Enhancing EEG data quality and precision for cloud-based clinical applications: an evaluation of the SLOG framework
Automation is revamping our preprocessing pipelines, and accelerating the delivery of personalized digital medicine. It improves efficiency, reduces costs, and allows clinicians to treat patients without significant delays. However, the influx of multimodal data highlights the need to protect sensit...
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Veröffentlicht in: | Biomedical physics & engineering express 2024-10, Vol.10 (6), p.67001 |
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description | Automation is revamping our preprocessing pipelines, and accelerating the delivery of personalized digital medicine. It improves efficiency, reduces costs, and allows clinicians to treat patients without significant delays. However, the influx of multimodal data highlights the need to protect sensitive information, such as clinical data, and safeguard data fidelity. One of the neuroimaging modalities that produces large amounts of time-series data is Electroencephalography (EEG). It captures the neural dynamics in a task or resting brain state with high temporal resolution. EEG electrodes placed on the scalp acquire electrical activity from the brain. These electrical potentials attenuate as they cross multiple layers of brain tissue and fluid yielding relatively weaker signals than noise-low signal-to-noise ratio. EEG signals are further distorted by internal physiological artifacts, such as eye movements (EOG) or heartbeat (ECG), and external noise, such as line noise (50 Hz). EOG artifacts, due to their proximity to the frontal brain regions, are particularly challenging to eliminate. Therefore, a widely used EOG rejection method, independent component analysis (ICA), demands manual inspection of the marked EOG components before they are rejected from the EEG data. We underscore the inaccuracy of automatized ICA rejection and provide an auxiliary algorithm-Second Layer Inspection for EOG (SLOG) in the clinical environment. SLOG based on spatial and temporal patterns of eye movements, re-examines the already marked EOG artifacts and confirms no EEG-related activity is mistakenly eliminated in this artifact rejection step. SLOG achieved a 99% precision rate on the simulated dataset while 85% precision on the real EEG dataset. One of the primary considerations for cloud-based applications is operational costs, including computing power. Algorithms like SLOG allow us to maintain data fidelity and precision without overloading the cloud platforms and maxing out our budgets. |
doi_str_mv | 10.1088/2057-1976/ad7e2d |
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Therefore, a widely used EOG rejection method, independent component analysis (ICA), demands manual inspection of the marked EOG components before they are rejected from the EEG data. We underscore the inaccuracy of automatized ICA rejection and provide an auxiliary algorithm-Second Layer Inspection for EOG (SLOG) in the clinical environment. SLOG based on spatial and temporal patterns of eye movements, re-examines the already marked EOG artifacts and confirms no EEG-related activity is mistakenly eliminated in this artifact rejection step. SLOG achieved a 99% precision rate on the simulated dataset while 85% precision on the real EEG dataset. One of the primary considerations for cloud-based applications is operational costs, including computing power. 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Phys. Eng. Express</addtitle><description>Automation is revamping our preprocessing pipelines, and accelerating the delivery of personalized digital medicine. It improves efficiency, reduces costs, and allows clinicians to treat patients without significant delays. However, the influx of multimodal data highlights the need to protect sensitive information, such as clinical data, and safeguard data fidelity. One of the neuroimaging modalities that produces large amounts of time-series data is Electroencephalography (EEG). It captures the neural dynamics in a task or resting brain state with high temporal resolution. EEG electrodes placed on the scalp acquire electrical activity from the brain. These electrical potentials attenuate as they cross multiple layers of brain tissue and fluid yielding relatively weaker signals than noise-low signal-to-noise ratio. EEG signals are further distorted by internal physiological artifacts, such as eye movements (EOG) or heartbeat (ECG), and external noise, such as line noise (50 Hz). EOG artifacts, due to their proximity to the frontal brain regions, are particularly challenging to eliminate. Therefore, a widely used EOG rejection method, independent component analysis (ICA), demands manual inspection of the marked EOG components before they are rejected from the EEG data. We underscore the inaccuracy of automatized ICA rejection and provide an auxiliary algorithm-Second Layer Inspection for EOG (SLOG) in the clinical environment. SLOG based on spatial and temporal patterns of eye movements, re-examines the already marked EOG artifacts and confirms no EEG-related activity is mistakenly eliminated in this artifact rejection step. SLOG achieved a 99% precision rate on the simulated dataset while 85% precision on the real EEG dataset. 