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| Autori principali: | , , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| Soggetti: | |
| Accesso online: | https://arxiv.org/abs/2411.17709 |
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| _version_ | 1866915036226650112 |
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| author | Poziomska, Martyna Dovgialo, Marian Olbratowski, Przemysław Niedbalski, Paweł Ogniewski, Paweł Zych, Joanna Rogala, Jacek Żygierewicz, Jarosław |
| author_facet | Poziomska, Martyna Dovgialo, Marian Olbratowski, Przemysław Niedbalski, Paweł Ogniewski, Paweł Zych, Joanna Rogala, Jacek Żygierewicz, Jarosław |
| contents | This study investigates the impact of quantity and diversity of data on the performance of various machine-learning models for detecting general EEG pathology. We utilized an EEG dataset of 2,993 recordings from Temple University Hospital and a dataset of 55,787 recordings from Elmiko Biosignals sp. z o.o. The latter contains data from 39 hospitals and a diverse patient set with varied conditions. Thus, we introduce the Elmiko dataset - the largest publicly available EEG corpus. Our findings show that small and consistent datasets enable a wide range of models to achieve high accuracy; however, variations in pathological conditions, recording protocols, and labeling standards lead to significant performance degradation. Nonetheless, increasing the number of available recordings improves predictive accuracy and may even compensate for data diversity, particularly in neural networks based on attention mechanism or transformer architecture. A meta-model that combined these networks with a gradient-boosting approach using handcrafted features demonstrated superior performance across varied datasets. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2411_17709 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Quantity versus Diversity: Influence of Data on Detecting EEG Pathology with Advanced ML Models Poziomska, Martyna Dovgialo, Marian Olbratowski, Przemysław Niedbalski, Paweł Ogniewski, Paweł Zych, Joanna Rogala, Jacek Żygierewicz, Jarosław Signal Processing Machine Learning This study investigates the impact of quantity and diversity of data on the performance of various machine-learning models for detecting general EEG pathology. We utilized an EEG dataset of 2,993 recordings from Temple University Hospital and a dataset of 55,787 recordings from Elmiko Biosignals sp. z o.o. The latter contains data from 39 hospitals and a diverse patient set with varied conditions. Thus, we introduce the Elmiko dataset - the largest publicly available EEG corpus. Our findings show that small and consistent datasets enable a wide range of models to achieve high accuracy; however, variations in pathological conditions, recording protocols, and labeling standards lead to significant performance degradation. Nonetheless, increasing the number of available recordings improves predictive accuracy and may even compensate for data diversity, particularly in neural networks based on attention mechanism or transformer architecture. A meta-model that combined these networks with a gradient-boosting approach using handcrafted features demonstrated superior performance across varied datasets. |
| title | Quantity versus Diversity: Influence of Data on Detecting EEG Pathology with Advanced ML Models |
| topic | Signal Processing Machine Learning |
| url | https://arxiv.org/abs/2411.17709 |