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| Autori principali: | , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| Soggetti: | |
| Accesso online: | https://arxiv.org/abs/2503.16928 |
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| _version_ | 1866908276825784320 |
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| author | Martin, Thibault Sauleau, Paul Haegelen, Claire Jannin, Pierre Baxter, John S. H. |
| author_facet | Martin, Thibault Sauleau, Paul Haegelen, Claire Jannin, Pierre Baxter, John S. H. |
| contents | The analysis of electrophysiological data is crucial for certain surgical procedures such as deep brain stimulation, which has been adopted for the treatment of a variety of neurological disorders. During the procedure, auditory analysis of these signals helps the clinical team to infer the neuroanatomical location of the stimulation electrode and thus optimize clinical outcomes. This task is complex, and requires an expert who in turn requires significant training. In this paper, we propose a generative neural network, called MerGen, capable of simulating de novo electrophysiological recordings, with a view to providing a realistic learning tool for clinicians trainees for identifying these signals. We demonstrate that the generated signals are perceptually indistinguishable from real signals by experts in the field, and that it is even possible to condition the generation efficiently to provide a didactic simulator adapted to a particular surgical scenario. The efficacy of this conditioning is demonstrated, comparing it to intra-observer and inter-observer variability amongst experts. We also demonstrate the use of this network for data augmentation for automatic signal classification which can play a role in decision-making support in the operating theatre. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_16928 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | MerGen: Micro-electrode recording synthesis using a generative data-driven approach Martin, Thibault Sauleau, Paul Haegelen, Claire Jannin, Pierre Baxter, John S. H. Machine Learning The analysis of electrophysiological data is crucial for certain surgical procedures such as deep brain stimulation, which has been adopted for the treatment of a variety of neurological disorders. During the procedure, auditory analysis of these signals helps the clinical team to infer the neuroanatomical location of the stimulation electrode and thus optimize clinical outcomes. This task is complex, and requires an expert who in turn requires significant training. In this paper, we propose a generative neural network, called MerGen, capable of simulating de novo electrophysiological recordings, with a view to providing a realistic learning tool for clinicians trainees for identifying these signals. We demonstrate that the generated signals are perceptually indistinguishable from real signals by experts in the field, and that it is even possible to condition the generation efficiently to provide a didactic simulator adapted to a particular surgical scenario. The efficacy of this conditioning is demonstrated, comparing it to intra-observer and inter-observer variability amongst experts. We also demonstrate the use of this network for data augmentation for automatic signal classification which can play a role in decision-making support in the operating theatre. |
| title | MerGen: Micro-electrode recording synthesis using a generative data-driven approach |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2503.16928 |