Non-invasive convulsive seizure assessment using wearable accelerometer device

Abstract

Epilepsy can be characterized by recurrent and unprovoked episodes of dysfunctional neuronal activity in time coupled with a change in behavior and altered state of consciousness. Epilepsy is one of the most prevalent neurological disorders. The prevalence of epilepsy is approximately 50 million worldwide. One of the major disabilities attributed to epilepsy is the unpredictability of epileptic seizures (ES). A person cannot call for help during a seizure, often suffering injuries due to falls, burns, tongue biting, etc.; thus, independent living is impaired. A more serious consequence is epilepsy-associated mortality. The increased mortality in epilepsy is attributed mainly to direct causes, i.e., accidental death (drowning, motor vehicle accidents, serious head injuries) and sudden unexpected death in epilepsy (SUDEP). Evidence suggests that appropriate and timely intervention following a seizure can reduce the risk of epilepsy-associated injuries and mortality.

Another class of seizures known as psychogenic non-epileptic seizures (PNES) are involuntary events that share diagnostic similarities with generalized epileptic tonic-clonic seizures (GTCS). PNES events have a causal association to sporadic attacks resulting from autonomic malfunction often linked to major psychosocial distress. PNES has a prevalence of 1-33 cases per 100,000, accounting for 5-20% of patients thought to have epilepsy. Patients with PNES require treatment tailored to address the associated psychosis. There is the potential for severe harm from the adverse effects of the anti-epileptic drugs (AEDs) prescribed to patients with PNES, as well as increased risk of morbidity and mortality due to intubation from prolonged seizures. In this thesis, we describe the development of a wrist-worn accelerometer (ACM)-based system for the automated detection and classification of seizures.

The first section of this thesis describes the development of a wireless remote monitoring system based on a single wrist-worn ACM sensor. A novel seizure detection algorithm was proposed and validated on 5576 h of ACM data recorded from 79 patients admitted to the Epilepsy Monitoring Unit at Royal Melbourne Hospital, Melbourne, Australia. The wearable ACM sensor achieved high seizure detection sensitivity and specificity that correlated with the gold-standard diagnosis. The study showed that a single wrist-worn ACM sensor can efficiently detect different types of convulsive seizures and can differentiate seizures from activities of daily living. In addition, it demonstrated the feasibility of a unobtrusive system for continuous remote monitoring and assessment of patients with epilepsy.

The second section describes novel features based on capturing the temporal variations in rhythmic limb movement during a seizure, to differentiate GTCS from convulsive PNES. We observed that the manifestation of GTCS can be characterized by an onset that involves increased muscle tone, usually accompanied by irregular and asymmetric jerking, followed by tremulousness that translates into clonic activity before subsiding gradually. By contrast, no clear distinction could be seen between different phases of convulsive PNES events. Based on these observations, we proposed two new indexes that capture the onset and subsiding behavior of an event: (1) tonic index (TI), and (2) dispersion decay index (DDI). The study showed that the TI and DDI can differentiate GTCS from convulsive PNES. Importantly, the study showed that different phases of a seizure contain clues for differential diagnosis of PNES, which is an expensive clinical procedure. In addition, these results highlight the feasibility of wearable ACM based device for outpatient diagnosis of convulsive PNES.

Despite rapid technological advancement in surgical techniques and discovery of anti-epileptic medication one-third of the epileptic patients are forced to live with seizures. The unpredictability and risk of injury (falls, head injuries, etc.) associated with seizures are the major contributors to poor quality of life (QOL), requiring round-the-clock monitoring by caregivers. Therefore, in the third section of the thesis we present a novel algorithm for real-time onset detection of GTCS events using a single wrist-worn ACM-based device. The algorithm was tested on 5576 h of ACM data from 79 patients and detected 21 of 21 (sensitivity: 100%, FAR: 0.76/24 h) GTCS events from 12 patients at 7 s from onset. Taking into consideration the challenges to real-time onset detection of seizures, it is anticipated that the proposed wrist-worn ACM-based system would aid efficient real-time remote monitoring of epileptic patients, improving their (QOL) and acting as a seizure triggered alarm and therapeutic system.