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

  • Authors:

  • Tamara M. E. Nijsen;Ronald M. Aarts;Pierre J. M. Cluitmans;Paul A. M. Griep

  • Affiliations:

  • Biomedical Sensor Systems Department, Philips Research Laboratories, Eindhoven, The Netherlands and Epilepsy Centre Kempenhaeghe, Heeze, The Netherlands;Department of Electrical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands and Philips Research Laboratories;Department of Electrical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands;Department of Clinical Physics, Epilepsy Centre Kempenhaeghe, Heeze, The Netherlands

  • Venue:
  • IEEE Transactions on Information Technology in Biomedicine
  • Year:
  • 2010

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Abstract

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 time-frequency 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. All these movements are considered clinically important to detect. Thus, the results show that both CWT and MOD are useful for the detection of myoclonic seizures. On top of that, MOD has the advantage that it consists of parameters that are related to seizure duration and intensity that are physiologically meaningful. Furthermore, in future work, the model can also be useful for the detection of other motor seizure types.