Early Seizure Detection Research Articles

64-Channel UWB Wireless Neural Vector Analyzer SOC With a Closed-Loop Phase Synchrony-Triggered Neurostimulator

An ultra wideband (UWB) 64-channel responsive neural stimulator system-on-chip (SoC) is presented. It demonstrates the first on-chip neural vector analyzer capable of wirelessly monitoring magnitude, phase and phase synchronization of neural signals. In a closed-loop, abnormal phase synchrony triggers the programmable-waveform biphasic current-mode neural stimulator. To implement these functionalities, the SoC integrates 64 neural recording amplifiers with tunable switched-capacitor (SC) bandpass filters, 64 multiplying 8-bit SAR ADCs, 64 programmable 16-tap FIR filters, a tri-core CORDIC processor, 64 biphasic current stimulation channels, and a 3.1-10.6 GHz UWB wireless transmitter onto a 4 mm × 3 mm 0.13 μm CMOS die. To minimize both the area and power dissipation of the SoC, the SAR ADC is re-used as a multiplier for FIR filtering and as a DAC and duty cycle controller for the biphasic neural stimulator. The SoC has been validated in the early detection and abortion of seizures in freely moving rodents on-line and in early seizure detection in humans off-line.

Early detection of epileptic seizures based on parameter identification of neural mass model

Physiologically based models are attractive for seizure detection, as their parameters can be explicitly related to neurological mechanisms. We propose an early seizure detection algorithm based on parameter identification of a neural mass model. The occurrence of a seizure is detected by analysing the time shift of key model parameters. The algorithm was evaluated against the manual scoring of a human expert on intracranial EEG samples from 16 patients suffering from different types of epilepsy. Results suggest that the algorithm is best suited for patients suffering from temporal lobe epilepsy (sensitivity was 95.0%±10.0% and false positive rate was 0.20±0.22 per hour).

Peri-ictal ECG changes in childhood epilepsy: Implications for detection systems

Early detection of seizures could reduce associated morbidity and mortality and improve the quality of life of patients with epilepsy. In this study, the aim was to investigate whether ictal tachycardia is present in focal and generalized epileptic seizures in children. We sought to predict in which type of seizures tachycardia can be identified before actual seizure onset.Electrocardiogram segments in 80 seizures were analyzed in time and frequency domains before and after the onset of epileptic seizures on EEG. These ECG parameters were analyzed to find the most informative ones that can be used for seizure detection. The algorithm of Leutmezer et al. [17] was used to find the temporal relationship between the change in heart rate and seizure onset.In the time domain, the mean RR shows a significant difference before compared to after onset of the seizure in focal seizures. This can be observed in temporal lobe seizures as well as frontal lobe seizures. Calculation of mean RR interval has a high specificity for detection of ictal heart rate changes.Preictal heart rate changes are observed in 70% of the partial seizures.Ictal heart rate changes are present only in partial seizures in this childhood epilepsy study. The changes can be observed in temporal lobe seizures as well as in frontal lobe seizures. Heart rate changes precede seizure onset in 70% of the focal seizures, making seizure detection and closed-loop systems a possible therapeutic alternative in the population of children with refractory epilepsy.

The incidence of seizures in patients undergoing therapeutic hypothermia after resuscitation from cardiac arrest

Non-convulsive seizures/status epilepticus occur in approximately 20% of comatose, non-cardiac arrest intensive care unit (ICU) patients, and are associated with increased mortality. The prevalence and clinical significance of seizures in comatose survivors of cardiac arrest undergoing therapeutic hypothermia is not well described. At this urban level I trauma center, every patient undergoing therapeutic hypothermia is monitored with continuous video encephalography (cvEEG). We abstracted medical records for all cardiac arrest patients treated with therapeutic hypothermia during 2010. Clinical data were extracted in duplicate. cvEEGs were independently reviewed for seizures by two board-certified epileptologists. There were 33 patients treated with therapeutic hypothermia after cardiac arrest in 2010 who met inclusion criteria for this study. Median age was 58 (range 28-86 years), 63% were white, 55% were male, and 9% had a history of seizures or epilepsy. During cooling, seizures occurred in 5/33 patients (15%, 95%CI 6%-33%). 11/33 patients (33%, 95% CI 19%-52%) had seizures at some time during hospitalization. 13/33 (39%) survived to discharge and of these, 7/13 (54%) survived to 30 days. 9/11 patients with seizures died during hospitalization, compared with 11/22 patients without seizures (82% vs. 50%; difference 32%, CI 951%-63%). No patient with seizures was alive at 30 days. Seizures are common in comatose patients treated with therapeutic hypothermia after cardiac arrest. All patients with seizures were deceased within 30 days of discharge. Routine use of EEG monitoring could assist in early detection of seizures in this patient population, providing an opportunity for intervention to potentially improve outcomes.

