Study on spike-and-wave detection in epileptic signals using t-location-scale distribution and the K-nearest neighbors classifier
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866911941891457024 |
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| author | Quintero-Rincón, Antonio Prendes, Jorge Muro, Valeria D'Giano, Carlos |
| author_facet | Quintero-Rincón, Antonio Prendes, Jorge Muro, Valeria D'Giano, Carlos |
| contents | Pattern classification in electroencephalography (EEG) signals is an important problem in biomedical engineering since it enables the detection of brain activity, particularly the early detection of epileptic seizures. In this paper, we propose a k-nearest neighbors classification for epileptic EEG signals based on a t-location-scale statistical representation to detect spike-and-waves. The proposed approach is demonstrated on a real dataset containing both spike-and-wave events and normal brain function signals, where our performance is evaluated in terms of classification accuracy, sensitivity, and specificity. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2405_14896 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Study on spike-and-wave detection in epileptic signals using t-location-scale distribution and the K-nearest neighbors classifier Quintero-Rincón, Antonio Prendes, Jorge Muro, Valeria D'Giano, Carlos Applications Machine Learning Computation Methodology Pattern classification in electroencephalography (EEG) signals is an important problem in biomedical engineering since it enables the detection of brain activity, particularly the early detection of epileptic seizures. In this paper, we propose a k-nearest neighbors classification for epileptic EEG signals based on a t-location-scale statistical representation to detect spike-and-waves. The proposed approach is demonstrated on a real dataset containing both spike-and-wave events and normal brain function signals, where our performance is evaluated in terms of classification accuracy, sensitivity, and specificity. |
| title | Study on spike-and-wave detection in epileptic signals using t-location-scale distribution and the K-nearest neighbors classifier |
| topic | Applications Machine Learning Computation Methodology |
| url | https://arxiv.org/abs/2405.14896 |