Technologies for Wearable Seizure Detection: A Systematic Review

Knowing when a seizure occurred is helpful because this information can be used to evaluate the effectiveness of seizure interventions and possibly alert caregivers to emergency situations. The current practice for recording seizures outside of a hospital and without sensors is through keeping a self-reported seizure diary. This practice may be unreliable if the diary is not updated or the person having the seizure does not realize it is happening. Wearable seizure detectors aim to solve this problem by reliably recording when a seizure happened and either sending out an alert or storing the data for later analysis. In this systematic review of the literature, 1,018 articles were evaluated to assess the current status of wearable seizure detection technology. A look into the challenges to developing such a device and how others have overcome some of these challenges is also discussed

Multiple Patients Behavior Detection in Real-time using mmWave Radar and Deep CNNs

To address potential gaps noted in patient monitoring in the hospital, a novel patient behavior detection system using mmWave radar and deep convolution neural network (CNN), which supports the simultaneous recognition of multiple patients' behaviors in real-time, is proposed. In this study, we use an mmWave radar to track multiple patients and detect the scattering point cloud of each one. For each patient, the Doppler pattern of the point cloud over a time period is collected as the behavior signature. A three-layer CNN model is created to classify the behavior for each patient. The tracking and point clouds detection algorithm was also implemented on an mmWave radar hardware platform with an embedded graphics processing unit (GPU) board to collect Doppler pattern and run the CNN model. A training dataset of six types of behavior were collected, over a long duration, to train the model using Adam optimizer with an objective to minimize cross-entropy loss function. Lastly, the system was tested for real-time operation and obtained a very good inference accuracy when predicting each patient's behavior in a two-patient scenario.Comment: This paper has been submitted to IEEE Radar Conference 201

SeizeIT: SEIZURE victims are no longer leashed

Seizure considered to be one of the severe and most common type of neurological disorders. Despite the availability of numerous anti-seizure drugs, it is often difficult to control the disease completely and effectively. Lack of close supervision and failure in providing urgent medical care during and after seizure episodes, leads to serious injuries or even death. On the other hand, Use of wireless sensor networks in everyday applications have rapidly increased due to decreased technology costs and improved product reliability. Therefore developing a wearable device to monitor seizure may complete the anamnesis, help medical staff in diagnosing and acute treatment while preventing seizure related accidents. There are number of seizure detection systems available in the market. Still their performance is far from perfect. This paper explores an application of biomedical wireless sensor networks, which attempts to monitor patients in a completely non-invasive and non-intrusive manner. It describes a wearable device together with seizure prediction and alerting system, which is designed to address some issues with seizure detection systems in the market. Its functional block diagram and operating modes are detailed. Possible application areas of the device are also discusse

Epilepsy

With the vision of including authors from different parts of the world, different educational backgrounds, and offering open-access to their published work, InTech proudly presents the latest edited book in epilepsy research, Epilepsy: Histological, electroencephalographic, and psychological aspects. Here are twelve interesting and inspiring chapters dealing with basic molecular and cellular mechanisms underlying epileptic seizures, electroencephalographic findings, and neuropsychological, psychological, and psychiatric aspects of epileptic seizures, but non-epileptic as well

Heart Rate Variability as a Tool for Seizure Prediction: A Scoping Review

: The most critical burden for People with Epilepsy (PwE) is represented by seizures, the unpredictability of which severely impacts quality of life. The design of real-time warning systems that can detect or even predict ictal events would enhance seizure management, leading to high benefits for PwE and their caregivers. In the past, various research works highlighted that seizure onset is anticipated by significant changes in autonomic cardiac control, which can be assessed through heart rate variability (HRV). This manuscript conducted a scoping review of the literature analyzing HRV-based methods for detecting or predicting ictal events. An initial search on the PubMed database returned 402 papers, 72 of which met the inclusion criteria and were included in the review. These results suggest that seizure detection is more accurate in neonatal and pediatric patients due to more significant autonomic modifications during the ictal transitions. In addition, conventional metrics are often incapable of capturing cardiac autonomic variations and should be replaced with more advanced methodologies, considering non-linear HRV features and machine learning tools for processing them. Finally, studies investigating wearable systems for heart monitoring denoted how HRV constitutes an efficient biomarker for seizure detection in patients presenting significant alterations in autonomic cardiac control during ictal events

