Physiological measurement最新文献

{"title":"Noise-based correction for electrical impedance tomography.","authors":"Kai Mason, Florencia Maurino-Alperovich, David Holder, Kirill Aristovich","doi":"10.1088/1361-6579/ad4e93","DOIUrl":"10.1088/1361-6579/ad4e93","url":null,"abstract":"<p><p><i>Objective.</i>Noisy measurements frequently cause noisy and inaccurate images in impedance imaging. No post-processing technique exists to calculate the propagation of measurement noise and use this to suppress noise in the image. The objectives of this work were (1) to develop a post-processing method for noise-based correction (NBC) in impedance tomography, (2) to test whether NBC improves image quality in electrical impedance tomography (EIT), (3) to determine whether it is preferable to use correlated or uncorrelated noise for NBC, (4) to test whether NBC works with<i>in vivo</i>data and (5) to test whether NBC is stable across model and perturbation geometries.<i>Approach.</i>EIT was performed<i>in silico</i>in a 2D homogeneous circular domain and an anatomically realistic, heterogeneous 3D human head domain for four perturbations and 25 noise levels in each case. This was validated by performing EIT for four perturbations in a circular, saline tank in 2D as well as a human head-shaped saline tank with a realistic skull-like layer in 3D. Images were assessed on the error in the weighted spatial variance (WSV) with respect to the true, target image. The effect of NBC was also tested for<i>in vivo</i>EIT data of lung ventilation in a human thorax and cortical activity in a rat brain.<i>Main results.</i>On visual inspection, NBC maintained or increased image quality for all perturbations and noise levels in 2D and 3D, both experimentally and<i>in silico</i>. Analysis of the WSV showed that NBC significantly improved the WSV in nearly all cases. When the WSV was inferior with NBC, this was either visually imperceptible or a transformation between noisy reconstructions. For<i>in vivo</i>data, NBC improved image quality in all cases and preserved the expected shape of the reconstructed perturbation.<i>Significance.</i>In practice, uncorrelated NBC performed better than correlated NBC and is recommended as a general-use post-processing technique in EIT.</p>","PeriodicalId":20047,"journal":{"name":"Physiological measurement","volume":null,"pages":null},"PeriodicalIF":3.2,"publicationDate":"2024-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141076364","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"LDSG-Net: an efficient lightweight convolutional neural network for acute hypotensive episode prediction during ICU hospitalization.","authors":"Longfei Liu, Yujie Hang, Rongqin Chen, Xianliang He, Xingliang Jin, Dan Wu, Ye Li","doi":"10.1088/1361-6579/ad4e92","DOIUrl":"10.1088/1361-6579/ad4e92","url":null,"abstract":"<p><p><i>Objective</i>. Acute hypotension episode (AHE) is one of the most critical complications in intensive care unit (ICU). A timely and precise AHE prediction system can provide clinicians with sufficient time to respond with proper therapeutic measures, playing a crucial role in saving patients' lives. Recent studies have focused on utilizing more complex models to improve predictive performance. However, these models are not suitable for clinical application due to limited computing resources for bedside monitors.<i>Approach</i>. To address this challenge, we propose an efficient lightweight dilated shuffle group network. It effectively incorporates shuffling operations into grouped convolutions on the channel and dilated convolutions on the temporal dimension, enhancing global and local feature extraction while reducing computational load.<i>Main results</i>. Our benchmarking experiments on the MIMIC-III and VitalDB datasets, comprising 6036 samples from 1304 patients and 2958 samples from 1047 patients, respectively, demonstrate that our model outperforms other state-of-the-art lightweight CNNs in terms of balancing parameters and computational complexity. Additionally, we discovered that the utilization of multiple physiological signals significantly improves the performance of AHE prediction. External validation on the MIMIC-IV dataset confirmed our findings, with prediction accuracy for AHE 5 min prior reaching 93.04% and 92.04% on the MIMIC-III and VitalDB datasets, respectively, and 89.47% in external verification.<i>Significance</i>. Our study demonstrates the potential of lightweight CNN architectures in clinical applications, providing a promising solution for real-time AHE prediction under resource constraints in ICU settings, thereby marking a significant step forward in improving patient care.