Optimal Control-Based Bayesian Detection of Clinical and Behavioral State Transitions

Accurately detecting hidden clinical or behavioral states from sequential measurements is an emerging topic in neuroscience and medicine, which may dramatically impact neural prosthetics, brain-computer interface and drug delivery. For example, early detection of an epileptic seizure from sequential electroencephalographic (EEG) measurements would allow timely administration of anticonvulsant drugs or neurostimulation, thus reducing physical impairment and risks of overtreatment. We develop a Bayesian paradigm for state transition detection that combines optimal control and Markov processes. We define a hidden Markov model of the state evolution and develop a detection policy that minimizes a loss function of both probability of false positives and accuracy (i.e., lag between estimated and actual transition time). Our strategy automatically adapts to each newly acquired measurement based on the state evolution model and the relative loss for false positives and accuracy, thus resulting in a time varying threshold policy. The paradigm was used in two applications: 1) detection of movement onset (behavioral state) from subthalamic single unit recordings in Parkinson's disease patients performing a motor task; 2) early detection of an approaching seizure (clinical state) from multichannel intracranial EEG recordings in rodents treated with pentylenetetrazol chemoconvulsant. Our paradigm performs significantly better than chance and improves over widely used detection algorithms.

Implantable Closed-Loop Epilepsy Prosthesis

In this article, we present an implantable closed-loop epilepsy prosthesis, which is dedicated to automatically detect seizure onsets based on intracerebral electroencephalographic (icEEG) recordings from intracranial electrode contacts and provide an electrical stimulation feedback to the same contacts in order to disrupt these seizures. A novel epileptic seizure detector and a dedicated electrical stimulator were assembled together with common recording electrodes to complete the proposed prosthesis. The seizure detector was implemented in CMOS 0.18- μ m by incorporating a new seizure detection algorithm that models time-amplitude and -frequency relationship in icEEG. The detector was validated offline on ten patients with refractory epilepsy and showed excellent performance for early detection of seizures. The electrical stimulator, used for suppressing the developing seizure, is composed of two biphasic channels and was assembled with embedded FPGA in a miniature PCB. The stimulator efficiency was evaluated on cadaveric animal brain tissue in an in vitro morphologic electrical model. Spatial characteristics of the voltage distribution in cortex were assessed in an attempt to identify optimal stimulation parameters required to affect the suspected epileptic focus. The experimental results suggest that lower frequency stimulation parameters cause significant amount of shunting of current through the cerebrospinal fluid; however higher frequency stimulation parameters produce effective spatial voltage distribution with lower stimulation charge.

Phase-Synchronization Early Epileptic Seizure Detector VLSI Architecture

A low-power VLSI processor architecture that computes in real time the magnitude and phase-synchronization of two input neural signals is presented. The processor is a part of an envisioned closed-loop implantable microsystem for adaptive neural stimulation. The architecture uses three CORDIC processing cores that require shift-and-add operations but no multiplication. The 10-bit processor synthesized and prototyped in a standard 1.2 V 0.13 μm CMOS technology utilizes 41,000 logic gates. It dissipates 3.6 μW per input pair, and provides 1.7 kS/s per-channel throughput when clocked at 2.5 MHz. The power scales linearly with the number of input channels or the sampling rate. The efficacy of the processor in early epileptic seizure detection is validated on human intracranial EEG data.

