A clinical evaluation of a novel algorithm in the reliable detection of epileptic seizures. (November 2020)

• Record Type: Journal Article
• Authors: Brotherstone, Ruth; McLellan, Ailsa; Graham, Catriona; Fisher, Katie
• Is Part Of: Seizure. Volume 82(2020)
• Page Start: 109
• Page End: 117
• Publication Date: 2020-11
• Subjects: Epilepsy -- Seizure detection -- Heart rate change algorithm; Epilepsy -- Periodicals; Epilepsy -- Periodicals; Seizures -- Periodicals; Épilepsie -- Périodiques; Electronic journals; Electronic journals; 616.853
• DOI: 10.1016/j.seizure.2020.09.017
• Languages: English
• ISSNs: 1059-1311

Abstract

Highlights: Seizure detection multimodal novel algorithm using photoplethysmography has been clinically evaluated with pre-specified cut off points. Phase 3 prospective real time clinical trial, with synchronised EEG videotelemetry for multicentre paediatric and adult cohorts. False Alarm Rate (24 h FAR) 4.5, Median 1.0, standard deviation 7.8, 320 min between false alarms, 0.187/h. Seizure detection latency (median heart rate change) from seizure onset to detection time 58 s (31, 90). Abstract: Purpose: Undetected and prolonged epileptic seizures can result in hypoxic brain damage or death and occur most often when the victim is in bed alone or unsupervised. Sudden unexpected death in epilepsy may not always be preventable but it is believed that timely assistance with rescue medication and body re-positioning may overcome respiratory compromise in some cases. A novel algorithm based on a real time moving 9 s epoch, calculating 25 % percentage heart rate change and/or an oxygen saturation trigger level of <85 % was developed using photoplethysmography and incorporated into a prototype data storage device. Methods: The algorithm was clinically evaluated in this multicentre trial in the detection of clinically significant epileptic seizures. A range of epileptic seizures and normal physiological events were recorded and classified by reference standard EEG Videotelemetry and time-synchronised event data recorded by the prototype device incorporating the pre-specified cut-off points prospectively and retrospective analysis of all events. Results: 119 participants who were attending electroencephalographic (EEG) videotelemetry as part of their clinical management of their epilepsy consented to take part in the trial. 683 epileptic seizures (77 clinically significant seizures) and 2648 normal physiological events were captured. When using pre-specified cut-off point 25 % heart rate change and/or oxygen desaturation <85 % on the basis of one/other, the device showed a sensitivity of 87 % for detecting clinically significant seizures. False Alarm Rate 4.5 (24 h FAR), detection latency of 58 s using heart rate percentage change. Conclusions: The results indicate that the novel algorithm can be used in detecting clinically significant seizures.