</p>","PeriodicalId":20047,"journal":{"name":"Physiological measurement","volume":null,"pages":null},"PeriodicalIF":3.2,"publicationDate":"2024-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141076308","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Developing technologies to assess vascular ageing: a roadmap from VascAgeNet.","authors":"Serena Zanelli, Davide Agnoletti, Jordi Alastruey, John Allen, Elisabetta Bianchini, Vasiliki Bikia, Pierre Boutouyrie, Rosa Maria Bruno, Rachel Climie, Djeldjli Djamaleddine, Eugenia Gkaliagkousi, Alessandro Giudici, Kristina Gopcevic, Andrea Grillo, Andrea Guala, Bernhard Hametner, Jayaraj Joseph, Parmis Karimpour, Vimarsha Kodithuwakku, Panayiotis A Kyriacou, Antonios Lazaridis, Mai Tone Lonnebakken, Maria Raffaella Martina, Christopher Clemens Mayer, P M Nabeel, Petras Navickas, Janos Nemcsik, Stefan Orter, Chloe Park, Telmo Pereira, Giacomo Pucci, Ana Belen Amado Rey, Paolo Salvi, Ana Carolina Gonçalves Seabra, Ute Seeland, Thomas van Sloten, Bart Spronck, Gerard Stansby, Indra Steens, Thomas Stieglitz, Isabella Tan, Dave Veerasingam, Siegfried Wassertheurer, Thomas Weber, Berend E Westerhof, Peter H Charlton","doi":"10.1088/1361-6579/ad548e","DOIUrl":"https://doi.org/10.1088/1361-6579/ad548e","url":null,"abstract":"<p><p>Vascular ageing is the deterioration of arterial structure and function which occurs naturally with age, and which can be accelerated with disease. Measurements of vascular ageing are emerging as markers of cardiovascular risk, with potential applications in disease diagnosis and prognosis, and for guiding treatments. However, vascular ageing is not yet routinely assessed in clinical practice. A key step towards this is the development of technologies to assess vascular ageing. In this Roadmap, experts discuss several aspects of this process, including: measurement technologies; the development pipeline; clinical applications; and future research directions. The Roadmap summarises the state of the art, outlines the major challenges to overcome, and identifies potential future research directions to address these challenges.</p>","PeriodicalId":20047,"journal":{"name":"Physiological measurement","volume":null,"pages":null},"PeriodicalIF":3.2,"publicationDate":"2024-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141261732","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Prospective validation of clinical deterioration predictive models prior to intensive care unit transfer among patients admitted to acute care cardiology wards.","authors":"Jessica Keim-Malpass, Liza P Moorman, J Randall Moorman, Susan Hamil, Gholamreza Yousefvand, Oliver J Monfredi, Sarah J Ratcliffe, Katy N Krahn, Marieke K Jones, Matthew T Clark, Jamieson M Bourque","doi":"10.1088/1361-6579/ad4e90","DOIUrl":"10.1088/1361-6579/ad4e90","url":null,"abstract":"<p><p><i>Objective</i>. Very few predictive models have been externally validated in a prospective cohort following the implementation of an artificial intelligence analytic system. This type of real-world validation is critically important due to the risk of data drift, or changes in data definitions or clinical practices over time, that could impact model performance in contemporaneous real-world cohorts. In this work, we report the model performance of a predictive analytics tool developed before COVID-19 and demonstrate model performance during the COVID-19 pandemic.<i>Approach</i>. The analytic system (CoMETⓇ, Nihon Kohden Digital Health Solutions LLC, Irvine, CA) was implemented in a randomized controlled trial that enrolled 10 422 patient visits in a 1:1 display-on display-off design. The CoMET scores were calculated for all patients but only displayed in the display-on arm. Only the control/display-off group is reported here because the scores could not alter care patterns.<i>Main results.</i>Of the 5184 visits in the display-off arm, 311 experienced clinical deterioration and care escalation, resulting in transfer to the intensive care unit, primarily due to respiratory distress. The model performance of CoMET was assessed based on areas under the receiver operating characteristic curve, which ranged from 0.725 to 0.737.<i>Significance.</i>The models were well-calibrated, and there were dynamic increases in the model scores in the hours preceding the clinical deterioration events. A hypothetical alerting strategy based on a rise in score and duration of the rise would have had good performance, with a positive predictive value more than 10-fold the event rate. We conclude that predictive statistical models developed five years before study initiation had good model performance despite the passage of time and the impact of the COVID-19 pandemic.</p>","PeriodicalId":20047,"journal":{"name":"Physiological measurement","volume":null,"pages":null},"PeriodicalIF":2.3,"publicationDate":"2024-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141076322","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Wagner diagram for modeling O₂pathway-calculation and graphical display by the Helsinki O₂Pathway Tool.","authors":"Antti-Pekka E Rissanen, Tom Mikkola, Dominique D Gagnon, Elias Lehtonen, Sakari Lukkarinen, Juha E Peltonen","doi":"10.1088/1361-6579/ad4c36","DOIUrl":"10.1088/1361-6579/ad4c36","url":null,"abstract":"<p><p><i>Objective.