Psychogenic non-epileptic seizures: a self-directed learning module for nurses

Background: Psychogenic non-epileptic seizures (PNES) are the result of underlying psychopathology and ineffective coping responses. People with PNES represent a significant proportion of admissions to diagnostic epilepsy units. Nurses who are new or inexperienced working in epilepsy units are unaware of the presence of patients who have PNES and as result, are unprepared to provide individualized nursing care to this patient group. Purpose: The purpose of this practicum was to develop a self-directed PNES learning module for nurses to augment the current nurse orientation education with an aim of developing PNES knowledge and sensitivity in nurses before they are assigned their first patient. Methods: Three methodologies were used and completed in this practicum which included an environmental scan, a review of the literature and consultations with key stakeholders. Results: The self-directed PNES learning module was composed of three units which included explanations of key terms essential to nursing care and engagement of the patient who has PNES. Themes of advocacy and compassion are central to the body of work. Conclusion: The goal of the practicum was achieved in its delivery of a resource which provides a means to resolve the learning needs of novice or inexperienced nurses transferring into diagnostic epilepsy units. The practicum has produced a tangible means to improve the nursing care provided to patients who have PNES by developing competency and sensitivity in nurses tasked with providing individualized care to these patients

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

Improving diagnostic procedures for epilepsy through automated recording and analysis of patients’ history

Transient loss of consciousness (TLOC) is a time-limited state of profound cognitive impairment characterised by amnesia, abnormal motor control, loss of responsiveness, a short duration and complete recovery. Most instances of TLOC are caused by one of three health conditions: epilepsy, functional (dissociative) seizures (FDS), or syncope. There is often a delay before the correct diagnosis is made and 10-20% of individuals initially receive an incorrect diagnosis. Clinical decision tools based on the endorsement of TLOC symptom lists have been limited to distinguishing between two causes of TLOC. The Initial Paroxysmal Event Profile (iPEP) has shown promise but was demonstrated to have greater accuracy in distinguishing between syncope and epilepsy or FDS than between epilepsy and FDS. The objective of this thesis was to investigate whether interactional, linguistic, and communicative differences in how people with epilepsy and people with FDS describe their experiences of TLOC can improve the predictive performance of the iPEP. An online web application was designed that collected information about TLOC symptoms and medical history from patients and witnesses using a binary questionnaire and verbal interaction with a virtual agent. We explored potential methods of automatically detecting these communicative differences, whether the differences were present during an interaction with a VA, to what extent these automatically detectable communicative differences improve the performance of the iPEP, and the acceptability of the application from the perspective of patients and witnesses. The two feature sets that were applied to previous doctor-patient interactions, features designed to measure formulation effort or detect semantic differences between the two groups, were able to predict the diagnosis with an accuracy of 71% and 81%, respectively. Individuals with epilepsy or FDS provided descriptions of TLOC to the VA that were qualitatively like those observed in previous research. Both feature sets were effective predictors of the diagnosis when applied to the web application recordings (85.7% and 85.7%). Overall, the accuracy of machine learning models trained for the threeway classification between epilepsy, FDS, and syncope using the iPEP responses from patients that were collected through the web application was worse than the performance observed in previous research (65.8% vs 78.3%), but the performance was increased by the inclusion of features extracted from the spoken descriptions on TLOC (85.5%). Finally, most participants who provided feedback reported that the online application was acceptable. These findings suggest that it is feasible to differentiate between people with epilepsy and people with FDS using an automated analysis of spoken seizure descriptions. Furthermore, incorporating these features into a clinical decision tool for TLOC can improve the predictive performance by improving the differential diagnosis between these two health conditions. Future research should use the feedback to improve the design of the application and increase perceived acceptability of the approach