LIVIVO - The Search Portal for Life Sciences

zur deutschen Oberfläche wechseln
Advanced search

Search results

Result 1 - 10 of total 16

Search options

  1. Article ; Online: Dexmedetomidine-induced deep sedation mimics non-rapid eye movement stage 3 sleep: large-scale validation using machine learning.

    Ramaswamy, Sowmya M / Weerink, Maud A S / Struys, Michel M R F / Nagaraj, Sunil B

    Sleep

    2020  Volume 44, Issue 2

    Abstract: Study objectives: Dexmedetomidine-induced electroencephalogram (EEG) patterns during deep sedation are comparable with natural sleep patterns. Using large-scale EEG recordings and machine learning techniques, we investigated whether dexmedetomidine- ... ...

    Abstract Study objectives: Dexmedetomidine-induced electroencephalogram (EEG) patterns during deep sedation are comparable with natural sleep patterns. Using large-scale EEG recordings and machine learning techniques, we investigated whether dexmedetomidine-induced deep sedation indeed mimics natural sleep patterns.
    Methods: We used EEG recordings from three sources in this study: 8,707 overnight sleep EEG and 30 dexmedetomidine clinical trial EEG. Dexmedetomidine-induced sedation levels were assessed using the Modified Observer's Assessment of Alertness/Sedation (MOAA/S) score. We extracted 22 spectral features from each EEG recording using a multitaper spectral estimation method. Elastic-net regularization method was used for feature selection. We compared the performance of several machine learning algorithms (logistic regression, support vector machine, and random forest), trained on individual sleep stages, to predict different levels of the MOAA/S sedation state.
    Results: The random forest algorithm trained on non-rapid eye movement stage 3 (N3) predicted dexmedetomidine-induced deep sedation (MOAA/S = 0) with area under the receiver operator characteristics curve >0.8 outperforming other machine learning models. Power in the delta band (0-4 Hz) was selected as an important feature for prediction in addition to power in theta (4-8 Hz) and beta (16-30 Hz) bands.
    Conclusions: Using a large-scale EEG data-driven approach and machine learning framework, we show that dexmedetomidine-induced deep sedation state mimics N3 sleep EEG patterns.
    Clinical trials: Name-Pharmacodynamic Interaction of REMI and DMED (PIRAD), URL-https://clinicaltrials.gov/ct2/show/NCT03143972, and registration-NCT03143972.
    MeSH term(s) Deep Sedation ; Dexmedetomidine ; Electroencephalography ; Hypnotics and Sedatives/adverse effects ; Machine Learning ; Sleep, Slow-Wave
    Chemical Substances Hypnotics and Sedatives ; Dexmedetomidine (67VB76HONO)
    Language English
    Publishing date 2020-10-13
    Publishing country United States
    Document type Journal Article ; Research Support, Non-U.S. Gov't
    ZDB-ID 424441-2
    ISSN 1550-9109 ; 0161-8105
    ISSN (online) 1550-9109
    ISSN 0161-8105
    DOI 10.1093/sleep/zsaa167
    Database MEDical Literature Analysis and Retrieval System OnLINE

    More links

    Kategorien

    In stock of ZB MED Cologne/Königswinter

    Zs.A 1475: Show issues Location:
    Je nach Verfügbarkeit (siehe Angabe bei Bestand)
    bis Jg. 1994: Bestellungen von Artikeln über das Online-Bestellformular
    Jg. 1995 - 2021: Lesesall (1.OG)
    ab Jg. 2022: Lesesaal (EG)

    Order via subito

    This service is chargeable due to the Delivery terms set by subito. Orders including an article and supplementary material will be classified as separate orders. In these cases, fees will be demanded for each order.

  2. Article ; Online: Predicting short- and long-term glycated haemoglobin response after insulin initiation in patients with type 2 diabetes mellitus using machine-learning algorithms.

    Nagaraj, Sunil B / Sidorenkov, Grigory / van Boven, Job F M / Denig, Petra

    Diabetes, obesity & metabolism

    2019  Volume 21, Issue 12, Page(s) 2704–2711

    Abstract: Aim: To assess the potential of supervised machine-learning techniques to identify clinical variables for predicting short-term and long-term glycated haemoglobin (HbA1c) response after insulin treatment initiation in patients with type 2 diabetes ... ...

