Seizure Detection, Seizure Prediction, and Closed-Loop Warning Systems in Epilepsy

Nearly one-third of patients with epilepsy continue to have seizures despite optimal medication management. Systems employed to detect seizures may have the potential to improve outcomes in these patients by allowing more tailored therapies and might, additionally, have a role in accident and SUDEP prevention. Automated seizure detection and prediction require algorithms which employ feature computation and subsequent classification. Over the last few decades, methods have been developed to detect seizures utilizing scalp and intracranial EEG, electrocardiography, accelerometry and motion sensors, electrodermal activity, and audio/video captures. To date, it is unclear which combination of detection technologies yields the best results, and approaches may ultimately need to be individualized. This review presents an overview of seizure detection and related prediction methods and discusses their potential uses in closed-loop warning systems in epilepsy

Evaluation of Wearable Electronics for Epilepsy: A Systematic Review

Epilepsy is a neurological disorder that affects 50 million people worldwide. It is characterised by seizures that can vary in presentation, from short absences to protracted convulsions. Wearable electronic devices that detect seizures have the potential to hail timely assistance for individuals, inform their treatment, and assist care and self-management. This systematic review encompasses the literature relevant to the evaluation of wearable electronics for epilepsy. Devices and performance metrics are identified, and the evaluations, both quantitative and qualitative, are presented. Twelve primary studies comprising quantitative evaluations from 510 patients and participants were collated according to preferred reporting items for systematic reviews and meta-analyses (PRISMA) guidelines. Two studies (with 104 patients/participants) comprised both qualitative and quantitative evaluation components. Despite many works in the literature proposing and evaluating novel and incremental approaches to seizure detection, there is a lack of studies evaluating the devices available to consumers and researchers, and there is much scope for more complete evaluation data in quantitative studies. There is also scope for further qualitative evaluations amongst individuals, carers, and healthcare professionals regarding their use, experiences, and opinions of these devices

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

Optimal Inertial Sensor Placement and Motion Detection for Epileptic Seizure Patient Monitoring

Use of inertial sensory systems to monitor and detect seizure episodes in patients suffering from epilepsy is investigated via numerical simulations and experiments. Numerical simulations employ a mathematical model that is able to predict human body dynamic responses during a typical epileptic seizure. An optimized inertial sensor placement procedure is developed to address achievement of highest possible sensing resolution in determining angular accelerations with minimal errors. In addition, a joint torque estimation procedure is formulated to assist in the future development of a possible detection scheme. Experimental motion data obtained from an epileptic seizure patient as well as a healthy subject via a cluster of inertial measurement sensors formed a basis for proposing a suitable detection scheme based on non-linear response analysis. In particular, preliminary experimental data analysis has shown that the proposed modified Poincaré Map based scheme can become an effective tool in detecting of seizure via inertial measurements

Multicenter clinical assessment of improved wearable multimodal convulsive seizure detectors

Objective New devices are needed for monitoring seizures, especially those associated with sudden unexpected death in epilepsy (SUDEP). They must be unobtrusive and automated, and provide false alarm rates (FARs) bearable in everyday life. This study quantifies the performance of new multimodal wrist-worn convulsive seizure detectors. Methods Hand-annotated video-electroencephalographic seizure events were collected from 69 patients at six clinical sites. Three different wristbands were used to record electrodermal activity (EDA) and accelerometer (ACM) signals, obtaining 5,928 h of data, including 55 convulsive epileptic seizures (six focal tonic–clonic seizures and 49 focal to bilateral tonic–clonic seizures) from 22 patients. Recordings were analyzed offline to train and test two new machine learning classifiers and a published classifier based on EDA and ACM. Moreover, wristband data were analyzed to estimate seizure-motion duration and autonomic responses. Results The two novel classifiers consistently outperformed the previous detector. The most efficient (Classifier III) yielded sensitivity of 94.55%, and an FAR of 0.2 events/day. No nocturnal seizures were missed. Most patients had <1 false alarm every 4 days, with an FAR below their seizure frequency. When increasing the sensitivity to 100% (no missed seizures), the FAR is up to 13 times lower than with the previous detector. Furthermore, all detections occurred before the seizure ended, providing reasonable latency (median = 29.3 s, range = 14.8–151 s). Automatically estimated seizure durations were correlated with true durations, enabling reliable annotations. Finally, EDA measurements confirmed the presence of postictal autonomic dysfunction, exhibiting a significant rise in 73% of the convulsive seizures. Significance The proposed multimodal wrist-worn convulsive seizure detectors provide seizure counts that are more accurate than previous automated detectors and typical patient self-reports, while maintaining a tolerable FAR for ambulatory monitoring. Furthermore, the multimodal system provides an objective description of motor behavior and autonomic dysfunction, aimed at enriching seizure characterization, with potential utility for SUDEP warning

