Ambulatory EEG to Classify the Epilepsy Syndrome

A Nanoclay-Enhanced Hydrogel for Self-Adhesive Wearable Electrophysiology Electrodes with High Sensitivity and Stability

Hydrogel-based wet electrodes are the most important biosensors for electromyography (EMG), electrocardiogram (ECG), and electroencephalography (EEG); but, are limited by poor strength and weak adhesion. Herein, a new nanoclay-enhanced hydrogel (NEH) has been reported, which can be fabricated simply by dispersing nanoclay sheets (Laponite XLS) into the precursor solution (containing acrylamide, N, N'-Methylenebisacrylamide, ammonium persulfate, sodium chloride, glycerin) and then thermo-polymerizing at 40 °C for 2 h. This NEH, with a double-crosslinked network, has nanoclay-enhanced strength and self-adhesion for wet electrodes with excellent long-term stability of electrophysiology signals. First of all, among existing hydrogels for biological electrodes, this NEH has outstanding mechanical performance (93 kPa of tensile strength and 1326% of breaking elongation) and adhesion (14 kPa of adhesive force), owing to the double-crosslinked network of the NEH and the composited nanoclay, respectively. Furthermore, this NEH can still maintain a good water-retaining property (it can remain at 65.4% of its weight after 24 h at 40 °C and 10% humidity) for excellent long-term stability of signals, on account of the glycerin in the NEH. In the stability test of skin-electrode impedance at the forearm, the impedance of the NEH electrode can be stably kept at about 100 kΩ for more than 6 h. As a result, this hydrogel-based electrode can be applied for a wearable self-adhesive monitor to highly sensitively and stably acquire EEG/ECG electrophysiology signals of the human body over a relatively long time. This work provides a promising wearable self-adhesive hydrogel-based electrode for electrophysiology sensing; which, will also inspire the development of new strategies to improve electrophysiological sensors.

Sub-scalp electroencephalography: A next-generation technique to study human neurophysiology

Sub-scalp electroencephalography (ssEEG) is emerging as a promising technology in ultra-long-term electroencephalography (EEG) recordings. Given the diversity of devices available in this nascent field, uncertainty persists about its utility in epilepsy evaluation. This review critically dissects the many proposed utilities of ssEEG devices including (1) seizure quantification, (2) seizure characterization, (3) seizure lateralization, (4) seizure localization, (5) seizure alarms, (6) seizure forecasting, (7) biomarker discovery, (8) sleep medicine, and (9) responsive stimulation. The different ssEEG devices in development have individual design philosophies with unique strengths and limitations. There are devices offering primarily unilateral recordings (24/7 EEG^TM SubQ, Neuroview^TM, Soenia® UltimateEEG™), bilateral recordings (Minder™, Epios™), and even those with responsive stimulation capability (EASEE®). We synthesize the current knowledge of these ssEEG systems. We review the (1) ssEEG devices, (2) use case scenarios, (3) challenges and (4) suggest a roadmap for ideal ssEEG designs.

Prediction Value of Epilepsy Secondary to Inferior Cavity Hemorrhage Based on Scalp EEG Wave Pattern in Deep Learning

Objective

To search the predictive value of epilepsy secondary to acute subarachnoid hemorrhage (aSAH) based on EEG wave pattern in deep learning.

Methods

A total of 156 cases of secondary epilepsy with lower cavity hemorrhage in our hospital were selected and divided into the late epilepsy group and the early epilepsy group according to seizure time, and the nonseizure group and the seizure group according to seizure condition. General data of patients were collected, the EEG types of each group were analyzed, and the disease recurrence rate, treatment effect, and symptom onset time were compared.

Results

Rapid and slow and rapid blood flow velocity were the main abnormal manifestations of epilepsy secondary to inferior cavity hemorrhage, accounting for 33.3% and 18.6%, respectively. Compared with the seizure group, the proportion of type ii and type iii in the nonseizure group was higher, and the proportion of type ii and type iii in the early epilepsy group was higher than in the late epilepsy group (P < 0.05). The diagnostic accuracy, missed diagnosis rate, misdiagnosis rate, specificity, and sensitivity of the EEG wave pattern were 94.9%, 3.2%, 1.9%, 91.7%, and 96.2%, respectively. Compared with the early epilepsy group, the recurrence rate of type iii and type iv in the late epilepsy group was higher (P < 0.05). The effective rates of the attack group and the nonattack group were 72.7% and 97.0%, respectively. Compared with the attack group, the effective rate of the nonattack group was higher (P < 0.05). The effective rates of the early epilepsy group and the late epilepsy group were 91.7% and 85.0%, respectively. Compared with the late epilepsy group, the effective rate of the early epilepsy group was higher (P < 0.05). Compared with the early epilepsy group, the late epilepsy group had longer tonic-clonic seizures, atonic seizures, and absent seizures, and the difference between the groups was statistically significant (P < 0.05).

Conclusion

In aSAH secondary epilepsy disease prediction, based on indepth study of the scalp EEG wave type prediction, they play an important role, including aSAH high-risk secondary epilepsy wave types for V, III, and IV types, as well as early and late epilepsy associated with disease stage. Through the diagnosis method to predict the severity of disease, this builds a good foundation for clinical treatment. It is beneficial to improve the effective rate of treatment.

Electric Fence Artifact on Ambulatory EEG and Review of Common EEG Electrical Artifacts

Ambulatory electroencephalogram (AEEG) can be a cost-effective and valuable alternative to in-patient long-term EEG monitoring. A potential benefit of AEEG is that it allows monitoring in the patient's unique home environment. While this can be more affordable and convenient for the patient, it can also present unique challenges for the reviewer. Unlike long-term monitoring in an epilepsy unit, the AEEG recording occurs in a less controlled environment and most often without immediate EEG technical assistance during the recording. As a result, unique EEG artifacts can occur with AEEG. Their recognition and correct interpretation are crucial for proper EEG analysis. This report presents a case of a patient who underwent a 72-hour AEEG to evaluate symptoms initially concerning for subclinical seizures. During the AEEG recording, the patient had a tactile encounter with an electric fence. This tactile event resulted in a unique, not previously reported, pattern clouding an otherwise normal study. By conducting a brief review of the most common non-physiologic environmental artifacts encountered in modern EEG monitoring, we aim to emphasize the importance of patient education to prevent artifactual pollution. This knowledge can facilitate planning and help avoid environmental influences that may create artifacts when recording in an uncontrolled setting.