    Abstract Aim: To assess the potential of supervised machine-learning techniques to identify clinical variables for predicting short-term and long-term glycated haemoglobin (HbA1c) response after insulin treatment initiation in patients with type 2 diabetes mellitus (T2DM).
    Materials and methods: We included patients with T2DM from the Groningen Initiative to Analyse Type 2 diabetes Treatment (GIANTT) database who started insulin treatment between 2007 and 2013 and had a minimum follow-up of 2 years. Short- and long-term responses at 6 (±2) and 24 (±2) months after insulin initiation, respectively, were assessed. Patients were defined as good responders if they had a decrease in HbA1c ≥ 5 mmol/mol or reached the recommended level of HbA1c ≤ 53 mmol/mol. Twenty-four baseline clinical variables were used for the analysis and an elastic net regularization technique was used for variable selection. The performance of three traditional machine-learning algorithms was compared for the prediction of short- and long-term responses and the area under the receiver-operating characteristic curve (AUC) was used to assess the performance of the prediction models.
    Results: The elastic net regularization-based generalized linear model, which included baseline HbA1c and estimated glomerular filtration rate, correctly classified short- and long-term HbA1c response after treatment initiation, with AUCs of 0.80 (95% CI 0.78-0.83) and 0.81 (95% CI 0.79-0.84), respectively, and outperformed the other machine-learning algorithms. Using baseline HbA1c alone, an AUC = 0.71 (95% CI 0.65-0.73) and 0.72 (95% CI 0.66-0.75) was obtained for predicting short-term and long-term response, respectively.
    Conclusions: Machine-learning algorithm performed well in the prediction of an individual's short-term and long-term HbA1c response using baseline clinical variables.
    MeSH term(s) Aged ; Algorithms ; Diabetes Mellitus, Type 2/drug therapy ; Female ; Glycated Hemoglobin A/analysis ; Humans ; Hypoglycemic Agents/therapeutic use ; Insulin/therapeutic use ; Machine Learning ; Male ; Middle Aged
    Chemical Substances Glycated Hemoglobin A ; Hypoglycemic Agents ; Insulin ; hemoglobin A1c protein, human
    Language English
    Publishing date 2019-09-30
    Publishing country England
    Document type Journal Article ; Observational Study ; Research Support, Non-U.S. Gov't
    ZDB-ID 1454944-x
    ISSN 1463-1326 ; 1462-8902
    ISSN (online) 1463-1326
    ISSN 1462-8902
    DOI 10.1111/dom.13860
    Database MEDical Literature Analysis and Retrieval System OnLINE

    Full text online

    More links

    Kategorien

    In stock of ZB MED Cologne/Königswinter

    Zs.A 5442: Show issues Location:
    Je nach Verfügbarkeit (siehe Angabe bei Bestand)
    bis Jg. 1994: Bestellungen von Artikeln über das Online-Bestellformular
    Jg. 1995 - 2021: Lesesall (2.OG)
    ab Jg. 2022: Lesesaal (EG)

    Order via subito

    This service is chargeable due to the Delivery terms set by subito. Orders including an article and supplementary material will be classified as separate orders. In these cases, fees will be demanded for each order.

  3. Article ; Online: Brain Monitoring of Sedation in the Intensive Care Unit Using a Recurrent Neural Network.

    Sun, Haoqi / Nagaraj, Sunil B / Akeju, Oluwaseun / Purdon, Patrick L / Westover, Brandon M

    Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference

    2018  Volume 2018, Page(s) 1–4

    Abstract: Over and under-sedation are common in critically ill patients admitted to the Intensive Care Unit. Clinical assessments provide limited time resolution and are based on behavior rather than the brain itself. Existing brain monitors have been developed ... ...