Characterization of early partial seizure onset: Frequency, complexity and entropy

ObjectiveA clear classification of partial seizures onset features is not yet established. Complexity and entropy have been very widely used to describe dynamical systems, but a systematic evaluation of these measures to characterize partial seizures has never been performed. MethodsEighteen different measures including power in frequency bands up to 300Hz, Gabor atom density (GAD), Higuchi fractal dimension (HFD), Lempel–Ziv complexity, Shannon entropy, sample entropy, and permutation entropy, were selected to test sensitivity to partial seizure onset. Intracranial recordings from 45 patients with mesial temporal, neocortical temporal and neocortical extratemporal seizure foci were included (331 partial seizures). ResultsGAD, Lempel–Ziv complexity, HFD, high frequency activity, and sample entropy were the most reliable measures to assess early seizure onset. ConclusionsIncreases in complexity and occurrence of high-frequency components appear to be commonly associated with early stages of partial seizure evolution from all regions. The type of measure (frequency-based, complexity or entropy) does not predict the efficiency of the method to detect seizure onset. SignificanceDifferences between measures such as GAD and HFD highlight the multimodal nature of partial seizure onsets. Improved methods for early seizure detection may be achieved from a better understanding of these underlying dynamics.

Seizure detection using digital trend analysis: Factors affecting utility

EEG monitoring is important for the early detection of seizures during the course of critical illness. However, the logistics of real time EEG interpretation is challenging for the neurophysiology and critical care medicine teams. This study evaluated factors affecting the utility of digital trend analysis (DTA) for rapid seizure identification in children. digital EEG files of seizures in critically ill children were retrieved for DTA. The envelop trend (ET) and compressed spectral array (CSA) were applied to the raw EEG data and presented to an experienced and inexperienced user for interpretation who were blinded to conventional EEG findings. The EEG findings with and without presence of seizures and features of seizures were analyzed. we found that a number of factors affected accurate seizure detection including factors related to interpreter's experiences, display size and type of DTA methods used for analysis in addition to baseline EEG findings. ET was more dependent on user experience, furthermore, display size and multimodal DTA application (CSA and ET combined) increased the sensitivity of seizure detection for the experienced user compared to inexperience users. The artifacts were reported as seizures regardless of experience without presence of conventional EEG recording. The maximum spike amplitude, seizure duration, and seizure frequency were other important determinants for accuracy. Electrographic seizures with shorter duration were better detected by ET, and the maximum spike amplitude was important for both the ET and CSA. Repetitive seizures are readily detected by both digital trending methods. Artifacts may be reported as seizures regardless of experience if conventional EEG recording is not available for the interpretation. DTA applied to the raw EEG data does produce a graphic display that facilitates identification of seizures. The actual characteristics of the electrographic seizure may predict which DTA method is better and the overall accuracy of seizure detection may increase when multimodal trending is used simultaneously. Application of DTA alone with display of conventional EEG is beneficial for rapid interpretation of EEG findings regardless of experience.

Early seizure detection in rats based on vagus nerve activity

Continuous, scheduled vagus nerve stimulation (VNS) is used for the treatment of refractory epilepsy. On-demand VNS, started prior to or at the onset of a seizure may improve the effect of the treatment, however, this requires seizures to be predicted or detected early. This study investigates the possibility of early seizure detection based on the cervical vagus electroneurogram (VENG). Fourteen anesthetized rats received an intravenous infusion (IV) of either saline (control, n = 6) or pentylenetetrazol (PTZ) diluted in saline (PTZ-treated, n = 8). A cardiac-related VENG profile (CrVENG) was derived by using R-peak triggered averaging of the VENG energy. Following, changes in this profile were evaluated as a seizure predictor. Using left nerve VENG, seizures were detected in all PTZ-treated rats 103 ± 51 s (mean ± SD) before they developed tonic seizures. Control rats did not develop seizures and our method did also not detected seizures in these rats. Seizures can be early detecting based on left nerve VENG in anesthetized rats. Preictal CrVENG changes may reflect central-mediated changes and/or changes in the relation between the respiration and the cardiac cycle. Further research is needed to evaluate the method in awake and freely moving animals and eventually in humans.

