Data quality evaluation in wearable monitoring
Abstract Wearable recordings of neurophysiological signals captured from the wrist offer enormous potential for seizure monitoring. Yet, data quality remains one of the most challenging factors that impact data reliability. We suggest a combined data quality assessment tool for the evaluation of mul...
| Main Authors: | , , , , , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2022-12-01
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| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-022-25949-x |
| _version_ | 1811179152909271040 |
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| author | Sebastian Böttcher Solveig Vieluf Elisa Bruno Boney Joseph Nino Epitashvili Andrea Biondi Nicolas Zabler Martin Glasstetter Matthias Dümpelmann Kristof Van Laerhoven Mona Nasseri Benjamin H. Brinkman Mark P. Richardson Andreas Schulze-Bonhage Tobias Loddenkemper |
| author_facet | Sebastian Böttcher Solveig Vieluf Elisa Bruno Boney Joseph Nino Epitashvili Andrea Biondi Nicolas Zabler Martin Glasstetter Matthias Dümpelmann Kristof Van Laerhoven Mona Nasseri Benjamin H. Brinkman Mark P. Richardson Andreas Schulze-Bonhage Tobias Loddenkemper |
| author_sort | Sebastian Böttcher |
| collection | DOAJ |
| description | Abstract Wearable recordings of neurophysiological signals captured from the wrist offer enormous potential for seizure monitoring. Yet, data quality remains one of the most challenging factors that impact data reliability. We suggest a combined data quality assessment tool for the evaluation of multimodal wearable data. We analyzed data from patients with epilepsy from four epilepsy centers. Patients wore wristbands recording accelerometry, electrodermal activity, blood volume pulse, and skin temperature. We calculated data completeness and assessed the time the device was worn (on-body), and modality-specific signal quality scores. We included 37,166 h from 632 patients in the inpatient and 90,776 h from 39 patients in the outpatient setting. All modalities were affected by artifacts. Data loss was higher when using data streaming (up to 49% among inpatient cohorts, averaged across respective recordings) as compared to onboard device recording and storage (up to 9%). On-body scores, estimating the percentage of time a device was worn on the body, were consistently high across cohorts (more than 80%). Signal quality of some modalities, based on established indices, was higher at night than during the day. A uniformly reported data quality and multimodal signal quality index is feasible, makes study results more comparable, and contributes to the development of devices and evaluation routines necessary for seizure monitoring. |
| first_indexed | 2024-04-11T06:31:05Z |
| format | Article |
| id | doaj.art-d4d2f367dc3f422ba120b1c039f07516 |
| institution | Directory Open Access Journal |
| issn | 2045-2322 |
| language | English |
| last_indexed | 2024-04-11T06:31:05Z |
| publishDate | 2022-12-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj.art-d4d2f367dc3f422ba120b1c039f075162022-12-22T04:40:06ZengNature PortfolioScientific Reports2045-23222022-12-0112111610.1038/s41598-022-25949-xData quality evaluation in wearable monitoringSebastian Böttcher0Solveig Vieluf1Elisa Bruno2Boney Joseph3Nino Epitashvili4Andrea Biondi5Nicolas Zabler6Martin Glasstetter7Matthias Dümpelmann8Kristof Van Laerhoven9Mona Nasseri10Benjamin H. Brinkman11Mark P. Richardson12Andreas Schulze-Bonhage13Tobias Loddenkemper14Department of Neurosurgery, Epilepsy Center, Medical Center – University of FreiburgDivision of Epilepsy and Clinical Neurophysiology, Boston Children’s Hospital, Harvard Medical SchoolDepartment of Basic and Clinical Neuroscience, Institute of Psychiatry, Psychology and Neuroscience, King’s CollegeBioelectronics Neurophysiology and Engineering Laboratory, Department of Neurology, Mayo ClinicDepartment of Neurosurgery, Epilepsy Center, Medical Center – University of FreiburgDepartment of Basic and Clinical Neuroscience, Institute of Psychiatry, Psychology and Neuroscience, King’s CollegeDepartment of Neurosurgery, Epilepsy Center, Medical Center – University of FreiburgDepartment of Neurosurgery, Epilepsy Center, Medical Center – University of FreiburgDepartment of Neurosurgery, Epilepsy Center, Medical Center – University of FreiburgUbiquitous Computing, Department of Electrical Engineering and Computer Science, University of SiegenBioelectronics Neurophysiology and Engineering Laboratory, Department of Neurology, Mayo ClinicBioelectronics Neurophysiology and Engineering Laboratory, Department of Neurology, Mayo ClinicDepartment of Basic and Clinical Neuroscience, Institute of Psychiatry, Psychology and Neuroscience, King’s CollegeDepartment of Neurosurgery, Epilepsy Center, Medical Center – University of FreiburgDivision of Epilepsy and Clinical Neurophysiology, Boston Children’s Hospital, Harvard Medical SchoolAbstract Wearable recordings of neurophysiological signals captured from the wrist offer enormous potential for seizure monitoring. Yet, data quality remains one of the most challenging factors that impact data reliability. We suggest a combined data quality assessment tool for the evaluation of multimodal wearable data. We analyzed data from patients with epilepsy from four epilepsy centers. Patients wore wristbands recording accelerometry, electrodermal activity, blood volume pulse, and skin temperature. We calculated data completeness and assessed the time the device was worn (on-body), and modality-specific signal quality scores. We included 37,166 h from 632 patients in the inpatient and 90,776 h from 39 patients in the outpatient setting. All modalities were affected by artifacts. Data loss was higher when using data streaming (up to 49% among inpatient cohorts, averaged across respective recordings) as compared to onboard device recording and storage (up to 9%). On-body scores, estimating the percentage of time a device was worn on the body, were consistently high across cohorts (more than 80%). Signal quality of some modalities, based on established indices, was higher at night than during the day. A uniformly reported data quality and multimodal signal quality index is feasible, makes study results more comparable, and contributes to the development of devices and evaluation routines necessary for seizure monitoring.https://doi.org/10.1038/s41598-022-25949-x |
| spellingShingle | Sebastian Böttcher Solveig Vieluf Elisa Bruno Boney Joseph Nino Epitashvili Andrea Biondi Nicolas Zabler Martin Glasstetter Matthias Dümpelmann Kristof Van Laerhoven Mona Nasseri Benjamin H. Brinkman Mark P. Richardson Andreas Schulze-Bonhage Tobias Loddenkemper Data quality evaluation in wearable monitoring Scientific Reports |
| title | Data quality evaluation in wearable monitoring |
| title_full | Data quality evaluation in wearable monitoring |
| title_fullStr | Data quality evaluation in wearable monitoring |
| title_full_unstemmed | Data quality evaluation in wearable monitoring |
| title_short | Data quality evaluation in wearable monitoring |
| title_sort | data quality evaluation in wearable monitoring |
| url | https://doi.org/10.1038/s41598-022-25949-x |
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