    Abstract Over and under-sedation are common in critically ill patients admitted to the Intensive Care Unit. Clinical assessments provide limited time resolution and are based on behavior rather than the brain itself. Existing brain monitors have been developed primarily for non-ICU settings. Here, we use a clinical dataset from 154 ICU patients in whom the Richmond Agitation-Sedation Score is assessed about every 2 hours. We develop a recurrent neural network (RNN) model to discriminate between deep vs. no sedation, trained end-to-end from raw EEG spectrograms without any feature extraction. We obtain an average area under the ROC of 0.8 on 10-fold cross validation across patients. Our RNN is able to provide reliable estimates of sedation levels consistently better compared to a feed-forward model with simple smoothing. Decomposing the prediction error in terms of sedatives reveals that patient-specific calibration for sedatives is expected to further improve sedation monitoring.
    MeSH term(s) Aged ; Anesthesia ; Brain/physiology ; Critical Illness ; Female ; Humans ; Hypnotics and Sedatives ; Intensive Care Units ; Male ; Middle Aged ; Monitoring, Physiologic ; Nerve Net ; Prospective Studies ; Time Factors
    Chemical Substances Hypnotics and Sedatives
    Language English
    Publishing date 2018-09-17
    Publishing country United States
    Document type Journal Article
    ISSN 2694-0604
    ISSN (online) 2694-0604
    DOI 10.1109/EMBC.2018.8513185
    Database MEDical Literature Analysis and Retrieval System OnLINE

    More links

    Kategorien

    Order via subito

    This service is chargeable due to the Delivery terms set by subito. Orders including an article and supplementary material will be classified as separate orders. In these cases, fees will be demanded for each order.

    Inter-library loan at ZB MED

    Your chosen title can be delivered directly to ZB MED Cologne location if you are registered as a user at ZB MED Cologne.

  4. Article ; Online: Automatic Detection of General Anesthetic-States using ECG-Derived Autonomic Nervous System Features.

    Polk, Sam L / Kashkooli, Kimia / Nagaraj, Sunil B / Chamadia, Shubham / Murphy, James M / Sun, Haoqi / Westover, M Brandon / Barbieri, Riccardo / Akeju, Oluwaseun

    Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference

    2019  Volume 2019, Page(s) 2019–2022

    Abstract: Electroencephalogram (EEG)-based prediction systems are used to target anesthetic-states in patients undergoing procedures with general anesthesia (GA). These systems are not widely employed in resource-limited settings because they are cost-prohibitive. ...

    Abstract Electroencephalogram (EEG)-based prediction systems are used to target anesthetic-states in patients undergoing procedures with general anesthesia (GA). These systems are not widely employed in resource-limited settings because they are cost-prohibitive. Although anesthetic-drugs induce highly-structured, oscillatory neural dynamics that make EEG-based systems a principled approach for anesthetic-state monitoring, anesthetic-drugs also significantly modulate the autonomic nervous system (ANS). Because ANS dynamics can be inferred from electrocardiogram (ECG) features such as heart rate variability, it may be possible to develop an ECG-based system to infer anesthetic-states as a low-cost and practical alternative to EEG-based anesthetic-state prediction systems. In this work, we demonstrate that an ECG-based system using ANS features can be used to discriminate between non-GA and GA states in sevoflurane, with a GA F
    MeSH term(s) Anesthesia, General ; Anesthetics, Inhalation ; Autonomic Nervous System/physiology ; Consciousness ; Electrocardiography ; Electroencephalography ; Heart Rate ; Humans
    Chemical Substances Anesthetics, Inhalation
    Language English
    Publishing date 2019-12-30
    Publishing country United States
    Document type Journal Article ; Research Support, N.I.H., Extramural
    ISSN 2694-0604
    ISSN (online) 2694-0604
    DOI 10.1109/EMBC.2019.8857704
    Database MEDical Literature Analysis and Retrieval System OnLINE

    More links

    Kategorien

    Order via subito

    This service is chargeable due to the Delivery terms set by subito. Orders including an article and supplementary material will be classified as separate orders. In these cases, fees will be demanded for each order.

    Inter-library loan at ZB MED

    Your chosen title can be delivered directly to ZB MED Cologne location if you are registered as a user at ZB MED Cologne.

  5. Article ; Online: Novel drug-independent sedation level estimation based on machine learning of quantitative frontal electroencephalogram features in healthy volunteers.

    Ramaswamy, Sowmya M / Kuizenga, Merel H / Weerink, Maud A S / Vereecke, Hugo E M / Struys, Michel M R F / Nagaraj, Sunil B

    British journal of anaesthesia

    2019  Volume 123, Issue 4, Page(s) 479–487

    Abstract: Background: Sedation indicators based on a single quantitative EEG (QEEG) feature have been criticised for their limited performance. We hypothesised that integration of multiple QEEG features into a single sedation-level estimator using a machine ... ...