Neurophysiological testing in neurocritical care

To summarize a consensus of European authorities about the applications of clinical neurophysiology in the ICU and, particularly, for a clinically useful management of individual patients. Clinical neurophysiology is useful for diagnosis (epilepsy, brain death, and neuromuscular disorders), prognosis (anoxic ischemic encephalopathy, head trauma, and neurological disturbances of metabolic and toxic origin), and follow-up. The prognostic significance of each test varies as a function of coma etiology. A distinction should be made between tests whose abnormalities are indicative of a poor prognosis (bilateral absence of N20 in anoxic coma, abnormalities suggesting pontine involvement in head trauma) and those whose relative normalcy constitutes an argument for a good prognosis (integrity of brainstem conductions in head trauma, presence of cognitive evoked potentials - mismatch negativity, P300 - irrespective of coma etiology).The highlights of the recent literature mainly concern continuous neuromonitoring for early detection of nonconvulsive seizures, both in adult and neonatal ICU, brain entry into the ischemic penumbra zone, and neuronal functional consequences of intracranial hypertension. The domain of clinical neurophysiology is similar to that of clinical examination and complementary to that of imaging techniques. It substantially improves the individual management of ICU patients.

Granger causality relationships between local field potentials in an animal model of temporal lobe epilepsy

An understanding of the in vivo spatial emergence of abnormal brain activity during spontaneous seizure onset is critical to future early seizure detection and closed-loop seizure prevention therapies. In this study, we use Granger causality (GC) to determine the strength and direction of relationships between local field potentials (LFPs) recorded from bilateral microelectrode arrays in an intermittent spontaneous seizure model of chronic temporal lobe epilepsy before, during, and after Racine grade partial onset generalized seizures. Our results indicate distinct patterns of directional GC relationships within the hippocampus, specifically from the CA1 subfield to the dentate gyrus, prior to and during seizure onset. Our results suggest sequential and hierarchical temporal relationships between the CA1 and dentate gyrus within and across hippocampal hemispheres during seizure. Additionally, our analysis suggests a reversal in the direction of GC relationships during seizure, from an abnormal pattern to more anatomically expected pattern. This reversal correlates well with the observed behavioral transition from tonic to clonic seizure in time-locked video. These findings highlight the utility of GC to reveal dynamic directional temporal relationships between multichannel LFP recordings from multiple brain regions during unprovoked spontaneous seizures.

Non-parametric early seizure detection in an animal model of temporal lobe epilepsy

The performance of five non-parametric, univariate seizure detection schemes (embedding delay, Hurst scale, wavelet scale, nonlinear autocorrelation and variance energy) were evaluated as a function of the sampling rate of EEG recordings, the electrode types used for EEG acquisition, and the spatial location of the EEG electrodes in order to determine the applicability of the measures in real-time closed-loop seizure intervention. The criteria chosen for evaluating the performance were high statistical robustness (as determined through the sensitivity and the specificity of a given measure in detecting a seizure) and the lag in seizure detection with respect to the seizure onset time (as determined by visual inspection of the EEG signal by a trained epileptologist). An optimality index was designed to evaluate the overall performance of each measure. For the EEG data recorded with microwire electrode array at a sampling rate of 12 kHz, the wavelet scale measure exhibited better overall performance in terms of its ability to detect a seizure with high optimality index value and high statistics in terms of sensitivity and specificity.

SAW Microsensor Brain Implant for Prediction and Monitoring of Seizures

An implantable surface acoustic wave (SAW) microsensor has been developed for early detection and monitoring of seizures based on local temperature changes in the brain's epileptogenic zones that occur prior to and during an epileptic event. Three SAW sensors were designed and fabricated: a 172 MHz filter, a 434 MHz filter, and a 434 MHz delay line. Their temperature sensitivities were tested by measuring the phase change between the input and output waveforms as a function of temperature. We achieved a phase sensitivity of 144 phase degrees per degC and a minimum detectable temperature of 5 mK for the 434-MHz, 10.2-mus delay line. Based on the sensitivity tests, a prototype 434 MHz SAW sensor was fabricated to a size of 11times1times1.1 mm, which is commensurate with existing brain implantable probes. Because of possible damping of the surface waves by the surrounding tissue or fluid, a glass housing with dry air was built on the top of the SAW substrate. Test and reference sensors were used in the prototype system to minimize the effect of source instabilities and to amplify the temperature effect. The phase change between the output waveforms of the sensors was measured with phase detector electronics after they were converted to lower (10.7 MHz) frequencies by standard mixers. The complete prototype sensor was tested in a saline water bath and found to detect as low as 3 mK changes of temperature caused by the addition of hot water.

