Automated seizure detection with noninvasive wearable devices: A systematic review and meta-analysis.

Objective: This study was undertaken to review the reported performance of noninvasive wearable devices in detecting epileptic seizures and psychogenic nonepileptic seizures (PNES).

Methods: We conducted a systematic review and meta-analysis of studies reported up to November 15, 2021. We included studies that used video-electroencephalographic (EEG) monitoring as the gold standard to determine the sensitivity and false alarm rate (FAR) of noninvasive wearables for automated seizure detection.

Identifying smoker subgroups with high versus low smoking cessation attempt probability: A decision tree analysis approach.

Background: Regression-based research has successfully identified independent predictors of smoking cessation, both its initiation and maintenance. However, it is unclear how these various independent predictors interact with each other and conjointly influence smoking behaviour. As a proof-of-concept, this study used decision tree analysis (DTA) to identify the characteristics of smoker subgroups with high versus low smoking cessation initiation probability based on the conjoint effects of four predictor variables, and determine any variations by socio-economic status (SES).

The utility of an automated and ambulatory device for detecting and differentiating epileptic and psychogenic non-epileptic seizures.

Objective: Accurate differentiation between epileptic seizures (ES) and psychogenic non-epileptic seizures (PNES) can be challenging based on history alone. Inpatient video EEG monitoring (VEM) is often needed for a definitive diagnosis. However, VEM is highly resource intensive, is of limited availability, and cannot be undertaken over long periods.

Novel features for capturing temporal variations of rhythmic limb movement to distinguish convulsive epileptic and psychogenic nonepileptic seizures.

Objective: To investigate the characteristics of motor manifestation during convulsive epileptic and psychogenic nonepileptic seizures (PNES), captured using a wrist-worn accelerometer (ACM) device. The main goal was to find quantitative ACM features that can differentiate between convulsive epileptic and convulsive PNES.

Methods: In this study, motor data were recorded using wrist-worn ACM-based devices.

Improved Detection and Classification of Convulsive Epileptic and Psychogenic Non-epileptic Seizures Using FLDA and Bayesian Inference.

A high number of patients with epileptic seizures (ES) are misdiagnosed due to prevalence of mimic conditions. The clinical characteristics of mimics are often similar to ES. The events mostly misdiagnosed are of psychogenic origin and are termed as psychogenic non-epileptic seizures (PNES).

Onset Detection of Epileptic Seizures From Accelerometry Signal.

Epileptic seizures are the result of any abnormal asynchronous firing of cortical neurons. Seizures are abrupt and pose a risk of injury and fatal harm to the patient. Epilepsy affects patients quality of life (QOL) and imposes financial, social, and physical burden on the patient.

Automated Detection of Convulsive Seizures Using a Wearable Accelerometer Device.

Epileptic seizure detection requires specialized approaches such as video/electroencephalography monitoring. However, these approaches are restricted mainly to hospital setting and requires video/EEG analysis by experts, which makes these approaches resource- and labor-intensive. In contrast, we aim to develop a wireless remote monitoring system based on a single wrist-worn accelerometer device, which is sensitive to multiple types of convulsive seizures and is capable of detecting seizures with short duration.

Detection of generalized tonic-clonic seizures using short length accelerometry signal.

Epileptic seizures are characterized by the excessive and abrupt electrical discharge in the brain. This asynchronous firing of neurons causes unprovoked convulsions which can be a cause of sudden unexpected death in epilepsy (SUDEP). Remote monitoring of epileptic patients can help prevent SUDEP.

Gaussian mixture model for the identification of psychogenic non-epileptic seizures using a wearable accelerometer sensor.

Any abnormal hypersynchronus activity of neurons can be characterized as an epileptic seizure (ES). A broad class of non-epileptic seizures is comprised of Psychogenic non-epileptic seizures (PNES). PNES are paroxysmal events, which mimics epileptic seizures and pose a diagnostic challenge with epileptic seizures due to their clinical similarities.

Classification of convulsive psychogenic non-epileptic seizures using histogram of oriented motion of accelerometry signals.

A seizure is caused due to sudden surge of electrical activity within the brain. There is another class of seizures called psychogenic non-epileptic seizure (PNES) that mimics epilepsy, but is caused due to underlying psychology. The diagnosis of PNES is done using video-electroencephalography monitoring (VEM), which is a resource intensive process.

Classification of convulsive psychogenic non-epileptic seizures using muscle transforms obtained from accelerometry signal.

Convulsive psychogenic non-epileptic seizure (PNES) can be characterized as events which mimics epileptic seizures but do not show any characteristic changes on electroencephalogram (EEG). Correct diagnosis requires video-electroencephalography monitoring (VEM) as the diagnosis of PNES is extremely difficult in primary health care. Recent work has demonstrated the usefulness of accelerometry signal taken during a seizure in classification of PNES.

Automatic Detection and Classification of Convulsive Psychogenic Nonepileptic Seizures Using a Wearable Device.

Epilepsy is one of the most common neurological disorders and patients suffer from unprovoked seizures. In contrast, psychogenic nonepileptic seizures (PNES) are another class of seizures that are involuntary events not caused by abnormal electrical discharges but are a manifestation of psychological distress. The similarity of these two types of seizures poses diagnostic challenges that often leads in delayed diagnosis of PNES.

Technical Validation of ARTSENS-An Image Free Device for Evaluation of Vascular Stiffness.

Vascular stiffness is an indicator of cardiovascular health, with carotid artery stiffness having established correlation to coronary heart disease and utility in cardiovascular diagnosis and screening. State of art equipment for stiffness evaluation are expensive, require expertise to operate and not amenable for field deployment. In this context, we developed ARTerial Stiffness Evaluation for Noninvasive Screening (ARTSENS), a device for image free, noninvasive, automated evaluation of vascular stiffness amenable for field use.