    Abstract Background: Sedation indicators based on a single quantitative EEG (QEEG) feature have been criticised for their limited performance. We hypothesised that integration of multiple QEEG features into a single sedation-level estimator using a machine learning algorithm could reliably predict levels of sedation, independent of the sedative drug used.
    Methods: In total, 102 subjects receiving propofol (N=36; 16 male/20 female), sevoflurane (N=36; 16 male/20 female), or dexmedetomidine (N=30; 15 male/15 female) were included in this study of healthy volunteers. Sedation level was assessed using the Modified Observer's Assessment of Alertness/Sedation (MOAA/S) score. We used 44 QEEG features estimated from the EEG data in a logistic regression algorithm, and an elastic-net regularisation method was used for feature selection. The area under the receiver operator characteristic curve (AUC) was used to assess the performance of the logistic regression model.
    Results: The performances obtained when the system was trained and tested as drug-dependent mode to distinguish between awake and sedated states (mean AUC [standard deviation]) were propofol=0.97 (0.03), sevoflurane=0.74 (0.25), and dexmedetomidine=0.77 (0.10). The drug-independent system resulted in mean AUC=0.83 (0.17) to discriminate between the awake and sedated states.
    Conclusions: The incorporation of large numbers of QEEG features and machine learning algorithms is feasible for next-generation monitors of sedation level. Different QEEG features were selected for propofol, sevoflurane, and dexmedetomidine groups, but the sedation-level estimator maintained a high performance for predicting MOAA/S independent of the drug used.
    Clinical trial registration: NCT02043938; NCT03143972.
    MeSH term(s) Anesthetics/pharmacology ; Consciousness Monitors ; Electroencephalography/statistics & numerical data ; Frontal Lobe/drug effects ; Humans ; Machine Learning ; Reference Values ; Reproducibility of Results ; Wakefulness/drug effects
    Chemical Substances Anesthetics
    Language English
    Publishing date 2019-07-18
    Publishing country England
    Document type Journal Article
    ZDB-ID 80074-0
    ISSN 1471-6771 ; 0007-0912
    ISSN (online) 1471-6771
    ISSN 0007-0912
    DOI 10.1016/j.bja.2019.06.004
    Database MEDical Literature Analysis and Retrieval System OnLINE

    More links

    Kategorien

    In stock of ZB MED Cologne/Königswinter

    Uk I Zs.80: Show issues Location:
    Je nach Verfügbarkeit (siehe Angabe bei Bestand)
    bis Jg. 2021: Bestellungen von Artikeln über das Online-Bestellformular
    ab Jg. 2022: Lesesaal (EG)

    Order via subito

    This service is chargeable due to the Delivery terms set by subito. Orders including an article and supplementary material will be classified as separate orders. In these cases, fees will be demanded for each order.

  6. Article ; Online: The revised Cerebral Recovery Index improves predictions of neurological outcome after cardiac arrest.

    Nagaraj, Sunil B / Tjepkema-Cloostermans, Marleen C / Ruijter, Barry J / Hofmeijer, Jeannette / van Putten, Michel J A M

    Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology

    2018  Volume 129, Issue 12, Page(s) 2557–2566

    Abstract: Objective: Analysis of the electroencephalogram (EEG) background pattern helps predicting neurological outcome of comatose patients after cardiac arrest (CA). Visual analysis may not extract all discriminative information. We present predictive values ... ...