</i>Maximal O₂uptake (V˙O2max) reflects the individual's maximal rate of O₂transport and utilization through the integrated whole-body pathway composed of the lungs, heart, blood, circulation, and metabolically active tissues. As such,V˙O2maxis strongly associated with physical capacity as well as overall health and thus acts as one predictor of physical performance and as a vital sign in determination of status and progress of numerous clinical conditions. Quantifying the contribution of single parts of the multistep O₂pathway toV˙O2maxprovides mechanistic insights into exercise (in)tolerance and into therapy-, training-, or disuse-induced adaptations at individual or group levels. We developed a desktop application (Helsinki O₂Pathway Tool-HO₂PT) to model numerical and graphical display of the O₂pathway based on the 'Wagner diagram' originally formulated by Peter D. Wagner and his colleagues.<i>Approach.</i>The HO₂PT was developed and programmed in Python to integrate the Fick principle and Fick's law of diffusion into a computational system to import, calculate, graphically display, and export variables of the Wagner diagram.<i>Main results.</i>The HO₂PT models O₂pathway both numerically and graphically according to the Wagner diagram and pertains to conditions under which the mitochondrial oxidative capacity of metabolically active tissues exceeds the capacity of the O₂transport system to deliver O₂to the mitochondria. The tool is based on the Python open source code and libraries and freely and publicly available online for Windows, macOS, and Linux operating systems.<i>Significance.</i>The HO₂PT offers a novel functional and demonstrative platform for those interested in examiningV˙O2maxand its determinants by using the Wagner diagram. It will improve access to and usability of Wagner's and his colleagues' integrated physiological model and thereby benefit users across the wide spectrum of contexts such as scientific research, education, exercise testing, sports coaching, and clinical medicine.</p>","PeriodicalId":20047,"journal":{"name":"Physiological measurement","volume":null,"pages":null},"PeriodicalIF":3.2,"publicationDate":"2024-06-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140945603","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A cross species thermoelectric and spatiotemporal analysis of alternans in live explanted hearts using dual voltage-calcium fluorescence optical mapping.","authors":"Anna Crispino, Alessandro Loppini, Ilija Uzelac, Shahriar Iravanian, Neal K Bhatia, Michael Burke, Simonetta Filippi, Flavio H Fenton, Alessio Gizzi","doi":"10.1088/1361-6579/ad4e8f","DOIUrl":"10.1088/1361-6579/ad4e8f","url":null,"abstract":"<p><p><i>Objective.</i>Temperature plays a crucial role in influencing the spatiotemporal dynamics of the heart. Electrical instabilities due to specific thermal conditions typically lead to early period-doubling bifurcations and beat-to-beat alternans. These pro-arrhythmic phenomena manifest in voltage and calcium traces, resulting in compromised contractile behaviors. In such intricate scenario, dual optical mapping technique was used to uncover unexplored multi-scale and nonlinear couplings, essential for early detection and understanding of cardiac arrhythmia.<i>Approach.</i>We propose a methodological analysis of synchronized voltage-calcium signals for detecting alternans, restitution curves, and spatiotemporal alternans patterns under different thermal conditions, based on integral features calculation. To validate our approach, we conducted a cross-species investigation involving rabbit and guinea pig epicardial ventricular surfaces and human endocardial tissue under pacing-down protocols.<i>Main results.</i>We show that the proposed integral feature, as the area under the curve, could be an easily applicable indicator that may enhance the predictability of the onset and progression of cardiac alternans. Insights into spatiotemporal correlation analysis of characteristic spatial lengths across different heart species were further provided.<i>Significance.</i>Exploring cross-species thermoelectric features contributes to understanding temperature-dependent proarrhythmic regimes and their implications on coupled spatiotemporal voltage-calcium dynamics. The findings provide preliminary insights and potential strategies for enhancing arrhythmia detection and treatment.