Detection of subtle nocturnal motor activity from 3-D accelerometry recordings in epilepsy patients
| Autor: | P.A.M. Griep, Johan Arends, Pierre J. M. Cluitmans, Tamara M.E. Nijsen |
|---|---|
| Přispěvatelé: | Signal Processing Systems, Medical signal processing |
| Jazyk: | angličtina |
| Rok vydání: | 2007 |
| Předmět: |
Adult
Male medicine.medical_specialty Feature vector Speech recognition Movement Polysomnography Feature extraction Acceleration Biomedical Engineering Audiology Nocturnal Motor Activity Sensitivity and Specificity Epilepsy medicine Humans Diagnosis Computer-Assisted Monitoring Physiologic Supervised learning Reproducibility of Results medicine.disease Jerk Statistical classification Algorithm design Female Psychology |
| Zdroj: | IEEE Transactions on Biomedical Engineering, 54(11), 2073-2081. IEEE Computer Society |
| ISSN: | 0018-9294 |
| DOI: | 10.1109/tbme.2007.895114 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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