Automatic detection of epileptic seizure using time-frequency distributions
The aim of this work is to introduce a new method based on time frequency distribution for classifying the EEG signals. Some parameters are extracted using time-frequency distribution as inputs to a feed-forward backpropagation neural networks (FBNN). The proposed method had better results with 98.2...
| Κύριοι συγγραφείς: | , , , , |
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| Μορφή: | Conference item |
| Έκδοση: |
2006
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| _version_ | 1826298485819834368 |
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| author | Mohseni, H Maghsoudi, A Kadbi, M Hashemi, J Ashourvan, A |
| author_facet | Mohseni, H Maghsoudi, A Kadbi, M Hashemi, J Ashourvan, A |
| author_sort | Mohseni, H |
| collection | OXFORD |
| description | The aim of this work is to introduce a new method based on time frequency distribution for classifying the EEG signals. Some parameters are extracted using time-frequency distribution as inputs to a feed-forward backpropagation neural networks (FBNN). The proposed method had better results with 98.25% accuracy compared to previously studied methods such as wavelet transform, entropy, logistic regression and Lyapunov exponent. |
| first_indexed | 2024-03-07T04:47:34Z |
| format | Conference item |
| id | oxford-uuid:d3d75cff-86ec-4bea-851b-c1b60416ed09 |
| institution | University of Oxford |
| last_indexed | 2024-03-07T04:47:34Z |
| publishDate | 2006 |
| record_format | dspace |
| spelling | oxford-uuid:d3d75cff-86ec-4bea-851b-c1b60416ed092022-03-27T08:14:02ZAutomatic detection of epileptic seizure using time-frequency distributionsConference itemhttp://purl.org/coar/resource_type/c_5794uuid:d3d75cff-86ec-4bea-851b-c1b60416ed09Symplectic Elements at Oxford2006Mohseni, HMaghsoudi, AKadbi, MHashemi, JAshourvan, AThe aim of this work is to introduce a new method based on time frequency distribution for classifying the EEG signals. Some parameters are extracted using time-frequency distribution as inputs to a feed-forward backpropagation neural networks (FBNN). The proposed method had better results with 98.25% accuracy compared to previously studied methods such as wavelet transform, entropy, logistic regression and Lyapunov exponent. |
| spellingShingle | Mohseni, H Maghsoudi, A Kadbi, M Hashemi, J Ashourvan, A Automatic detection of epileptic seizure using time-frequency distributions |
| title | Automatic detection of epileptic seizure using time-frequency distributions |
| title_full | Automatic detection of epileptic seizure using time-frequency distributions |
| title_fullStr | Automatic detection of epileptic seizure using time-frequency distributions |
| title_full_unstemmed | Automatic detection of epileptic seizure using time-frequency distributions |
| title_short | Automatic detection of epileptic seizure using time-frequency distributions |
| title_sort | automatic detection of epileptic seizure using time frequency distributions |
| work_keys_str_mv | AT mohsenih automaticdetectionofepilepticseizureusingtimefrequencydistributions AT maghsoudia automaticdetectionofepilepticseizureusingtimefrequencydistributions AT kadbim automaticdetectionofepilepticseizureusingtimefrequencydistributions AT hashemij automaticdetectionofepilepticseizureusingtimefrequencydistributions AT ashourvana automaticdetectionofepilepticseizureusingtimefrequencydistributions |