Self-Reporting Technologies for Supporting Epilepsy Treatment

Epilepsy diagnosis and treatment relies heavily on patient self-reporting for informing clinical decision-making. These self-reports are traditionally collected from handwritten patient journals and tend to be either incomplete or inaccurate. Recent mobile and wearable health tracking developments stand to dramatically impact clinical practice through supporting patient and caregiver data collection activities. However, the specific types and characteristics of the data that clinicians need for patient care are not well known. In this study, we conducted interviews, a literature review, an expert panel, and online surveys to assess the availability and quality of patient-reported data that is useful but reported as being unavailable, difficult for patients to collect, or unreliable during epilepsy diagnosis and treatment, respectively. The results highlight important yet underexplored data collection and design opportunities for supporting the diagnosis, treatment, and self-management of epilepsy and expose notable gaps between clinical data needs and current patient practices

Accelerometry based detection of epileptic seizures

Epilepsy is one of the most common neurological disorders. Epileptic seizures are the manifestation of abnormal hypersynchronous discharges of cortical neurons that impair brain function. Most of the people affected can be treated successfully with drug therapy or neurosurgical procedures. But there is still a large group of epilepsy patients that continues to have frequent seizures. For these patients automated detection of epileptic seizures can be of great clinical importance. Seizure detection can influence daily care or can be used to evaluate treatment effect. Furthermore automated detection can be used to trigger an alarm system during seizures that might be harmful to the patient. This thesis focusses on accelerometry (ACM) based seizure detection. A detailed overview is provided, on the perspectives for long-term epilepsy monitoring and automated seizure detection. The value of accelerometry for seizure detection is shown by means of a clinical evaluation and the first steps are made towards automatic detection of epileptic seizures based on ACM. With accelerometers movements are recorded. A large group of epileptic seizures manifest in specific movement patterns, so called motor seizures. Chapter 2 of this thesis presents an overview of the published literature on available methods for epileptic seizure detection in a long-term monitoring context. Based on this overview recommendations are formulated that should be used in seizure detection research and development. It is shown that for seizure detection in home environments, other sensor modalities besides EEG become more important. The use of alternative sensor modalities (such as ACM) is relatively new and so is the algorithm development for seizure detection based on these measures. It was also found that for both the adaptation of existing techniques and the development of new algorithms, clinical information should be taken more into account. The value of ACM for seizure detection is shown by means of a clinical evaluation in chapter 3. Here 3-D ACM- and EEG/video-recordings of 18 patients with severe epilepsy are visually analyzed. A striking outcome presented in this chapter is the large number of visually detected seizures versus the number of seizures that was expected on forehand and the number of seizures that was observed by the nurses. These results underscore the need for an automatic seizure detection device even more, since in the current situation many seizures are missed and therefore it is possible that patients do not get the right (medical) treatment. It was also observed that 95% of the ACM-patterns during motor seizures are sequences of three elementary patterns: myoclonic, tonic and clonic patterns. These characteristic patterns are a starting point for the development of methods for automated seizure detection based on ACM. It was decided to use a modular approach for the detection methodology and develop algorithms separately for motor activity in general, myoclonic seizures and tonic seizures. Furthermore, clinical information is incorporated in the detection methodology. Therefore in this thesis features were used that are either based on the shape of the patterns of interest as described in clinical practice (chapter 4 and 7), or the features were based on a physiological model with parameters that are related to seizure duration and intensity (chapter 5 and 6). In chapter 4 an algorithm is developed to distinguish periods with and without movement from ACM-data. Hence, when there is no movement there is no motor seizure. The amount of data that needs further analysis for seizure detection is thus reduced. From 15 ACM-signals (measured on five positions on the body), two features are computed, the variance and the jerk. In the resulting 2-D feature space a linear threshold function is used for classification. For training and testing the algorithm ACM data along with video data are used from nocturnal recordings in mentally retarded patients with severe epilepsy. Using this algorithm the amount of data that needs further analysis is reduced considerably. The results also indicate that the algorithm is robust for fluctuations across patients and thus there is no need for training the algorithm for each new patient. For the remaining data it needs to be established whether the detected movement is seizure related or not. To this purpose a model is developed for the accelerometer pattern measured on the arm during a myoclonic seizure (chapter 5). The model consists of a mechanical and an electrophysiological part. This model is used as a matched wavelet filter to detect myoclonic seizures. In chapter 6 the model based wavelet is compared to three other time frequency measures: the short time Fourier transform, the Wigner distribution and the continuous wavelet transform using a Daubechies wavelet. All four time-frequency methods are evaluated in a linear classification setup. Data from mentally retarded patients with severe epilepsy are used for training and evaluation. The results show that both wavelets are useful for detection of myoclonic seizures. On top of that, our model based wavelet has the advantage that it consists of parameters that are related to seizure duration and intensity that are physiological meaningful. Besides myoclonic seizures, the model is also useful for the detection of clonic seizures; physiologically these are repetitive myoclonic seizures. Finally for the detection of tonic seizures, in chapter 7 a set of features is studied that incorporate the mean characteristics of ACM-patterns associated with tonic seizures. Linear discriminant analysis is used for classification in the multi-dimensional feature space. For training and testing the algorithm, again data are used from recordings in mentally retarded patients with severe epilepsy. The results show that our approach is useful for the automated detection of tonic seizures based on 3-D ACM and that it is a promising contribution in a complete multi-sensor seizure detection setup