictal infraslow activity may reflect the loss of neural inhibition. Narrowband interictal EEG power bands provide information about the SOZ and can help predict mesial temporal involvement in seizure onset. Together with interictal epileptiform discharges and high frequency oscillations, these interictal biomarkers may provide prognostic information prior to surgical resection.

Cerebral Hemodynamic Evaluation of Main Cerebral Vessels in Epileptic Patients Based on Transcranial Doppler

Objective: To study whether there is a difference in peak and mean blood flow velocity between the left and right major cerebral vessels in patients with epilepsy.Methods: Sixteen patients with epilepsy underwent FDG18-PET-CT (PET) scan and electroencephalogram (EEG) examinations. Transcranial Doppler (TCD) was used to detect the peak flow velocity (PFV), mean flow velocity (MFV), and other hemodynamic indicators of bilateral anterior, middle, and posterior cerebral arteries in each patient. According to different patterns of the PET or interictal EEG, the differences in PFV, and MFV of corresponding vessels on both sides under different patterns were compared.Results: According to the PET of the low-metabolism region corresponding to the supplying artery, the PFV and MFV of the supplying artery in the low-metabolism region were lower than the value of the corresponding contralateral vessel. The PFV and MFV on the low metabolic side of PET were lower than that of the corresponding vessels on the opposite side. The PFV and MFV on the discharge side of interictal EEG were also lower than the PFV and MFV of the corresponding vessels on the opposite side. The MFV of posterior cerebral artery on the low metabolic side of PET or the interictal discharge side was significantly different from that of the contralateral vessels (P < 0.05). However, the other aforementioned differences in PFV and MFV did not achieve statistical significance.Conclusion: In epileptic patients, the PFV and MFV of main cerebral vessels on the PET hypometabolized side or the interictal discharge side was lower than that of corresponding vessels on the contralateral side. To some extent, the difference in the MFV of PCA between the bilateral sides can facilitate the lateral diagnosis of the epileptogenic zone.