Detection system of motor epileptic seizures through motion analysis with 3D accelerometers

Characteristics of motion signal profiles of tonic-clonic, tonic, hyperkinetic, and motor seizures extracted from nocturnal video recordings

OBJECTIVE

In this study, characteristics of signal profiles formed by motion, oscillation, and sound signals were analyzed to evaluate generalizability and variability in a single patient setting (intra-patient variability) and between patients (inter-patient variability). As a secondary objective, the effect of brivaracetam intervention on signal profiles was explored.

METHODS

Patient data included 13 hyperkinetic seizures, 65 tonic seizures, 13 tonic-clonic seizures, and 138 motor seizures from 11 patients. All patients underwent an 8-week monitoring, and after a 3-week baseline, brivaracetam was initiated. Motion, oscillation, and sound features extracted from the video were used to form signal profiles. Variance of signals was calculated, and combined median and quartile visualizations were used to visualize the results. Similarly, the effect of intervention was visualized.

RESULTS

Hyperkinetic motion signals showed a rapid increase in motion and sound signals without oscillations and achieved low intra-patient variance. Tonic component created a recognizable peak in motion signal typical for tonic and tonic-clonic seizures. For tonic seizures, inter-patient variance was low. Motor signal profiles were varying, and they did not form a generalizable signal profile. Visually recognizable changes were observed in the signal profiles of two patients.

SIGNIFICANCE

Video-based motion signal analysis enabled the extraction of motion features characteristic for different motor seizure types which might be useful in further development of this system. Tonic component formed a recognizable seizure signature in the motion signal. Hyperkinetic and motor seizures may have not only significantly different motion signal amplitude but also overlapping signal profile characteristics which might hamper their automatic differentiation. Motion signals might be useful in the assessment of movement intensity changes to evaluate the treatment effect. Further research is needed to test generalizability and to increase reliability of the results.

Detection of Generalized Tonic–Clonic Seizures in Dogs With a Seizure Detection System Established Using Acceleration Data and the Mahalanobis Distance: A Preliminary Study

Caregivers of dogs with epilepsy experience severe stress due to unpredictable seizures. Hence, they feel the need for a better management strategy. A seizure detection system (SDS), which can identify seizures and provide notifications to caregivers immediately, is required to address this issue. The current study aimed to establish a wearable automatic SDS using acceleration data and the Mahalanobis distance and to preliminarily investigate its feasibility among dogs. A generalized tonic–clonic seizure (GTCS) was targeted because it is the most common type of seizure and can have serious consequences (i.e., status epilepticus). This study comprised three phases. First, the reference datasets of epileptic and non-epileptic activities were established using acceleration data of GTCSs in 3 dogs and daily activities in 27 dogs. Second, the GTCS-detecting algorithm was created using the reference datasets and was validated using other acceleration data of GTCSs in 4 epileptic dogs and daily activities in 27 dogs. Third, a feasibility test of the SDS prototype was performed in three dogs with epilepsy. The algorithm was effective in identifying all acceleration data of GTCSs as seizures and all acceleration data of daily activities as non-seizure activities. Dogs with epilepsy were monitored with the prototype for 48–72 h, and three GTCSs were identified. The prototype detected all GTCSs accurately. A false positive finding was not obtained unless the accelerometer was displaced. Hence, a method that can detect epileptic seizures, particularly GTCSs, was established. Nevertheless, further large-scale studies must be conducted before the method can be commercialized.

