Impact of Temporal Resolution on Autocorrelative Features of Cerebral Physiology from Invasive and Non-Invasive Sensors in Acute Traumatic Neural Injury: Insights from the CAHR-TBI Cohort
Therapeutic management during the acute phase of traumatic brain injury (TBI) relies on continuous multimodal cerebral physiologic monitoring to detect and prevent secondary injury. These high-resolution data streams come from various invasive/non-invasive sensor technologies and challenge clinician...
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MDPI AG
2025-04-01
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| author | Nuray Vakitbilir Rahul Raj Donald E. G. Griesdale Mypinder Sekhon Francis Bernard Clare Gallagher Eric P. Thelin Logan Froese Kevin Y. Stein Andreas H. Kramer Marcel J. H. Aries Frederick A. Zeiler |
| author_facet | Nuray Vakitbilir Rahul Raj Donald E. G. Griesdale Mypinder Sekhon Francis Bernard Clare Gallagher Eric P. Thelin Logan Froese Kevin Y. Stein Andreas H. Kramer Marcel J. H. Aries Frederick A. Zeiler |
| author_sort | Nuray Vakitbilir |
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| description | Therapeutic management during the acute phase of traumatic brain injury (TBI) relies on continuous multimodal cerebral physiologic monitoring to detect and prevent secondary injury. These high-resolution data streams come from various invasive/non-invasive sensor technologies and challenge clinicians, as they are difficult to integrate into management algorithms and prognostic models. Data reduction techniques, like moving average filters, simplify data but may fail to address statistical autocorrelation and could introduce new properties, affecting model utility and interpretation. This study uses the CAnadian High-Resolution TBI (CAHR-TBI) dataset to examine the impact of temporal resolution changes (1 min to 24 h) on autoregressive integrated moving average (ARIMA) modeling for raw and derived cerebral physiologic signals. Stationarity tests indicated that the majority of the signals required first-order differencing to address persistent trends. A grid search identified optimal ARIMA parameters (<i>p</i>,<i>d</i>,<i>q</i>) for each signal and resolution. Subgroup analyses revealed population-specific differences in temporal structure, and small-scale forecasting using optimal parameters confirmed model adequacy. Variations in optimal structures across signals and patients highlight the importance of tailoring ARIMA models for precise interpretation and performance. Findings show that both raw and derived indices exhibit intrinsic ARIMA components regardless of resolution. Ignoring these features risks compromising the significance of models developed from such data. This underscores the need for careful resolution considerations in temporal modeling for TBI care. |
| format | Article |
| id | doaj-art-d2e2d7a686964d3ab874e83b226c29b1 |
| institution | OA Journals |
| issn | 1424-8220 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj-art-d2e2d7a686964d3ab874e83b226c29b12025-08-20T02:31:11ZengMDPI AGSensors1424-82202025-04-01259276210.3390/s25092762Impact of Temporal Resolution on Autocorrelative Features of Cerebral Physiology from Invasive and Non-Invasive Sensors in Acute Traumatic Neural Injury: Insights from the CAHR-TBI CohortNuray Vakitbilir0Rahul Raj1Donald E. G. Griesdale2Mypinder Sekhon3Francis Bernard4Clare Gallagher5Eric P. Thelin6Logan Froese7Kevin Y. Stein8Andreas H. Kramer9Marcel J. H. Aries10Frederick A. Zeiler11Department of Biomedical Engineering, Price Faculty of Engineering, University of Manitoba, Winnipeg, MB R3T 5V6, CanadaDepartment of Neurosurgery, University of Helsinki, 00100 Helsinki, FinlandDepartment of Anesthesiology, Pharmacology, and Therapeutics, University of British Columbia, Vancouver, BC V6T 1Z4, CanadaDivision of Critical Care, Department of Medicine, University of British Columbia, Vancouver, BC V6T 1Z4, CanadaSection of Critical Care, Department of Medicine, University of Montreal, Montreal, QC H3T 1J4, CanadaSection of Neurosurgery, University of Calgary, Calgary, AB T2N 1N4, CanadaDepartment of Neurology, Karolinska University Hospital, 171 77 Stockholm, SwedenDepartment of Neurology, Karolinska University Hospital, 171 77 