Beware: The Misuse of Technology and the Law of Unintended Consequences

Electronic monitoring of the fetal heart rate during labor (EFM), originally designed to assess fetal stress and allow the early detection of the compromised fetus, has instead led to increasing maternal morbidity without decreasing fetal morbidity. The unintended consequences of this technologic advance have led to the creation of a pseudodisease and unwarranted intervention in response to its detection. Is it ethical to introduce a new technology without adequate assessment of its possible consequences? Are we about to repeat this (error resulting from the introduction of EFM?) There is increasing interest in monitoring the function of the newborn brain, to enable the early detection of subclinical seizures. The monitor may also be used for assessing brain function in older children and adults who are comatose or paralyzed and cannot appropriately respond to stimuli. Use of this amplitude-integrated electroencephalography (aEEG) in the newborn for detection of seizures and other brain abnormalities is not dissimilar to the use of electronic fetal heart rate monitoring. Whether seizures or subclinical seizures themselves cause harm to the developing nervous system is unclear. The effectiveness of medications for treatment of seizures in the newborn has not been established. Therefore, the consequences of introducing automated EEG for the detection of subclinical neonatal seizures are likely to be similar to the results of the introduction of EFM: creation of another pseudodisease, followed by unwarranted intervention, and increased legal liability. What are the ethics of continued approval and introduction of unevaluated technology? What is the wisdom of its use? Beware of the unintended consequences.

Ictogenesis

Ictogenesis

Demographic and Seizure Variables, But Not Hypnotizability or Dissociation, Differentiated Psychogenic from Organic Seizures

Early detection and differential diagnosis of psychogenic non-epileptic seizures (PNES) and epileptic seizures (ES) is a major clinical issue in comprehensive epilepsy centers. Using blind conditions with patients with PNES (N= 10) and ES (N= 31) before diagnosis, we tested the hypotheses that individuals with PNES would exhibit significantly greater dissociativity, hypnotizability, absorption, and history of early abuse than ES patients. Although PNES patients tended to show greater dissociative phenomena, only the last of our hypotheses was fully supported. Although absorption did not discriminate between the two diagnostic groups, it was significantly higher among those reporting early abuse. A logistic regression analysis using scores on dissociation, hypnotizability and absorption showed them to be poor predictors of diagnosis; however, other analyses revealed that female gender, reports of multiple trauma incidents lasting months or years, initial seizure onset in late teens or twenties, and daily seizure attacks significantly differentiated PNES from ES patients. Thus, demographic and seizure variables proved to be much better predictors of diagnosis than psychological dimensions often associated with PNES.

The role of patient companions in long-term video-EEG monitoring

In developing countries it is difficult to have full-time dedicated nurses in Epilepsy Monitoring Units (EMU). Our one-bed EMU is within the Neurology Service and is adequately staffed during daytime working hours only. So we created a new model where the patient’s companion was asked to press a nurse call button, allowing the examination of the patient by the nurse. In this study we aimed to understand how patient companions behaved and which factors influenced their behaviour. Patients were allowed to choose a single companion who were educated by the specialist monitoring nurse according to a protocol. Only the first recorded seizures of the patients were included in the study. The seizures were reviewed from the video-cassette recordings and the behaviour of the companions was scored according to the results of the following three questions: (1) when was the seizure noticed?; (2) was the nurse call button pushed?; and (3) did the companion prevent the recording of the seizure by the camera? The companions were grouped according to the following criteria; age, sex, level of education, type of relationship. The scores were compared for each criterion separately. The behaviours of the 50 companions (34F, 16M; age: 25–72) were studied. When statistically compared for age, sex and level of education, there were no significant differences between different groups. However, the mean score of the 47 companions who were immediate family members (3.72) was greater than those three who were not (1.66) In one-bed EMUs, patient companions who are family members can help nurses in the early detection of seizures.