Detection of Movement and Lead-Popping Artifacts in Polysomnography EEG Data
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 powe...
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MDPI AG
2024-10-01
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| Online Access: | https://www.mdpi.com/2624-6120/5/4/38 |
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| author | Nishanth Anandanadarajah Amlan Talukder Deryck Yeung Yuanyuan Li David M. Umbach Zheng Fan Leping Li |
| author_facet | Nishanth Anandanadarajah Amlan Talukder Deryck Yeung Yuanyuan Li David M. Umbach Zheng Fan Leping Li |
| author_sort | Nishanth Anandanadarajah |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-b7abe076019747a8adfbeb9828b659ad |
| institution | DOAJ |
| issn | 2624-6120 |
| language | English |
| publishDate | 2024-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Signals |
| spelling | doaj-art-b7abe076019747a8adfbeb9828b659ad2025-08-20T02:43:54ZengMDPI AGSignals2624-61202024-10-015469070410.3390/signals5040038Detection of Movement and Lead-Popping Artifacts in Polysomnography EEG DataNishanth Anandanadarajah0Amlan Talukder1Deryck Yeung2Yuanyuan Li3David M. Umbach4Zheng Fan5Leping Li6Biostatistics and Computational Biology Branch, National Institute of Environmental Health Sciences, Research Triangle Park, NC 27709, USABiostatistics and Computational Biology Branch, National Institute of Environmental Health Sciences, Research Triangle Park, NC 27709, USABiostatistics and Computational Biology Branch, National Institute of Environmental Health Sciences, Research Triangle Park, NC 27709, USABiostatistics and Computational Biology Branch, National Institute of Environmental Health Sciences, Research Triangle Park, NC 27709, USABiostatistics and Computational Biology Branch, National Institute of Environmental Health Sciences, Research Triangle Park, NC 27709, USADivision of Sleep Medicine, Department of Neurology, University of North Carolina at Chapel Hill, Chapel Hill, NC 27514, USABiostatistics and Computational Biology Branch, National Institute of Environmental Health Sciences, Research Triangle Park, NC 27709, USAPolysomnography (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.https://www.mdpi.com/2624-6120/5/4/38artifactEEGpolysomnographycorrelationmovementlead popping |
| spellingShingle | Nishanth Anandanadarajah Amlan Talukder Deryck Yeung Yuanyuan Li David M. Umbach Zheng Fan Leping Li Detection of Movement and Lead-Popping Artifacts in Polysomnography EEG Data Signals artifact EEG polysomnography correlation movement lead popping |
| title | Detection of Movement and Lead-Popping Artifacts in Polysomnography EEG Data |
| title_full | Detection of Movement and Lead-Popping Artifacts in Polysomnography EEG Data |
| title_fullStr | Detection of Movement and Lead-Popping Artifacts in Polysomnography EEG Data |
| title_full_unstemmed | Detection of Movement and Lead-Popping Artifacts in Polysomnography EEG Data |
| title_short | Detection of Movement and Lead-Popping Artifacts in Polysomnography EEG Data |
| title_sort | detection of movement and lead popping artifacts in polysomnography eeg data |
| topic | artifact EEG polysomnography correlation movement lead popping |
| url | https://www.mdpi.com/2624-6120/5/4/38 |
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