Neural stimulation systems for the control of refractory epilepsy: a review

Epilepsy affects nearly 1% of the world's population. A third of epilepsy patients suffer from a kind of epilepsy that cannot be controlled by current medications. For those where surgery is not an option, neurostimulation may be the only alternative to bring relief, improve quality of life, and avoid secondary injury to these patients. Until recently, open loop neurostimulation was the only alternative for these patients. However, for those whose epilepsy is applicable, the medical approval of the responsive neural stimulation and the closed loop vagal nerve stimulation systems have been a step forward in the battle against uncontrolled epilepsy. Nonetheless, improvements can be made to the existing systems and alternative systems can be developed to further improve the quality of life of sufferers of the debilitating condition. In this paper, we first present a brief overview of epilepsy as a disease. Next, we look at the current state of biomarker research in respect to sensing and predicting epileptic seizures. Then, we present the current state of open loop neural stimulation systems. We follow this by investigating the currently approved, and some of the recent experimental, closed loop systems documented in the literature. Finally, we provide discussions on the current state of neural stimulation systems for controlling epilepsy, and directions for future studies.

Devices for Ambulatory Monitoring of Sleep-Associated Disorders in Children with Neurological Diseases

Good sleep quality is essential for a child's wellbeing. Early sleep problems have been linked to the later development of emotional and behavioral disorders and can negatively impact the quality of life of the child and his or her family. Sleep-associated conditions are frequent in the pediatric population, and even more so in children with neurological problems. Monitoring devices can help to better characterize sleep efficiency and sleep quality. They can also be helpful to better characterize paroxysmal nocturnal events and differentiate between nocturnal seizures, parasomnias, and obstructive sleep apnea, each of which has a different management. Overnight ambulatory detection devices allow for a tolerable, low cost, objective assessment of sleep quality in the patient's natural environment. They can also be used as a notification system to allow for rapid recognition and prompt intervention of events like seizures. Optimal monitoring devices will be patient- and diagnosis-specific, but may include a combination of modalities such as ambulatory electroencephalograms, actigraphy, and pulse oximetry. We will summarize the current literature on ambulatory sleep devices for detecting sleep disorders in children with neurological diseases.

Spectral Analysis of Acceleration Data for Detection of Generalized Tonic-Clonic Seizures

Generalized tonic-clonic seizures (GTCSs) can be underestimated and can also increase mortality rates. The monitoring devices used to detect GTCS events in daily life are very helpful for early intervention and precise estimation of seizure events. Several studies have introduced methods for GTCS detection using an accelerometer (ACM), electromyography, or electroencephalography. However, these studies need to be improved with respect to accuracy and user convenience. This study proposes the use of an ACM banded to the wrist and spectral analysis of ACM data to detect GTCS in daily life. The spectral weight function dependent on GTCS was used to compute a GTCS-correlated score that can effectively discriminate between GTCS and normal movement. Compared to the performance of the previous temporal method, which used a standard deviation method, the spectral analysis method resulted in better sensitivity and fewer false positive alerts. Finally, the spectral analysis method can be implemented in a GTCS monitoring device using an ACM and can provide early alerts to caregivers to prevent risks associated with GTCS.

Ethernet Motion-Sensor Based Alarm System for Epilepsy Monitoring

In several biomedical domains, it would be interesting to monitor subjects over night time using wearable motion sensors and trigger an alarm if a specific movement has been detected by processing the accelerometer readings. In this paper, the authors describe an innovative architecture for such an alarm system in the context of epilepsy monitoring. The main ingredients of the proposed system are wireless motion sensors, a radio-frequency transceiver linked to an Ethernet gateway and an acquisition server that incorporates real-time detection method. This motion analysis system is further integrated in the dataflow of an existing medicalized alarm system and an event is sent to healthcare professionals every time a seizure is detected by the expert system. The EPIMOUV system has been evaluated, during a 6-month period, in a specialized institution with epilepsy pharmaco-resistant residents.

A Bayesian approach for epileptic seizures detection with 3D accelerometers sensors

In this paper, an algorithm able to detect epilepsy seizure based on 3D accelerometers and with patient adaptation is presented. This algorithm is based on a Bayesian approach using hidden Markov models for statistical modelling of moves signals. A particular focus is set on the learning procedure and in particular on its initialisation to ensure a good learning and to avoid numerical instability. Numerical simulations show that, without inhibition of the detection algorithm when the person is standing up, the algorithm is able to detect close to 90% of seizures when false alarms are 25% of alarms.