Detection of subtle nocturnal motor activity from 3-D accelerometry recordings in epilepsy patients

T.M.E. Nijsen, P.J.M. Cluitmans, J.B.A.M. Arends, P.A.M. Griep

Research output: Contribution to journalArticleAcademicpeer-review

44 Citations (Scopus)
1 Downloads (Pure)


This paper presents a first step towards reliable detection of nocturnal epileptic seizures based on 3-D accelerometry (ACM) recordings. The main goal is to distinguish between data with and without subtle nocturnal motor activity, thus reducing the amount of data that needs further (more complex) analysis for seizure detection. From 15 ACM signals (measured on five positions on the body), two features are computed, the variance and the jerk. In the resulting 2-D feature space, a linear threshold function is used for classification. For training and testing, the algorithm ACM data along with video data is used from nocturnal registrations in seven mentally retarded patients with severe epilepsy. Per patient, the algorithm detected 100% of the periods of motor activity that are marked in video recordings and the ACM signals by experts. From all the detections, 43%-89% was correct (mean=65%). We were able to reduce the amount of data that need to be analyzed considerably. The results show that our approach can be used for detection of subtle nocturnal motor activity. Furthermore, our results indicate that our algorithm is robust for fluctuations across patients. Consequently, there is no need for training the algorithm for each new patient.
Original languageEnglish
Pages (from-to)2073-2081
Number of pages9
JournalIEEE Transactions on Biomedical Engineering
Issue number11
Publication statusPublished - 2007


Dive into the research topics of 'Detection of subtle nocturnal motor activity from 3-D accelerometry recordings in epilepsy patients'. Together they form a unique fingerprint.

Cite this