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

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

Continuous assessment of epileptic seizures with wrist-worn biosensors

Thesis (Ph. D.)--Harvard-MIT Division of Health Sciences and Technology, 2011.Cataloged from PDF version of thesis.Includes bibliographical references (p. 145-159).Epilepsy is a neurological disorder characterized predominantly by an enduring predisposition to generate epileptic seizures. The apprehension about injury, or even death, resulting from a seizure often overshadows the lives of those unable to achieve complete seizure control. Moreover, the risk of sudden death in people with epilepsy is 24 times higher compared to the general population and the pathophysiology of sudden unexpected death in epilepsy (SUDEP) remains unclear. This thesis describes the development of a wearable electrodermal activity (EDA) and accelerometry (ACM) biosensor, and demonstrates its clinical utility in the assessment of epileptic seizures. The first section presents the development of a wrist-worn sensor that can provide comfortable and continuous measurements of EDA, a sensitive index of sympathetic activity, and ACM over extensive periods of time. The wearable biosensor achieved high correlations with a Food and Drug Administration (FDA) approved system for the measurement of EDA during various classic arousal experiments. This device offers the unprecedented ability to perform comfortable, long-term, and in situ assessment of EDA and ACM. The second section describes the autonomic alterations that accompany epileptic seizures uncovered using the wearable EDA biosensor and time-frequency mapping of heart rate variability. We observed that the post-ictal period was characterized by a surge in sympathetic sudomotor and cardiac activity coinciding with vagal withdrawal and impaired reactivation. The impact of autonomic dysregulation was more pronounced after generalized tonic-clonic seizures compared to complex partial seizures. Importantly, we found that the intensity of both sympathetic activation and parasympathetic suppression increased approximately linearly with duration of post-ictal EEG suppression, a possible marker for the risk of SUDEP. These results highlight a critical window of post-ictal autonomic dysregulation that may be relevant in the pathogenesis of SUDEP and hint at the possibility for assessment of SUDEP risk by autonomic biomarkers. Lastly, this thesis presents a novel algorithm for generalized tonic-clonic seizure detection with the use of EDA and ACM. The algorithm was tested on 4213 hours (176 days) of recordings from 80 patients containing a wide range of ordinary daily activities and detected 15/16 (94%) tonic-clonic seizures with a low rate of false alarms (<; 1 per 24 h). It is anticipated that the proposed wearable biosensor and seizure detection algorithm will provide an ambulatory seizure alarm and improve the quality of life of patients with uncontrolled tonic-clonic seizures.by Ming-Zher Poh.Ph.D

An IoT Platform for Epilepsy Monitoring and Supervising

Epilepsy is a chronic neurological disorder with several different types of seizures, some of them characterized by involuntary recurrent convulsions, which have a great impact on the everyday life of the patients. Several solutions have been proposed in the literature to detect this type of seizures and to monitor the patient; however, these approaches lack in ergonomic issues and in the suitable integration with the health system. This research makes an in-depth analysis of the main factors that an epileptic detection and monitoring tool should accomplish. Furthermore, we introduce the architecture for a specific epilepsy detection and monitoring platform, fulfilling these factors. Special attention has been given to the part of the system the patient should wear, providing details of this part of the platform. Finally, a partial implementation has been deployed and several tests have been proposed and carried out in order to make some design decisions

Big data in epilepsy: Clinical and research considerations. Report from the Epilepsy Big Data Task Force of the International League Against Epilepsy

Epilepsy is a heterogeneous condition with disparate etiologies and phenotypic and genotypic characteristics. Clinical and research aspects are accordingly varied, ranging from epidemiological to molecular, spanning clinical trials and outcomes, gene and drug discovery, imaging, electroencephalography, pathology, epilepsy surgery, digital technologies, and numerous others. Epilepsy data are collected in the terabytes and petabytes, pushing the limits of current capabilities. Modern computing firepower and advances in machine and deep learning, pioneered in other diseases, open up exciting possibilities for epilepsy too. However, without carefully designed approaches to acquiring, standardizing, curating, and making available such data, there is a risk of failure. Thus, careful construction of relevant ontologies, with intimate stakeholder inputs, provides the requisite scaffolding for more ambitious big data undertakings, such as an epilepsy data commons. In this review, we assess the clinical and research epilepsy landscapes in the big data arena, current challenges, and future directions, and make the case for a systematic approach to epilepsy big data

Multimodal nocturnal seizure detection in children with epilepsy:A prospective, multicenter, long-term, in-home trial

Objective: There is a pressing need for reliable automated seizure detection in epilepsy care. Performance evidence on ambulatory non-electroencephalography-based seizure detection devices is low, and evidence on their effect on caregiver's stress, sleep, and quality of life (QoL) is still lacking. We aimed to determine the performance of NightWatch, a wearable nocturnal seizure detection device, in children with epilepsy in the family home setting and to assess its impact on caregiver burden. Methods: We conducted a phase 4, multicenter, prospective, video-controlled, in-home NightWatch implementation study (NCT03909984). We included children aged 4–16 years, with ≥1 weekly nocturnal major motor seizure, living at home. We compared a 2-month baseline period with a 2-month NightWatch intervention. The primary outcome was the detection performance of NightWatch for major motor seizures (focal to bilateral or generalized tonic–clonic [TC] seizures, focal to bilateral or generalized tonic seizures lasting &gt;30 s, hyperkinetic seizures, and a remainder category of focal to bilateral or generalized clonic seizures and "TC-like" seizures). Secondary outcomes included caregivers' stress (Caregiver Strain Index [CSI]), sleep (Pittsburgh Quality of Sleep Index), and QoL (EuroQol five-dimension five-level scale). Results: We included 53 children (55% male, mean age = 9.7 ± 3.6 years, 68% learning disability) and analyzed 2310 nights (28 173 h), including 552 major motor seizures. Nineteen participants did not experience any episode of interest during the trial. The median detection sensitivity per participant was 100% (range = 46%–100%), and the median individual false alarm rate was.04 per hour (range = 0–.53). Caregiver's stress decreased significantly (mean total CSI score = 8.0 vs. 7.1, p =.032), whereas caregiver's sleep and QoL did not change significantly during the trial. Significance: The NightWatch system demonstrated high sensitivity for detecting nocturnal major motor seizures in children in a family home setting and reduced caregiver stress.</p

NOVEL COMPUTATIONAL ELECTROENCEPHALOGRAPHIC (EEG) METHODOLOGIES FOR AUTISM MANAGEMENT AND EPILEPTIC SEIZURE PREDICTION

The doctoral thesis deals with a novel methodology of looking and processing electroencephalographic (EEG) data. The first part deals with real-time brain stimulation in the form of a sonified neurofeedback therapy derived from a clinically comparable portable, 4-channel EEG system. The therapy aims to provide an effective management for symptoms of the Autism Spectrum Disorder (ASD). ASD is characterized with a high level of delta electroencephalographic waveform levels, while alpha and beta prove to be present at lower levels especially in the frontal-temporal regions. The treatment aims at lowering delta waves and promoting alpha and beta waveforms. The second part of the thesis focuses on using EEG data in the prediction of epileptic seizures. With the help of custom built algorithms and neural networks, an effective prediction of an epileptic seizure can be achieved