{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/a-single-channel-sleep-spindle-detector-based","title":"A single channel sleep-spindle detector based on multivariate classification of EEG epochs: MUSSDET.","arxiv_id":null,"date":"2018-03-01","proceeding":"Journal of Neuroscience Methods Volume 297 2018 3","authors":["DanielLachner-Piza","Nino Epitashvili","Andreas Schulze-Bonhage","Thomas Stieglitz","Julia Jacobs","Matthias Dümpelmann"],"abstract":"BACKGROUND:\r\nStudies on sleep-spindles are typically based on visual-marks performed by experts, however this process is time consuming and presents a low inter-expert agreement, causing the data to be limited in quantity and prone to bias. An automatic detector would tackle these issues by generating large amounts of objectively marked data.\r\n\r\nNEW METHOD:\r\nOur goal was to develop a sensitive, precise and robust sleep-spindle detection method. Emphasis has been placed on achieving a consistent performance across heterogeneous recordings and without the need for further parameter fine tuning. The developed detector runs on a single channel and is based on multivariate classification using a support vector machine. Scalp-electroencephalogram recordings were segmented into epochs which were then characterized by a selection of relevant and non-redundant features. The training and validation data came from the Medical Center-University of Freiburg, the test data consisted of 27 records coming from 2 public databases.\r\n\r\nRESULTS:\r\nUsing a sample based assessment, 53% sensitivity, 37% precision and 96% specificity was achieved on the DREAMS database. On the MASS database, 77% sensitivity, 46% precision and 96% specificity was achieved. The developed detector performed favorably when compared to previous detectors. The classification of normalized EEG epochs in a multidimensional space, as well as the use of a validation set, allowed to objectively define a single detection threshold for all databases and participants.\r\n\r\nCONCLUSIONS:\r\nThe use of the developed tool will allow increasing the data-size and statistical significance of research studies on the role of sleep-spindles.","url_abs":"https://doi.org/10.1016/j.jneumeth.2017.12.023","url_pdf":"https://www.deepdyve.com/lp/elsevier/a-single-channel-sleep-spindle-detector-based-on-multivariate-grFKFTd9gR#bsSignUpModal","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[],"tasks":[{"task_slug":"eeg-1","task_name":"EEG"},{"task_slug":"eeg","task_name":"Electroencephalogram (EEG)"},{"task_slug":"classification","task_name":"General Classification"},{"task_slug":"sensitivity","task_name":"Sensitivity"},{"task_slug":"specificity","task_name":"Specificity"},{"task_slug":"spindle-detection","task_name":"Spindle Detection"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/spindle-detection-on-mass-ss2","task":"Spindle Detection","dataset":"MASS SS2","model":"MUSSDET","rank_in_archive_order":6,"of":6,"metrics":{"F1-score (@IoU = 0.3)":"0.39"},"uses_additional_data":false}],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}