Adaptive wavelet transform-based method for recognizing characteristic oscillatory patterns
The problem concerning the automatic recognition of characteristic oscillatory patterns in multicomponent signals is investigated using the brain’s electric activity records, electroencephalograms (EEGs), as an example. It has been ascertained that recognition errors can be decreased by optimally se...
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| Veröffentlicht in: | Journal of communications technology & electronics 2013-08, Vol.58 (8), p.790-795 |
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| container_issue | 8 |
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| container_title | Journal of communications technology & electronics |
| container_volume | 58 |
| creator | Nazimov, A. I. Pavlov, A. N. Hramov, A. E. Grubov, V. V. Sitnikova, E. Yu Koronovskii, A. A. |
| description | The problem concerning the automatic recognition of characteristic oscillatory patterns in multicomponent signals is investigated using the brain’s electric activity records, electroencephalograms (EEGs), as an example. It has been ascertained that recognition errors can be decreased by optimally selecting continuous wavelet transform (CWT) parameters to obtain characteristics describing the most important information on analyzed patterns. The adaptive CWT-based method for identifying the characteristic types of EEG rhythmic activity is proposed. |
| doi_str_mv | 10.1134/S1064226913070115 |
| format | Article |
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| source | SpringerLink Journals - AutoHoldings |
| subjects | Analysis Brain Character recognition Communications Engineering Electroencephalography Electronics Engineering Error analysis Fourier transforms Investigations Medical equipment Methods Networks Optimization Pattern recognition Recognition Signal processing Studies Theory and Methods of Signal Processing Wavelet Wavelet transforms |
| title | Adaptive wavelet transform-based method for recognizing characteristic oscillatory patterns |
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