{"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/multichannel-sleep-spindle-detection-using","title":"Multichannel sleep spindle detection using sparse low-rank optimization","arxiv_id":null,"date":"2017-08-15","proceeding":"Journal of Neuroscience Methods Volume 288 2017 8","authors":["Ankit Parekha","Ivan W. Selesnick","Ricardo S.Osorio","Andrew W. Vargad","David M. Rapoport","Indu Ayappa"],"abstract":"BACKGROUND:\r\nAutomated single-channel spindle detectors, for human sleep EEG, are blind to the presence of spindles in other recorded channels unlike visual annotation by a human expert.\r\n\r\nNEW METHOD:\r\nWe propose a multichannel spindle detection method that aims to detect global and local spindle activity in human sleep EEG. Using a non-linear signal model, which assumes the input EEG to be the sum of a transient and an oscillatory component, we propose a multichannel transient separation algorithm. Consecutive overlapping blocks of the multichannel oscillatory component are assumed to be low-rank whereas the transient component is assumed to be piecewise constant with a zero baseline. The estimated oscillatory component is used in conjunction with a bandpass filter and the Teager operator for detecting sleep spindles.\r\n\r\nRESULTS AND COMPARISON WITH OTHER METHODS:\r\nThe proposed method is applied to two publicly available databases and compared with 7 existing single-channel automated detectors. F1 scores for the proposed spindle detection method averaged 0.66 (0.02) and 0.62 (0.06) for the two databases, respectively. For an overnight 6 channel EEG signal, the proposed algorithm takes about 4min to detect sleep spindles simultaneously across all channels with a single setting of corresponding algorithmic parameters.\r\n\r\nCONCLUSIONS:\r\nThe proposed method attempts to mimic and utilize, for better spindle detection, a particular human expert behavior where the decision to mark a spindle event may be subconsciously influenced by the presence of a spindle in EEG channels other than the central channel visible on a digital screen.","url_abs":"https://doi.org/10.1016/j.jneumeth.2017.06.004","url_pdf":"https://www.researchgate.net/publication/317533757_Multichannel_Sleep_Spindle_Detection_using_Sparse_Low-Rank_Optimization","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":[{"paper_slug":"multichannel-sleep-spindle-detection-using","repo_url":"https://github.com/aparek/mcsleep","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"eeg-1","task_name":"EEG"},{"task_slug":"eeg","task_name":"Electroencephalogram (EEG)"},{"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":"Multichannel Low-Rank","rank_in_archive_order":4,"of":6,"metrics":{"F1-score (@IoU = 0.3)":"0.50"},"uses_additional_data":false}],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}