Aron Jeremiah: Unravelling Propagation: Exploring Spinal Cord Injury Dynamics through Machine Learning
<p dir="ltr">Spinal cord injury (SCI) disrupts signal transmission within the nervous system, motivating the development of implantable interfaces for both stimulation and sensing. This work investigates the feasibility of classifying injury status using electrophysiological signals...
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2025
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| _version_ | 1852015683573907456 |
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| author | Aron Jeremiah (9644387) |
| author2 | Darren Svirskis (1190208) Brad Raos (1185423) Andreas Kempa-Liehr (4076275) |
| author2_role | author author author |
| author_facet | Aron Jeremiah (9644387) Darren Svirskis (1190208) Brad Raos (1185423) Andreas Kempa-Liehr (4076275) |
| author_role | author |
| dc.creator.none.fl_str_mv | Aron Jeremiah (9644387) Darren Svirskis (1190208) Brad Raos (1185423) Andreas Kempa-Liehr (4076275) |
| dc.date.none.fl_str_mv | 2025-10-19T21:10:43Z |
| dc.identifier.none.fl_str_mv | 10.17608/k6.auckland.30396103.v1 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/poster/Aron_Jeremiah_Unravelling_Propagation_Exploring_Spinal_Cord_Injury_Dynamics_through_Machine_Learning/30396103 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Machine learning not elsewhere classified Spinal cord injury (SCI) Machine learning Time-series analytics Neural implant Electrophysiology Hyperparameter optimization |
| dc.title.none.fl_str_mv | Aron Jeremiah: Unravelling Propagation: Exploring Spinal Cord Injury Dynamics through Machine Learning |
| dc.type.none.fl_str_mv | Image Poster info:eu-repo/semantics/publishedVersion image |
| description | <p dir="ltr">Spinal cord injury (SCI) disrupts signal transmission within the nervous system, motivating the development of implantable interfaces for both stimulation and sensing. This work investigates the feasibility of classifying injury status using electrophysiological signals acquired from freely moving rats with custom intraspinal neural implants. Time-series features were systematically extracted using tsfresh and evaluated for statistical relevance to injury state. Significant features were used to train a supervised LightGBM classifier, with performance assessed via leave-one-subject-out cross-validation. The model achieved an average Area Under the Curve (AUC) of 0.85, with a peak of 0.96, demonstrating reliable discrimination between healthy and injured conditions. Statistical analysis identified approximately 310 features (p < 0.001) associated with SCI, indicating measurable alterations in neural signal characteristics. These results demonstrate a data-driven framework for electrophysiological state classification and lay the groundwork for advanced diagnostic and prognostic tools in spinal injury assessment.</p> |
| eu_rights_str_mv | openAccess |
| id | Manara_d54bed2b39358f5895168dc3dc86c857 |
| identifier_str_mv | 10.17608/k6.auckland.30396103.v1 |
| network_acronym_str | Manara |
| network_name_str | ManaraRepo |
| oai_identifier_str | oai:figshare.com:article/30396103 |
| publishDate | 2025 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | Aron Jeremiah: Unravelling Propagation: Exploring Spinal Cord Injury Dynamics through Machine LearningAron Jeremiah (9644387)Darren Svirskis (1190208)Brad Raos (1185423)Andreas Kempa-Liehr (4076275)Machine learning not elsewhere classifiedSpinal cord injury (SCI)Machine learningTime-series analyticsNeural implantElectrophysiologyHyperparameter optimization<p dir="ltr">Spinal cord injury (SCI) disrupts signal transmission within the nervous system, motivating the development of implantable interfaces for both stimulation and sensing. This work investigates the feasibility of classifying injury status using electrophysiological signals acquired from freely moving rats with custom intraspinal neural implants. Time-series features were systematically extracted using tsfresh and evaluated for statistical relevance to injury state. Significant features were used to train a supervised LightGBM classifier, with performance assessed via leave-one-subject-out cross-validation. The model achieved an average Area Under the Curve (AUC) of 0.85, with a peak of 0.96, demonstrating reliable discrimination between healthy and injured conditions. Statistical analysis identified approximately 310 features (p < 0.001) associated with SCI, indicating measurable alterations in neural signal characteristics. These results demonstrate a data-driven framework for electrophysiological state classification and lay the groundwork for advanced diagnostic and prognostic tools in spinal injury assessment.</p>2025-10-19T21:10:43ZImagePosterinfo:eu-repo/semantics/publishedVersionimage10.17608/k6.auckland.30396103.v1https://figshare.com/articles/poster/Aron_Jeremiah_Unravelling_Propagation_Exploring_Spinal_Cord_Injury_Dynamics_through_Machine_Learning/30396103CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/303961032025-10-19T21:10:43Z |
| spellingShingle | Aron Jeremiah: Unravelling Propagation: Exploring Spinal Cord Injury Dynamics through Machine Learning Aron Jeremiah (9644387) Machine learning not elsewhere classified Spinal cord injury (SCI) Machine learning Time-series analytics Neural implant Electrophysiology Hyperparameter optimization |
| status_str | publishedVersion |
| title | Aron Jeremiah: Unravelling Propagation: Exploring Spinal Cord Injury Dynamics through Machine Learning |
| title_full | Aron Jeremiah: Unravelling Propagation: Exploring Spinal Cord Injury Dynamics through Machine Learning |
| title_fullStr | Aron Jeremiah: Unravelling Propagation: Exploring Spinal Cord Injury Dynamics through Machine Learning |
| title_full_unstemmed | Aron Jeremiah: Unravelling Propagation: Exploring Spinal Cord Injury Dynamics through Machine Learning |
| title_short | Aron Jeremiah: Unravelling Propagation: Exploring Spinal Cord Injury Dynamics through Machine Learning |
| title_sort | Aron Jeremiah: Unravelling Propagation: Exploring Spinal Cord Injury Dynamics through Machine Learning |
| topic | Machine learning not elsewhere classified Spinal cord injury (SCI) Machine learning Time-series analytics Neural implant Electrophysiology Hyperparameter optimization |