    Abstract Objective: Analysis of the electroencephalogram (EEG) background pattern helps predicting neurological outcome of comatose patients after cardiac arrest (CA). Visual analysis may not extract all discriminative information. We present predictive values of the revised Cerebral Recovery Index (rCRI), based on continuous extraction and combination of a large set of evolving quantitative EEG (qEEG) features and machine learning techniques.
    Methods: We included 551 subsequent patients from a prospective cohort study on continuous EEG after CA in two hospitals. Outcome at six months was classified as good (Cerebral Performance Category (CPC) 1-2) or poor (CPC 3-5). Forty-four qEEG features (from time, frequency and entropy domain) were selected by the least absolute shrinkage and selection operator (LASSO) method and used in a Random Forests classification system. We trained and evaluated the system with 10-fold cross validation. For poor outcome prediction, the sensitivity at 100% specificity (Se
    Results: Two hundred fifty-six (47%) patients had a good outcome. The rCRI predicted poor outcome with AUC = 0.94 (95% CI: 0.83-0.91), Se
    Conclusions: Results obtained in this study suggest that with machine learning algorithms and large set of qEEG features, it is possible to efficiently monitor patient outcome after CA. We also demonstrate the importance of selection of optimal performance metric to train a classifier model for outcome prediction.
    Significance: The rCRI is a sensitive, reliable predictor of neurological outcome of comatose patients after CA.
    MeSH term(s) Aged ; Brain Diseases/diagnosis ; Brain Diseases/epidemiology ; Brain Diseases/etiology ; Cerebral Cortex/physiopathology ; Electroencephalography/methods ; Female ; Glasgow Outcome Scale ; Heart Arrest/complications ; Humans ; Machine Learning ; Male ; Middle Aged
    Language English
    Publishing date 2018-10-27
    Publishing country Netherlands
    Document type Evaluation Studies ; Journal Article
    ZDB-ID 1463630-x
    ISSN 1872-8952 ; 0921-884X ; 1388-2457
    ISSN (online) 1872-8952
    ISSN 0921-884X ; 1388-2457
    DOI 10.1016/j.clinph.2018.10.004
    Database MEDical Literature Analysis and Retrieval System OnLINE

    Full text online

    More links

    Kategorien

    In stock of ZB MED Cologne/Königswinter

    Ui II Zs.140: Show issues Location:
    Je nach Verfügbarkeit (siehe Angabe bei Bestand)
    bis Jg. 2021: Bestellungen von Artikeln über das Online-Bestellformular
    ab Jg. 2022: Lesesaal (EG)

    Order via subito

    This service is chargeable due to the Delivery terms set by subito. Orders including an article and supplementary material will be classified as separate orders. In these cases, fees will be demanded for each order.

  7. Article ; Online: A metabolomics-based molecular pathway analysis of how the sodium-glucose co-transporter-2 inhibitor dapagliflozin may slow kidney function decline in patients with diabetes.

    Mulder, Skander / Hammarstedt, Ann / Nagaraj, Sunil B / Nair, Viji / Ju, Wenjun / Hedberg, Jonatan / Greasley, Peter J / Eriksson, Jan W / Oscarsson, Jan / Heerspink, Hiddo J L

    Diabetes, obesity & metabolism

    2020  Volume 22, Issue 7, Page(s) 1157–1166

    Abstract: Aim: To investigate which metabolic pathways are targeted by the sodium-glucose co-transporter-2 inhibitor dapagliflozin to explore the molecular processes involved in its renal protective effects.: Methods: An unbiased mass spectrometry plasma ... ...

    Abstract Aim: To investigate which metabolic pathways are targeted by the sodium-glucose co-transporter-2 inhibitor dapagliflozin to explore the molecular processes involved in its renal protective effects.
    Methods: An unbiased mass spectrometry plasma metabolomics assay was performed on baseline and follow-up (week 12) samples from the EFFECT II trial in patients with type 2 diabetes with non-alcoholic fatty liver disease receiving dapagliflozin 10 mg/day (n = 19) or placebo (n = 6). Transcriptomic signatures from tubular compartments were identified from kidney biopsies collected from patients with diabetic kidney disease (DKD) (n = 17) and healthy controls (n = 30) from the European Renal cDNA Biobank. Serum metabolites that significantly changed after 12 weeks of dapagliflozin were mapped to a metabolite-protein interaction network. These proteins were then linked with intra-renal transcripts that were associated with DKD or estimated glomerular filtration rate (eGFR). The impacted metabolites and their protein-coding transcripts were analysed for enriched pathways.
    Results: Of all measured (n = 812) metabolites, 108 changed (P < 0.05) during dapagliflozin treatment and 74 could be linked to 367 unique proteins/genes. Intra-renal mRNA expression analysis of the genes encoding the metabolite-associated proteins using kidney biopsies resulted in 105 genes that were significantly associated with eGFR in patients with DKD, and 135 genes that were differentially expressed between patients with DKD and controls. The combination of metabolites and transcripts identified four enriched pathways that were affected by dapagliflozin and associated with eGFR: glycine degradation (mitochondrial function), TCA cycle II (energy metabolism), L-carnitine biosynthesis (energy metabolism) and superpathway of citrulline metabolism (nitric oxide synthase and endothelial function).
    Conclusion: The observed molecular pathways targeted by dapagliflozin and associated with DKD suggest that modifying molecular processes related to energy metabolism, mitochondrial function and endothelial function may contribute to its renal protective effect.
    MeSH term(s) Benzhydryl Compounds/therapeutic use ; Diabetes Mellitus, Type 2/complications ; Diabetes Mellitus, Type 2/drug therapy ; Diabetes Mellitus, Type 2/genetics ; Glucose ; Glucosides ; Humans ; Kidney ; Metabolomics ; Sodium ; Sodium-Glucose Transporter 2 Inhibitors/therapeutic use ; Symporters
    Chemical Substances Benzhydryl Compounds ; Glucosides ; Sodium-Glucose Transporter 2 Inhibitors ; Symporters ; dapagliflozin (1ULL0QJ8UC) ; Sodium (9NEZ333N27) ; Glucose (IY9XDZ35W2)
    Language English
    Publishing date 2020-03-25
    Publishing country England
    Document type Journal Article ; Randomized Controlled Trial ; Research Support, N.I.H., Extramural ; Research Support, Non-U.S. Gov't
    ZDB-ID 1454944-x
    ISSN 1463-1326 ; 1462-8902
    ISSN (online) 1463-1326
    ISSN 1462-8902
    DOI 10.1111/dom.14018
    Database MEDical Literature Analysis and Retrieval System OnLINE

