Abstract—Tonic-clonic seizures pose a serious risk of injury to those afflicted. Therefore, patients both in home-based and residential care can require constant monitoring. Technical aids may help by alerting caregivers of detected seizures. So far, the usability of several sensor systems for seizure detection has been shown. However, most of these systems require some sensors to be physically attached to the patient or are limited with respect to their accuracy or robustness. Thus, we investigated the feasibility of using depth image sequences for the detection of seizure-like periodic motion. A static camera setup was utilized to monitor a limited region of interest comparable to a patient's bed during the night. Data of simulated limb motion including seizure-like movement was acquired with help of a robot moving a hand phantom both uncovered and covered by a duvet, ensuring the availability of a known ground truth. Subsequently, a characteristic of the recorded images which may be used to differentiate between normal and seizure-like motion was defined. Finally, linear discriminant analysis was applied to the determined characteristic. We found that the rapid detection of seizure-like periodic motion from depth image sequences is feasible even when the moving limb is covert by a blanket.
Index Terms—vision based seizure detection, periodicity analysis, depth image processing, epilepsy
Cite: Mareike Wendebourg, Omer Rajput, and Alexander Schlaefer, "Detection of Simulated Clonic Seizures from Depth Camera Recordings," Journal of Image and Graphics, Vol. 6, No. 2, pp. 88-94, December 2018. doi: 10.18178/joig.6.2.88-94
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