Abstract
Objective
Epilepsy is a neurological disease that affects approximately 50 million people worldwide, 30% of which suffer from refractory epilepsy and recurring seizures, which may contribute to higher anxiety levels and poorer quality of life. Seizure detection may contribute to addressing some of the challenges associated with this condition, by providing information to health professionals regarding seizure frequency, type, and/or location in the brain, thereby improving diagnostic accuracy and medication adjustment, and alerting caregivers or emergency services of dangerous seizure episodes. The main focus of this work was the development of an accurate video-based seizure detection method that ensured unobtrusiveness and privacy preservation, as well as provided novel approaches to reduce confounds and increase reliability.
Methods
The proposed approach is a video-based seizure detection method based on Optical Flow, Principal Component Analysis, Independent Component Analysis and Machine Learning classification. This method was tested on a set of 21 tonic-clonic seizure videos (5 to 30 minutes each, total of 4 hours and 36 minutes of recordings) from 12 patients using Leave-One-Subject-Out Cross-Validation.
Results
High accuracy levels were observed, namely a sensitivity and specificity of 99.06% ± 1.65% at the equal error rate and an average latency of 37.45 ± 1.31 seconds. When compared to annotations by health professionals, the beginning and ending of seizures was detected with an average offset of 9.69 ± 0.97 seconds.
Significance
The video-based seizure detection method developed herein is highly accurate. Moreover, it is intrinsically privacy-preserving, due to the employment of Optical Flow motion quantification. Additionally, due to our novel independence-based approach, this method is robust to different lighting conditions, partial occlusions of the patient, and other movements in the video frame, thereby setting the base for accurate and unobtrusive seizure detection.
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