{"doi":"10.17615/6ea1-w676","title":"Detection of Movement and Lead-Popping Artifacts in Polysomnography EEG Data","abstract":"Polysomnography (PSG) measures brain activity during sleep via electroencephalography (EEG) using six leads. Artifacts caused by movement or loose leads distort EEG measurements. We developed a method to automatically identify such artifacts in a PSG EEG trace. After preprocessing, we extracted power levels at frequencies of 0.5–32.5 Hz with multitaper spectral analysis using 4 s windows with 3 s overlap. For each resulting 1 s segment, we computed segment-specific correlations between power levels for all pairs of leads. We then averaged all pairwise correlation coefficients involving each lead, creating a time series of segment-specific average correlations for each lead. Our algorithm scans each averaged time series separately for “bad” segments using a local moving window. In a second pass, any segment whose averaged correlation is less than a global threshold among all remaining good segments is declared an outlier. We mark all segments between two outlier segments fewer than 300 s apart as artifact regions. This process is repeated, removing a channel with excessive outliers in each iteration. We compared artifact regions discovered by our algorithm to expert-assessed ground truth, achieving sensitivity and specificity of 80% and 91%, respectively. Our algorithm is an open-source tool, either as a Python package or a Docker.","journal":"UNC Libraries","year":2024,"id":505714,"datarank":0.0,"base_score":0.0,"endowment":0.0,"self_citation_contribution":0.0,"citation_network_contribution":0.0,"self_endowment_contribution":0.0,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":0,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.944,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2024-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":63127,"name":"Leping Li","orcid":"0000-0003-4208-0259","position":1,"is_corresponding":false},{"id":326055,"name":"Amlan Talukder","orcid":"0000-0001-7457-8268","position":2,"is_corresponding":false},{"id":1311932,"name":"Nishanth Anandanadarajah","orcid":"0000-0001-6916-3180","position":3,"is_corresponding":false},{"id":1142667,"name":"Deryck Yeung","orcid":null,"position":4,"is_corresponding":false},{"id":697680,"name":"Yuanyuan Li","orcid":"0000-0002-9311-9961","position":5,"is_corresponding":false},{"id":594639,"name":"Zheng Fan","orcid":"0000-0001-7786-5465","position":6,"is_corresponding":false},{"id":292086,"name":"David M. Umbach","orcid":"0000-0002-7142-2812","position":0,"is_corresponding":true}],"reference_count":0,"raw_metadata":{"citation_network_status":"fetched"},"created_at":"2026-07-19T02:10:47.170978Z","pmid":null,"pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}