Till sidans topp

Sidansvarig: Webbredaktion
Sidan uppdaterades: 2012-09-11 15:12

Tipsa en vän
Utskriftsversion

Tonic-clonic seizure dete… - Göteborgs universitet Till startsida
Webbkarta
Till innehåll Läs mer om hur kakor används på gu.se

Tonic-clonic seizure detection using accelerometry-based wearable sensors: A prospective, video-EEG controlled study

Artikel i vetenskaplig tidskrift
Författare Dongni Johansson
Fredrik Ohlsson
David Krysl
Bertil Rydenhag
Madeleine Czarnecki
Niclas Gustafsson
Jan Wipenmyr
Tomas McKelvey
Kristina Malmgren
Publicerad i Seizure
Volym 65
Sidor 48-54
ISSN 1059-1311
Publiceringsår 2019
Publicerad vid Institutionen för neurovetenskap och fysiologi, sektionen för klinisk neurovetenskap
Sidor 48-54
Språk en
Länkar doi.org/10.1016/j.seizure.2018.12.0...
Ämnesord Epilepsy, Machine learning, Seizure detection devices, Tonic-clonic seizure, Wrist-worn sensors
Ämneskategorier Neurologi

Sammanfattning

© 2018 Purpose: The aim of this prospective, video-electroencephalography (video-EEG) controlled study was to evaluate the performance of an accelerometry-based wearable system to detect tonic-clonic seizures (TCSs) and to investigate the accuracy of different seizure detection algorithms using separate training and test data sets. Methods: Seventy-five epilepsy surgery candidates undergoing video-EEG monitoring were included. The patients wore one three-axis accelerometer on each wrist during video-EEG. The accelerometer data was band-pass filtered and reduced using a movement threshold and mapped to a time-frequency feature space representation. Algorithms based on standard binary classifiers combined with a TCS specific event detection layer were developed and trained using the training set. Their performance was evaluated in terms of sensitivity and false positive (FP) rate using the test set. Results: Thirty-seven available TCSs in 11 patients were recorded and the data was divided into disjoint training (27 TCSs, three patients) and test (10 TCSs, eight patients) data sets. The classification algorithms evaluated were K-nearest-neighbors (KNN), random forest (RF) and a linear kernel support vector machine (SVM). For the TCSs detection performance of the three algorithms in the test set, the highest sensitivity was obtained for KNN (100% sensitivity, 0.05 FP/h) and the lowest FP rate was obtained for RF (90% sensitivity, 0.01 FP/h). Conclusions: The low FP rate enhances the clinical utility of the detection system for long-term reliable seizure monitoring. It also allows a possible implementation of an automated TCS detection in free-living environment, which could contribute to ascertain seizure frequency and thereby better seizure management.

Sidansvarig: Webbredaktion|Sidan uppdaterades: 2012-09-11
Dela:

På Göteborgs universitet använder vi kakor (cookies) för att webbplatsen ska fungera på ett bra sätt för dig. Genom att surfa vidare godkänner du att vi använder kakor.  Vad är kakor?

Denna text är utskriven från följande webbsida:
http://gu.se/forskning/publikation/?publicationId=276478
Utskriftsdatum: 2019-11-20