Time-frequency analysis of accelerometry data for detection of myoclonic seizures

Four time-frequency and time-scale methods are studied for their ability of detecting myoclonic seizures from accelerometric data. Methods that are used are: the short-time Fourier transform (STFT), the Wigner distribution (WD), the continuous wavelet transform (CWT) using a Daubechies wavelet, and a newly introduced model-based matched wavelet transform (MOD). Real patient data are analyzed using these four timefrequency and time-scale methods. To obtain quantitative results, all four methods are evaluated in a linear classification setup. Data from 15 patients are used for training and data from 21 patients for testing. Using features based on the CWT and MOD, the success rate of the classifier was 80%. Using STFT or WD-based features, the classification success is reduced. Analysis of the false positives revealed that they were either clonic seizures, the onset of tonic seizures, or sharp peaks in "normal" movements indicating that the patient was making a jerky movement. ...

• Type: Journal
• Year: 2010
• Where: TITB
• Authors: Tamara M. E. Nijsen, Ronald M. Aarts, Pierre J. M. Cluitmans, Paul A. M. Griep
• Keywords: Education, Myoclonic Seizures, Time-scale Methods, TITB 2010, Wavelet Transform