Time-Frequency Analysis of Accelerometry Data for Detection of Myoclonic Seizures
| Autor: | Pierre J. M. Cluitmans, Ronald M. Aarts, P.A.M. Griep, Tamara M.E. Nijsen |
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| Přispěvatelé: | Signal Processing Systems, Medical signal processing, Biomedical Diagnostics Lab |
| Rok vydání: | 2010 |
| Předmět: |
Movement
Speech recognition Acceleration Monitoring Ambulatory Epilepsies Myoclonic Linear classifier Daubechies wavelet symbols.namesake Seizures Humans Electrical and Electronic Engineering Continuous wavelet transform Mathematics Models Statistical Fourier Analysis Short-time Fourier transform Discriminant Analysis Wavelet transform Signal Processing Computer-Assisted General Medicine Computer Science Applications Time–frequency analysis Fourier transform Fourier analysis Arm symbols Biotechnology |
| Zdroj: | IEEE Transactions on Information Technology in Biomedicine, 14(5), 1197-1203. Institute of Electrical and Electronics Engineers |
| ISSN: | 1089-7771 |
| DOI: | 10.1109/titb.2010.2058123 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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