Characterisation of ictal and interictal states of epilepsy: A system dynamic approach of principal dynamic modes analysis

Closed-loop wearable ultrasound deep brain stimulation system based on EEG in mice

Objective. Epilepsy is one of the most common severe brain disorders. Ultrasound deep brain stimulation (UDBS) has shown a potential capability to suppress seizures. However, because seizures occur sporadically, it is necessary to develop a closed-loop system to suppress them. Therefore, we developed a closed-loop wearable UDBS system that delivers ultrasound to the hippocampus to suppress epileptic seizures.Approach.Mice were intraperitoneally injected with 10 mg kg^-1kainic acid and divided into sham and UDBS groups. Epileptic seizures were detected by applying both long short-term memory (LSTM) and bidirectional LSTM (BILSTM) networks according to EEG signal characteristics. When epileptic seizures were detected, the closed-loop UDBS system automatically activated a trigger switch to stimulate the hippocampus for 10 min and continuously record EEG signals until 20 min after ultrasonic stimulation. EEG signals were analyzed using the MATLAB software. After EEG recording, we observed the survival rate of the experimental mice for 72 h.Main results.The BiLSTM network was found to have preferable classification performance over the LSTM network. The closed-loop UDBS system with BiLSTM could automatically detect epileptic seizures using EEG signals and effectively reduce epileptic EEG power spectral density and seizure duration by 10.73%, eventually improving the survival rate of early epileptic mice from 67.57% in the sham group to 88.89% in the UDBS group.Significance.The closed-loop UDBS system developed in this study could be an effective clinical tool for the control of epilepsy.

Pannexin-1 Deficiency Decreases Epileptic Activity in Mice

Objective: Pannexin-1 (Panx1) is suspected of having a critical role in modulating neuronal excitability and acute neurological insults. Herein, we assess the changes in behavioral and electrophysiological markers of excitability associated with Panx1 via three distinct models of epilepsy. Methods Control and Panx1 knockout C57Bl/6 mice of both sexes were monitored for their behavioral and electrographic responses to seizure-generating stimuli in three epilepsy models—(1) systemic injection of pentylenetetrazol, (2) acute electrical kindling of the hippocampus and (3) neocortical slice exposure to 4-aminopyridine. Phase-amplitude cross-frequency coupling was used to assess changes in an epileptogenic state resulting from Panx1 deletion. Results: Seizure activity was suppressed in Panx1 knockouts and by application of Panx1 channel blockers, Brilliant Blue-FCF and probenecid, across all epilepsy models. In response to pentylenetetrazol, WT mice spent a greater proportion of time experiencing severe (stage 6) seizures as compared to Panx1-deficient mice. Following electrical stimulation of the hippocampal CA3 region, Panx1 knockouts had significantly shorter evoked afterdischarges and were resistant to kindling. In response to 4-aminopyridine, neocortical field recordings in slices of Panx1 knockout mice showed reduced instances of electrographic seizure-like events. Cross-frequency coupling analysis of these field potentials highlighted a reduced coupling of excitatory delta–gamma and delta-HF rhythms in the Panx1 knockout. Significance: These results suggest that Panx1 plays a pivotal role in maintaining neuronal hyperexcitability in epilepsy models and that genetic or pharmacological targeting of Panx1 has anti-convulsant effects.

A Strategy for Classification of "Vaginal vs. Cesarean Section" Delivery: Bivariate Empirical Mode Decomposition of Cardiotocographic Recordings

We propose objective and robust measures for the purpose of classification of "vaginal vs. cesarean section" delivery by investigating temporal dynamics and complex interactions between fetal heart rate (FHR) and maternal uterine contraction (UC) recordings from cardiotocographic (CTG) traces. Multivariate extension of empirical mode decomposition (EMD) yields intrinsic scales embedded in UC-FHR recordings while also retaining inter-channel (UC-FHR) coupling at multiple scales. The mode alignment property of EMD results in the matched signal decomposition, in terms of frequency content, which paves the way for the selection of robust and objective time-frequency features for the problem at hand. Specifically, instantaneous amplitude and instantaneous frequency of multivariate intrinsic mode functions are utilized to construct a class of features which capture nonlinear and nonstationary interactions from UC-FHR recordings. The proposed features are fed to a variety of modern machine learning classifiers (decision tree, support vector machine, AdaBoost) to delineate vaginal and cesarean dynamics. We evaluate the performance of different classifiers on a real world dataset by investigating the following classifying measures: sensitivity, specificity, area under the ROC curve (AUC) and mean squared error (MSE). It is observed that under the application of all proposed 40 features AdaBoost classifier provides the best accuracy of 91.8% sensitivity, 95.5% specificity, 98% AUC, and 5% MSE. To conclude, the utilization of all proposed time-frequency features as input to machine learning classifiers can benefit clinical obstetric practitioners through a robust and automatic approach for the classification of fetus dynamics.

System dynamics of active and passive postural changes: Insights from principal dynamic modes analysis of baroreflex loop

The baroreflex being a key modulator of cardiovascular control ensures adequate blood pressure regulation under orthostatic stress which otherwise may cause severe hypotension. Contrary to conventional baroreflex sensitivity indices derived across a-priori traditional frequency bands, the present study is aimed at proposing new indices for the assessment of baroreflex drive which follows active (supine to stand-up) and passive (supine to head-up tilt) postural changes. To achieve this, a novel system identification approach of principal dynamic modes (PDM) was utilized to extract data-adaptive frequency components of closed-loop interactions between beat-to-beat interval and systolic blood pressure recorded from 10 healthy humans. We observed that the gain of low-pass global PDM of cardiac arm (:feedback reflex loop, mediated by pressure sensors to adjust heart rate in response to arterial blood pressure), and 0.2 Hz global PDM of mechanical arm (:feed-forward pathways, originating changes in arterial blood pressure in response to heart rate variations) may function as potential markers to distinguish active and passive orthostatic tests in healthy subjects.