Automated Detection of Convulsive Seizures Using a Wearable Accelerometer Device

Abstract

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 using a single wrist-worn accelerometer sensor, which is sensitive to multiple types of convulsive seizures and is capable of detecting seizures with short duration. Simple time domain features including a new set of Poincare plot based features were extracted from the active movement events recorded using a wrist-worn accelerometer sensor. The best features were then selected using the area under the ROC curve analysis. Kernelized support vector data description (SVDD) was then used to classify non-seizure and seizure events. The proposed algorithm was evaluated on 5,576h of recordings from 79 patients and detected 40 (86.95%) of 46 convulsive seizures (generalized tonic-clonic (GTCS), psychogenic non-epileptic (PNES), and complex partial seizures (CPS)) from twenty patients with a total of 270 false alarms (1.16/24h). Furthermore, the algorithm showed a comparable performance (sensitivity 95.23% and false alarm rate 0.64/24h) with respect to existing unimodal and multi-modal methods for GTCS detection. The promising results shows the potential to build an ambulatory monitoring convulsive seizure detection system. A wearable accelerometer based seizure detection system would aid in continuous assessment of convulsive seizures in a timely and non-invasive manner.

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

“…The proposed quantitative method yields a nonlinear projection of the three‐dimensional (3D) rhythmic limb movement into a numerical score, thus allowing reliable distinction between GTCS and convulsive PNES with less experience. In addition, wearable ACM‐based devices can be used reliably for long‐term continuous monitoring of patients; therefore, in the future, the clinical utility of the proposed approach can be further enhanced into a wearable device by the incorporation of automated algorithm for detection and differentiation of GTCS and convulsive PNES.…”

“…Accelerometers (ACMs) have been shown to be an effective tool for detection of convulsive seizures especially, GTCS . In our previous work, we showed that ACMs can be used reliably for the detection of convulsive PNES events. However, differentiation of convulsive PNES and GTCS requires identification of unique features that can distinguish epileptic and nonepileptic motor activity.…”

“…It has been used extensively in the analysis of cardiac signals and is central to the field of heart‐rate variability (HRV) analysis . In addition, it can describe nonlinear dynamics of short‐length signals . Therefore, we hypothesize that the use of Poincaré maps will provide better insight about the motor manifestation characteristics of different seizure events (GTCS and PNES).…”

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

“…The use of limb-worn accelerometry sensors that can record movements is one such avenue that is being explored by a number of groups. [13][14][15] Our previous research has shown that the evolutionary pattern of the frequencies of rhythmic movement artifacts on EEG during PNES differs from that of ES. 16 Convulsive PNES were demonstrated to display a characteristic pattern of rhythmic movement artifact that remains stable over time during the event, whereas the EEG activity during convulsive ES tends to evolve throughout.…”

“…The use of limb‐worn accelerometry sensors that can record movements is one such avenue that is being explored by a number of groups . Our previous research has shown that the evolutionary pattern of the frequencies of rhythmic movement artifacts on EEG during PNES differs from that of ES .…”