NeuroKinect: A Novel Low-Cost 3Dvideo-EEG System for Epileptic Seizure Motion Quantification

Abstract

Epilepsy is a common neurological disorder which affects 0.5–1% of the world population. Its diagnosis relies both on Electroencephalogram (EEG) findings and characteristic seizure−induced body movements − called seizure semiology. Thus, synchronous EEG and (2D)video recording systems (known as Video−EEG) are the most accurate tools for epilepsy diagnosis. Despite the establishment of several quantitative methods for EEG analysis, seizure semiology is still analyzed by visual inspection, based on epileptologists’ subjective interpretation of the movements of interest (MOIs) that occur during recorded seizures. In this contribution, we present NeuroKinect, a low-cost, easy to setup and operate solution for a novel 3Dvideo-EEG system. It is based on a RGB-D sensor (Microsoft Kinect camera) and performs 24/7 monitoring of an Epilepsy Monitoring Unit (EMU) bed. It does not require the attachment of any reflectors or sensors to the patient’s body and has a very low maintenance load. To evaluate its performance and usability, we mounted a state-of-the-art 6-camera motion-capture system and our low-cost solution over the same EMU bed. A comparative study of seizure-simulated MOIs showed an average correlation of the resulting 3D motion trajectories of 84.2%. Then, we used our system on the routine of an EMU and collected 9 different seizures where we could perform 3D kinematic analysis of 42 MOIs arising from the temporal (TLE) (n = 19) and extratemporal (ETE) brain regions (n = 23). The obtained results showed that movement displacement and movement extent discriminated both seizure MOI groups with statistically significant levels (mean = 0.15 m vs. 0.44 m, p<0.001; mean = 0.068 m3 vs. 0.14 m3, p<0.05, respectively). Furthermore, TLE MOIs were significantly shorter than ETE (mean = 23 seconds vs 35 seconds, p<0.01) and presented higher jerking levels (mean = 345 ms−3 vs 172 ms−3, p<0.05). Our newly implemented 3D approach is faster by 87.5% in extracting body motion trajectories when compared to a 2D frame by frame tracking procedure. We conclude that this new approach provides a more comfortable (both for patients and clinical professionals), simpler, faster and lower-cost procedure than previous approaches, therefore providing a reliable tool to quantitatively analyze MOI patterns of epileptic seizures in the routine of EMUs around the world. We hope this study encourages other EMUs to adopt similar approaches so that more quantitative information is used to improve epilepsy diagnosis.

Automated analysis of seizure semiology and brain electrical activity in presurgery evaluation of epilepsy: A focused survey

“…28, 35 Pediaditis et al, 36 in their thorough review of visionbased motion analysis of epileptic seizures, evaluated different methodologies using marker-based and marker-free systems. 14 Video monitoring is still the ideal detection sensor to assess seizures characterized by motor phenomena.…”

“…A recent approach proposed by Cunha et al 35 implemented a low-cost 3D video-EEG system to perform a kinematic analysis and classify TLE and extratemporal lobe epilepsies (EXTE). The hardware was based on an redgreen-blue-depth (RGB-D) sensor (Microsoft Kinect camera) connected to a custom acquisition software tool called Kinect-Tracker.…”

“…Pediaditis et al 72 pioneered a method for facial motion analysis in epilepsy using an averaging background method and dense optical flow for the automatic feature extraction in video sequences. Image adapted from Cunha et al 35 Epilepsia ILAE Electrophysiological analysis in epilepsy EEG has long been considered the gold standard for the diagnosis of seizures. Later, 73 the authors improved the previous methodology using the Viola-Jones algorithm for face and eyes detection to study facial expressions from patients with absence seizures.…”

“…Noncamera approaches for motion quantification and classification among different epilepsy types have been investigated using devices such as accelerometers and gyroscopes; however, any sudden movement can be registered as a seizure event and they are not suitable for all kinds of semiology such as facial expressions. 28,35 Pediaditis et al, 36 in their thorough review of visionbased motion analysis of epileptic seizures, evaluated different methodologies using marker-based and marker-free systems. Marker-based techniques must employ reference markers such as reflective materials attached to the key body parts to track the velocity and trajectory of the movements.…”

“…These methodologies are still undergoing improvement; for example, this image illustrates that the fitting of the skeleton model has not been completely accurate because one of the two arms is detected entirely incorrectly. Image adapted from Cunha et al 35 Epilepsia ILAE…”