</p>","PeriodicalId":20047,"journal":{"name":"Physiological measurement","volume":null,"pages":null},"PeriodicalIF":3.2,"publicationDate":"2024-06-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141076169","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Highly comparative time series analysis of oxygen saturation and heart rate to predict respiratory outcomes in extremely preterm infants.","authors":"Jiaxing Qiu, Juliann M Di Fiore, Narayanan Krishnamurthi, Premananda Indic, John L Carroll, Nelson Claure, James S Kemp, Phyllis A Dennery, Namasivayam Ambalavanan, Debra E Weese-Mayer, Anna Maria Hibbs, Richard J Martin, Eduardo Bancalari, Aaron Hamvas, J Randall Moorman, Douglas E Lake, Katy N Krahn, Amanda M Zimmet, Bradley S Hopkins, Erin K Lonergan, Casey M Rand, Arlene Zadell, Arie Nakhmani, Waldemar A Carlo, Deborah Laney, Colm P Travers, Silvia Vanbuskirk, Carmen D'Ugard, Ana Cecilia Aguilar, Alini Schott, Julie Hoffmann, Laura Linneman","doi":"10.1088/1361-6579/ad4e91","DOIUrl":"10.1088/1361-6579/ad4e91","url":null,"abstract":"<p><p><i>Objective.</i>Highly comparative time series analysis (HCTSA) is a novel approach involving massive feature extraction using publicly available code from many disciplines. The Prematurity-Related Ventilatory Control (Pre-Vent) observational multicenter prospective study collected bedside monitor data from>700extremely preterm infants to identify physiologic features that predict respiratory outcomes.<i>Approach</i>. We calculated a subset of 33 HCTSA features on>7 M 10 min windows of oxygen saturation (SPO2) and heart rate (HR) from the Pre-Vent cohort to quantify predictive performance. This subset included representatives previously identified using unsupervised clustering on>3500HCTSA algorithms. We hypothesized that the best HCTSA algorithms would compare favorably to optimal PreVent physiologic predictor IH90_DPE (duration per event of intermittent hypoxemia events below 90%).<i>Main Results.</i>The top HCTSA features were from a cluster of algorithms associated with the autocorrelation of SPO2 time series and identified low frequency patterns of desaturation as high risk. These features had comparable performance to and were highly correlated with IH90_DPE but perhaps measure the physiologic status of an infant in a more robust way that warrants further investigation. The top HR HCTSA features were symbolic transformation measures that had previously been identified as strong predictors of neonatal mortality. HR metrics were only important predictors at early days of life which was likely due to the larger proportion of infants whose outcome was death by any cause. A simple HCTSA model using 3 top features outperformed IH90_DPE at day of life 7 (.778 versus .729) but was essentially equivalent at day of life 28 (.849 versus .850).<i>Significance</i>. These results validated the utility of a representative HCTSA approach but also provides additional evidence supporting IH90_DPE as an optimal predictor of respiratory outcomes.</p>","PeriodicalId":20047,"journal":{"name":"Physiological measurement","volume":null,"pages":null},"PeriodicalIF":2.3,"publicationDate":"2024-06-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11485323/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141076172","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Multi-source deep domain adaptation ensemble framework for cross-dataset motor imagery EEG transfer learning.","authors":"Minmin Miao, Zhong Yang, Zhenzhen Sheng, Baoguo Xu, Wenbin Zhang, Xinmin Cheng","doi":"10.1088/1361-6579/ad4e95","DOIUrl":"10.1088/1361-6579/ad4e95","url":null,"abstract":"<p><p><i>Objective</i>. Electroencephalography (EEG) is an important kind of bioelectric signal for measuring physiological activities of the brain, and motor imagery (MI) EEG has significant clinical application prospects. Convolutional neural network has become a mainstream algorithm for MI EEG classification, however lack of subject-specific data considerably restricts its decoding accuracy and generalization performance. To address this challenge, a novel transfer learning (TL) framework using auxiliary dataset to improve the MI EEG classification performance of target subject is proposed in this paper.<i>Approach</i>. We developed a multi-source deep domain adaptation ensemble framework (MSDDAEF) for cross-dataset MI EEG decoding. The proposed MSDDAEF comprises three main components: model pre-training, deep domain adaptation, and multi-source ensemble. Moreover, for each component, different designs were examined to verify the robustness of MSDDAEF.<i>Main results</i>. Bidirectional validation experiments were performed on two large public MI EEG datasets (openBMI and GIST). The highest average classification accuracy of MSDDAEF reaches 74.28% when openBMI serves as target dataset and GIST serves as source dataset. While the highest average classification accuracy of MSDDAEF is 69.85% when GIST serves as target dataset and openBMI serves as source dataset. In addition, the classification performance of MSDDAEF surpasses several well-established studies and state-of-the-art algorithms.<i>Significance</i>. The results of this study show that cross-dataset TL is feasible for left/right-hand MI EEG decoding, and further indicate that MSDDAEF is a promising solution for addressing MI EEG cross-dataset variability.