    Full text online

    More links

    Kategorien

    In stock of ZB MED Cologne/Königswinter

    Zs.A 5442: Show issues Location:
    Je nach Verfügbarkeit (siehe Angabe bei Bestand)
    bis Jg. 1994: Bestellungen von Artikeln über das Online-Bestellformular
    Jg. 1995 - 2021: Lesesall (2.OG)
    ab Jg. 2022: Lesesaal (EG)

    Order via subito

    This service is chargeable due to the Delivery terms set by subito. Orders including an article and supplementary material will be classified as separate orders. In these cases, fees will be demanded for each order.

  8. Article ; Online: ADARRI: a novel method to detect spurious R-peaks in the electrocardiogram for heart rate variability analysis in the intensive care unit.

    Rebergen, Dennis J / Nagaraj, Sunil B / Rosenthal, Eric S / Bianchi, Matt T / van Putten, Michel J A M / Westover, M Brandon

    Journal of clinical monitoring and computing

    2017  Volume 32, Issue 1, Page(s) 53–61

    Abstract: We developed a simple and fully automated method for detecting artifacts in the R-R interval (RRI) time series of the ECG that is tailored to the intensive care unit (ICU) setting. From ECG recordings of 50 adult ICU-subjects we selected 60 epochs with ... ...

    Abstract We developed a simple and fully automated method for detecting artifacts in the R-R interval (RRI) time series of the ECG that is tailored to the intensive care unit (ICU) setting. From ECG recordings of 50 adult ICU-subjects we selected 60 epochs with valid R-peak detections and 60 epochs containing artifacts leading to missed or false positive R-peak detections. Next, we calculated the absolute value of the difference between two adjacent RRIs (adRRI), and obtained the empirical probability distributions of adRRI values for valid R-peaks and artifacts. From these, we calculated an optimal threshold for separating adRRI values arising from artifact versus non-artefactual data. We compared the performance of our method with the methods of Berntson and Clifford on the same data. We identified 257,458 R-peak detections, of which 235,644 (91.5%) were true detections and 21,814 (8.5%) arose from artifacts. Our method showed superior performance for detecting artifacts with sensitivity 100%, specificity 99%, precision 99%, positive likelihood ratio of 100 and negative likelihood ratio <0.001 compared to Berntson's and Clifford's method with a sensitivity, specificity, precision and positive and negative likelihood ratio of 99%, 78%, 82%, 4.5, 0.013 for Berntson's method and 55%, 98%, 96%, 27.5, 0.460 for Clifford's method, respectively. A novel algorithm using a patient-independent threshold derived from the distribution of adRRI values in ICU ECG data identifies artifacts accurately, and outperforms two other methods in common use. Furthermore, the threshold was calculated based on real data from critically ill patients and the algorithm is easy to implement.
    MeSH term(s) Algorithms ; Artifacts ; Automation ; Critical Illness ; Electrocardiography ; Heart Rate/physiology ; Humans ; Infant, Newborn ; Intensive Care Units, Neonatal ; Intensive Care, Neonatal ; Predictive Value of Tests ; ROC Curve ; Reproducibility of Results ; Sensitivity and Specificity ; Signal Processing, Computer-Assisted ; Software
    Language English
    Publishing date 2017-02-16
    Publishing country Netherlands
    Document type Journal Article
    ZDB-ID 1418733-4
    ISSN 1573-2614 ; 1387-1307 ; 0748-1977
    ISSN (online) 1573-2614
    ISSN 1387-1307 ; 0748-1977
    DOI 10.1007/s10877-017-9999-9
    Database MEDical Literature Analysis and Retrieval System OnLINE