Stockholm, SwedenDepartment of Biomedical Engineering, Price Faculty of Engineering, University of Manitoba, Winnipeg, MB R3T 5V6, CanadaDepartment of Clinical Neurosciences, University of Calgary, Calgary, AB T2N 1N4, CanadaDepartment of Intensive Care, Maastricht University Medical Center+, 6229 Maastricht, The NetherlandsDepartment of Biomedical Engineering, Price Faculty of Engineering, University of Manitoba, Winnipeg, MB R3T 5V6, CanadaTherapeutic management during the acute phase of traumatic brain injury (TBI) relies on continuous multimodal cerebral physiologic monitoring to detect and prevent secondary injury. These high-resolution data streams come from various invasive/non-invasive sensor technologies and challenge clinicians, as they are difficult to integrate into management algorithms and prognostic models. Data reduction techniques, like moving average filters, simplify data but may fail to address statistical autocorrelation and could introduce new properties, affecting model utility and interpretation. This study uses the CAnadian High-Resolution TBI (CAHR-TBI) dataset to examine the impact of temporal resolution changes (1 min to 24 h) on autoregressive integrated moving average (ARIMA) modeling for raw and derived cerebral physiologic signals. Stationarity tests indicated that the majority of the signals required first-order differencing to address persistent trends. A grid search identified optimal ARIMA parameters (<i>p</i>,<i>d</i>,<i>q</i>) for each signal and resolution. Subgroup analyses revealed population-specific differences in temporal structure, and small-scale forecasting using optimal parameters confirmed model adequacy. Variations in optimal structures across signals and patients highlight the importance of tailoring ARIMA models for precise interpretation and performance. Findings show that both raw and derived indices exhibit intrinsic ARIMA components regardless of resolution. Ignoring these features risks compromising the significance of models developed from such data. This underscores the need for careful resolution considerations in temporal modeling for TBI care.https://www.mdpi.com/1424-8220/25/9/2762high frequency signalsARIMAtime-series analysiscerebral physiologystationarity analysismultimodal signal analysis |
| spellingShingle | Nuray Vakitbilir Rahul Raj Donald E. G. Griesdale Mypinder Sekhon Francis Bernard Clare Gallagher Eric P. Thelin Logan Froese Kevin Y. Stein Andreas H. Kramer Marcel J. H. Aries Frederick A. Zeiler Impact of Temporal Resolution on Autocorrelative Features of Cerebral Physiology from Invasive and Non-Invasive Sensors in Acute Traumatic Neural Injury: Insights from the CAHR-TBI Cohort Sensors high frequency signals ARIMA time-series analysis cerebral physiology stationarity analysis multimodal signal analysis |
| title | Impact of Temporal Resolution on Autocorrelative Features of Cerebral Physiology from Invasive and Non-Invasive Sensors in Acute Traumatic Neural Injury: Insights from the CAHR-TBI Cohort |
| title_full | Impact of Temporal Resolution on Autocorrelative Features of Cerebral Physiology from Invasive and Non-Invasive Sensors in Acute Traumatic Neural Injury: Insights from the CAHR-TBI Cohort |
| title_fullStr | Impact of Temporal Resolution on Autocorrelative Features of Cerebral Physiology from Invasive and Non-Invasive Sensors in Acute Traumatic Neural Injury: Insights from the CAHR-TBI Cohort |
| title_full_unstemmed | Impact of Temporal Resolution on Autocorrelative Features of Cerebral Physiology from Invasive and Non-Invasive Sensors in Acute Traumatic Neural Injury: Insights from the CAHR-TBI Cohort |
| title_short | Impact of Temporal Resolution on Autocorrelative Features of Cerebral Physiology from Invasive and Non-Invasive Sensors in Acute Traumatic Neural Injury: Insights from the CAHR-TBI Cohort |
| title_sort | impact of temporal resolution on autocorrelative features of cerebral physiology from invasive and non invasive sensors in acute traumatic neural injury insights from the cahr tbi cohort |
| topic | high frequency signals ARIMA time-series analysis cerebral physiology stationarity analysis multimodal signal analysis |
| url | https://www.mdpi.com/1424-8220/25/9/2762 |
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