</p>","PeriodicalId":20047,"journal":{"name":"Physiological measurement","volume":null,"pages":null},"PeriodicalIF":3.2,"publicationDate":"2024-06-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141076359","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Experimental validation of an advanced impedance pneumography for monitoring ventilation volume during programmed cycling exercise.","authors":"Xing Zhou, Qin Liu, Zixuan Bai, Shan Xue, Zhibin Kong, Yixin Ma","doi":"10.1088/1361-6579/ad4951","DOIUrl":"10.1088/1361-6579/ad4951","url":null,"abstract":"<p><p><i>Objective.</i>Impedance pneumography (IP) has provided static assessments of subjects' breathing patterns in previous studies. Evaluating the feasibility and limitation of ambulatory IP based respiratory monitoring needs further investigation on clinically relevant exercise designs. The aim of this study was to evaluate the capacity of an advanced IP in ambulatory respiratory monitoring, and its predictive value in independent ventilatory capacity quantification during cardiopulmonary exercise testing (CPET).<i>Approach.</i>35 volunteers were examined with the same calibration methodology and CPET exercise protocol comprising phases of rest, unloaded, incremental load, maximum load, recovery and further-recovery. In 3 or 4 deep breaths of calibration stage, thoracic impedance and criterion spirometric volume were simultaneously recorded to produce phase-specific prior calibration coefficients (CCs). The IP measurement during exercise protocol was converted by prior CCs to volume estimation curve and thus calculate minute ventilation (VE) independent from the spirometry approach.<i>Main results.</i>Across all measurements, the relative error of IP-derived VE (VE_R) and flowrate-derived VE (VE_f) was less than 13.8%. In Bland-Altman plots, the aggregate VE estimation bias was statistically insignificant for all 3 phases with pedaling exercise and the discrepancy between VE_Rand VE_ffell within the 95% limits of agreement (95% LoA) for 34 or all subjects in each of all CPET phases.<i>Significance.</i>This work reinforces the independent use of IP as an accurate and robust alternative to flowmeter for applications in cycle ergometry CPET, which could significantly encourage the clinical use of IP and improve the convenience and comfort of CPET.</p>","PeriodicalId":20047,"journal":{"name":"Physiological measurement","volume":null,"pages":null},"PeriodicalIF":3.2,"publicationDate":"2024-05-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140899110","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Pitfalls and possibilities of using Root SedLine for continuous assessment of EEG waveform-based metrics in intensive care research.","authors":"Stefan Yu Bögli, Marina Sandra Cherchi, Ihsane Olakorede, Andrea Lavinio, Erta Beqiri, Ethan Moyer, Dick Moberg, Peter Smielewski","doi":"10.1088/1361-6579/ad46e4","DOIUrl":"10.1088/1361-6579/ad46e4","url":null,"abstract":"<p><p><i>Objective.</i>The Root SedLine device is used for continuous electroencephalography (cEEG)-based sedation monitoring in intensive care patients. The cEEG traces can be collected for further processing and calculation of relevant metrics not already provided. Depending on the device settings during acquisition, the acquired traces may be distorted by max/min value cropping or high digitization errors. We aimed to systematically assess the impact of these distortions on metrics used for clinical research in the field of neuromonitoring.<i>Approach.</i>A 16 h cEEG acquired using the Root SedLine device at the optimal screen settings was analyzed. Cropping and digitization error effects were simulated by consecutive reduction of the maximum cEEG amplitude by 2<i>µ</i>V or by reducing the vertical resolution. Metrics were calculated within ICM+ using minute-by-minute data, including the total power, alpha delta ratio (ADR), and 95% spectral edge frequency. Data were analyzed by creating violin- or box-plots.<i>Main Results.</i>Cropping led to a continuous reduction in total and band power, leading to corresponding changes in variability thereof. The relative power and ADR were less affected. Changes in resolution led to relevant changes. While the total power and power of low frequencies were rather stable, the power of higher frequencies increased with reducing resolution.<i>Significance.</i>Care must be taken when acquiring and analyzing cEEG waveforms from Root SedLine for clinical research. To retrieve good quality metrics, the screen settings must be kept within the central vertical scale, while pre-processing techniques must be applied to exclude unacceptable periods.</p>","PeriodicalId":20047,"journal":{"name":"Physiological measurement","volume":null,"pages":null},"PeriodicalIF":3.2,"publicationDate":"2024-05-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140869319","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}