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Algorithms like SLOG allow us to maintain data fidelity and precision without overloading the cloud platforms and maxing out our budgets.</description><subject>Algorithms</subject><subject>Brain - diagnostic imaging</subject><subject>Brain - physiology</subject><subject>Cloud Computing</subject><subject>Data Accuracy</subject><subject>digital medicine</subject><subject>EEG artifact rejection</subject><subject>EEG data fidelity</subject><subject>Electroencephalography - methods</subject><subject>Electrooculography - methods</subject><subject>Eye Movements - physiology</subject><subject>Humans</subject><subject>independent component analysis ICA</subject><subject>signal processing</subject><subject>Signal Processing, Computer-Assisted</subject><subject>Signal-To-Noise Ratio</subject><issn>2057-1976</issn><issn>2057-1976</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2024</creationdate><recordtype>article</recordtype><sourceid>O3W</sourceid><sourceid>EIF</sourceid><recordid>eNp1kM9PwyAAhYnRuGXu7slw9GAdP9ZSvJllTpMlHtQzoUAdsy0dtJr99zI3Fy-eeJDvvYQPgEuMbjHK8wlBKUswZ9lEamaIPgHD49PpnzwA4xDWCCGckSzj6TkYUE5xOmV8CMK8WclG2eYdzucLqGUn4aaXle22UDYatt4oG6xrYOk8VJXrdVLIYHTMtrFKVlC2bRVDF6FwF0vQfMqq_7lDV8JuZeDL8nkBSy9r8-X8xwU4K2UVzPhwjsDbw_x19phE6ml2v0wUwbxLKMWao0LhnBda49yUhVYFRtxkiqhMs4JRnROZU81ZWRCsiOaYUJVypQnjdASu97utd5vehE7UNihTVbIxrg-CRo0smxKKIor2qPIuBG9K0XpbS78VGImdbbHTKXY6xd52rFwd1vuiNvpY-HUbgZs9YF0r1q73Tfzs_3vfyPSKCg</recordid><startdate>20241004</startdate><enddate>20241004</enddate><creator>Ghani, Amna</creator><creator>Heinrich, Hartmut</creator><creator>Brown, Trevor</creator><creator>Schellhorn, Klaus</creator><general>IOP Publishing</general><scope>O3W</scope><scope>TSCCA</scope><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7X8</scope><orcidid>https://orcid.org/0000-0002-8665-689X</orcidid></search><sort><creationdate>20241004</creationdate><title>Enhancing EEG data quality and precision for cloud-based clinical applications: an evaluation of the SLOG framework</title><author>Ghani, Amna ; Heinrich, Hartmut ; Brown, Trevor ; Schellhorn, Klaus</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c219t-331d90bc189bdd18efbdcb109e6c2c6d7b73d82a83d97fb21c2d9123c59cd2793</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2024</creationdate><topic>Algorithms</topic><topic>Brain - diagnostic imaging</topic><topic>Brain - physiology</topic><topic>Cloud Computing</topic><topic>Data Accuracy</topic><topic>digital medicine</topic><topic>EEG artifact rejection</topic><topic>EEG data fidelity</topic><topic>Electroencephalography - methods</topic><topic>Electrooculography - methods</topic><topic>Eye Movements - physiology</topic><topic>Humans</topic><topic>independent component analysis ICA</topic><topic>signal processing</topic><topic>Signal Processing, Computer-Assisted</topic><topic>Signal-To-Noise Ratio</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Ghani, Amna</creatorcontrib><creatorcontrib>Heinrich, Hartmut</creatorcontrib><creatorcontrib>Brown, Trevor</creatorcontrib><creatorcontrib>Schellhorn, Klaus</creatorcontrib><collection>Institute of Physics Open Access Journal Titles</collection><collection>IOPscience (Open Access)</collection><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>MEDLINE - Academic</collection><jtitle>Biomedical physics & engineering express</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Ghani, Amna</au><au>Heinrich, Hartmut</au><au>Brown, Trevor</au><au>Schellhorn, Klaus</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Enhancing EEG data quality and precision for cloud-based clinical applications: an evaluation of the SLOG framework</atitle><jtitle>Biomedical physics & engineering express</jtitle><stitle>BPEX</stitle><addtitle>Biomed. Phys. Eng. Express</addtitle><date>2024-10-04</date><risdate>2024</risdate><volume>10</volume><issue>6</issue><spage>67001</spage><pages>67001-</pages><issn>2057-1976</issn><eissn>2057-1976</eissn><abstract>Automation is revamping our preprocessing pipelines, and accelerating the delivery of personalized digital medicine. It improves efficiency, reduces costs, and allows clinicians to treat patients without significant delays. However, the influx of multimodal data highlights the need to protect sensitive information, such as clinical data, and safeguard data fidelity. One of the neuroimaging modalities that produces large amounts of time-series data is Electroencephalography (EEG). It captures the neural dynamics in a task or resting brain state with high temporal resolution. EEG electrodes placed on the scalp acquire electrical activity from the brain. 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subjects | Algorithms Brain - diagnostic imaging Brain - physiology Cloud Computing Data Accuracy digital medicine EEG artifact rejection EEG data fidelity Electroencephalography - methods Electrooculography - methods Eye Movements - physiology Humans independent component analysis ICA signal processing Signal Processing, Computer-Assisted Signal-To-Noise Ratio |
title | Enhancing EEG data quality and precision for cloud-based clinical applications: an evaluation of the SLOG framework |
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