Quantifying the Suitability of Biosignals Acquired During Surgery for Multimodal Analysis
<italic>Goal:</italic> Recently, large datasets of biosignals acquired during surgery became available. As they offer multiple physiological signals measured in parallel, multimodal analysis – which involves their joint analysis – can be conducted and could provide...
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IEEE
2024-01-01
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| Series: | IEEE Open Journal of Engineering in Medicine and Biology |
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| Online Access: | https://ieeexplore.ieee.org/document/10476670/ |
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| author | Ennio Idrobo-Avila Gergo Bognar Dagmar Krefting Thomas Penzel Peter Kovacs Nicolai Spicher |
| author_facet | Ennio Idrobo-Avila Gergo Bognar Dagmar Krefting Thomas Penzel Peter Kovacs Nicolai Spicher |
| author_sort | Ennio Idrobo-Avila |
| collection | DOAJ |
| description | <italic>Goal:</italic> Recently, large datasets of biosignals acquired during surgery became available. As they offer multiple physiological signals measured in parallel, multimodal analysis – which involves their joint analysis – can be conducted and could provide deeper insights than unimodal analysis based on a single signal. However, it is unclear what percentage of intraoperatively acquired data is suitable for multimodal analysis. Due to the large amount of data, manual inspection and labelling into suitable and unsuitable segments are not feasible. Nevertheless, multimodal analysis is performed successfully in sleep studies since many years as their signals have proven suitable. Hence, this study evaluates the suitability to perform multimodal analysis on a surgery dataset (VitalDB) using a multi-center sleep dataset (SIESTA) as reference. <italic>Methods:</italic> We applied widely known algorithms entitled “signal quality indicators” to the common biosignals in both datasets, namely electrocardiography, electroencephalography, and respiratory signals split in segments of 10 s duration. As there are no multimodal methods available, we used only unimodal signal quality indicators. In case, all three signals were determined as being adequate by the indicators, we assumed that the whole signal segment was suitable for multimodal analysis. <italic>Results:</italic> 82% of SIESTA and 72% of VitalDB are suitable for multimodal analysis. Unsuitable signal segments exhibit constant or physiologically unreasonable values. Histogram examination indicated similar signal quality distributions between the datasets, albeit with potential statistical biases due to different measurement setups. <italic>Conclusions:</italic> The majority of data within VitalDB is suitable for multimodal analysis. |
| format | Article |
| id | doaj-art-96496275904c4bf7a45cf90e2edfeed7 |
| institution | Kabale University |
| issn | 2644-1276 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Open Journal of Engineering in Medicine and Biology |
| spelling | doaj-art-96496275904c4bf7a45cf90e2edfeed72025-01-30T00:03:32ZengIEEEIEEE Open Journal of Engineering in Medicine and Biology2644-12762024-01-01525026010.1109/OJEMB.2024.337973310476670Quantifying the Suitability of Biosignals Acquired During Surgery for Multimodal AnalysisEnnio Idrobo-Avila0https://orcid.org/0000-0003-2014-0994Gergo Bognar1https://orcid.org/0000-0001-7818-5760Dagmar Krefting2https://orcid.org/0000-0002-7238-5339Thomas Penzel3https://orcid.org/0000-0002-4304-0112Peter Kovacs4https://orcid.org/0000-0002-0772-9721Nicolai Spicher5https://orcid.org/0000-0002-2879-9948Department of Medical Informatics, University Medical Center Göttingen, Georg-August-Universität, Göttingen, GermanyDepartment of Numerical Analysis, Faculty of Informatics, Eötvös Loránd University, Budapest, HungaryDepartment of Medical Informatics, University Medical Center Göttingen, Georg-August-Universität, Göttingen, GermanyInterdisciplinary Center of Sleep Medicine, Charité - Universitätsmedizin Berlin, Berlin, GermanyDepartment of Numerical Analysis, Faculty of Informatics, Eötvös Loránd University, Budapest, HungaryDepartment of Medical Informatics, University Medical Center Göttingen, Georg-August-Universität, Göttingen, Germany<italic>Goal:</italic> Recently, large datasets of biosignals acquired during surgery became available. As they offer multiple physiological signals measured in parallel, multimodal analysis – which involves their joint analysis – can be conducted and could provide deeper insights than unimodal analysis based on a single signal. However, it is unclear what percentage of intraoperatively acquired data is suitable for multimodal analysis. Due to the large amount of data, manual inspection and labelling into suitable and unsuitable segments are not feasible. Nevertheless, multimodal analysis is performed successfully in sleep studies since many years as their signals have proven suitable. Hence, this study evaluates the suitability to perform multimodal analysis on a surgery dataset (VitalDB) using a multi-center sleep dataset (SIESTA) as reference. <italic>Methods:</italic> We applied widely known algorithms entitled “signal quality indicators” to the common biosignals in both datasets, namely electrocardiography, electroencephalography, and respiratory signals split in segments of 10 s duration. As there are no multimodal methods available, we used only unimodal signal quality indicators. In case, all three signals were determined as being adequate by the indicators, we assumed that the whole signal segment was suitable for multimodal analysis. <italic>Results:</italic> 82% of SIESTA and 72% of VitalDB are suitable for multimodal analysis. Unsuitable signal segments exhibit constant or physiologically unreasonable values. Histogram examination indicated similar signal quality distributions between the datasets, albeit with potential statistical biases due to different measurement setups. <italic>Conclusions:</italic> The majority of data within VitalDB is suitable for multimodal analysis.https://ieeexplore.ieee.org/document/10476670/Signal qualityphysiological signalsVitalDB datasetSIESTA datasetmultimodal analysis |
| spellingShingle | Ennio Idrobo-Avila Gergo Bognar Dagmar Krefting Thomas Penzel Peter Kovacs Nicolai Spicher Quantifying the Suitability of Biosignals Acquired During Surgery for Multimodal Analysis IEEE Open Journal of Engineering in Medicine and Biology Signal quality physiological signals VitalDB dataset SIESTA dataset multimodal analysis |
| title | Quantifying the Suitability of Biosignals Acquired During Surgery for Multimodal Analysis |
| title_full | Quantifying the Suitability of Biosignals Acquired During Surgery for Multimodal Analysis |
| title_fullStr | Quantifying the Suitability of Biosignals Acquired During Surgery for Multimodal Analysis |
| title_full_unstemmed | Quantifying the Suitability of Biosignals Acquired During Surgery for Multimodal Analysis |
| title_short | Quantifying the Suitability of Biosignals Acquired During Surgery for Multimodal Analysis |
| title_sort | quantifying the suitability of biosignals acquired during surgery for multimodal analysis |
| topic | Signal quality physiological signals VitalDB dataset SIESTA dataset multimodal analysis |
| url | https://ieeexplore.ieee.org/document/10476670/ |
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