    Full text online

    More links

    Kategorien

    In stock of ZB MED Cologne/Königswinter

    Zs.A 2054: Show issues Location:
    Je nach Verfügbarkeit (siehe Angabe bei Bestand)
    bis Jg. 1994: Bestellungen von Artikeln über das Online-Bestellformular
    Jg. 1995 - 2021: Lesesall (1.OG)
    ab Jg. 2022: Lesesaal (EG)

    Order via subito

    This service is chargeable due to the Delivery terms set by subito. Orders including an article and supplementary material will be classified as separate orders. In these cases, fees will be demanded for each order.

  9. Article ; Online: Automated tracking of level of consciousness and delirium in critical illness using deep learning.

    Sun, Haoqi / Kimchi, Eyal / Akeju, Oluwaseun / Nagaraj, Sunil B / McClain, Lauren M / Zhou, David W / Boyle, Emily / Zheng, Wei-Long / Ge, Wendong / Westover, M Brandon

    NPJ digital medicine

    2019  Volume 2, Page(s) 89

    Abstract: Over- and under-sedation are common in the ICU, and contribute to poor ICU outcomes including delirium. Behavioral assessments, such as Richmond Agitation-Sedation Scale (RASS) for monitoring levels of sedation and Confusion Assessment Method for the ICU ...

    Abstract Over- and under-sedation are common in the ICU, and contribute to poor ICU outcomes including delirium. Behavioral assessments, such as Richmond Agitation-Sedation Scale (RASS) for monitoring levels of sedation and Confusion Assessment Method for the ICU (CAM-ICU) for detecting signs of delirium, are often used. As an alternative, brain monitoring with electroencephalography (EEG) has been proposed in the operating room, but is challenging to implement in ICU due to the differences between critical illness and elective surgery, as well as the duration of sedation. Here we present a deep learning model based on a combination of convolutional and recurrent neural networks that automatically tracks both the level of consciousness and delirium using frontal EEG signals in the ICU. For level of consciousness, the system achieves a median accuracy of 70% when allowing prediction to be within one RASS level difference across all patients, which is comparable or higher than the median technician-nurse agreement at 59%. For delirium, the system achieves an AUC of 0.80 with 69% sensitivity and 83% specificity at the optimal operating point. The results show it is feasible to continuously track level of consciousness and delirium in the ICU.
    Language English
    Publishing date 2019-09-09
    Publishing country England
    Document type Journal Article
    ISSN 2398-6352
    ISSN (online) 2398-6352
    DOI 10.1038/s41746-019-0167-0
    Database MEDical Literature Analysis and Retrieval System OnLINE

    More links

    Kategorien

    Order via subito

    This service is chargeable due to the Delivery terms set by subito. Orders including an article and supplementary material will be classified as separate orders. In these cases, fees will be demanded for each order.

    Inter-library loan at ZB MED

    Your chosen title can be delivered directly to ZB MED Cologne location if you are registered as a user at ZB MED Cologne.

  10. Article ; Online: Quantitative EEG reactivity and machine learning for prognostication in hypoxic-ischemic brain injury.

    Amorim, Edilberto / van der Stoel, Michelle / Nagaraj, Sunil B / Ghassemi, Mohammad M / Jing, Jin / O'Reilly, Una-May / Scirica, Benjamin M / Lee, Jong Woo / Cash, Sydney S / Westover, M Brandon

    Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology

    2019  Volume 130, Issue 10, Page(s) 1908–1916

    Abstract: Objective: Electroencephalogram (EEG) reactivity is a robust predictor of neurological recovery after cardiac arrest, however interrater-agreement among electroencephalographers is limited. We sought to evaluate the performance of machine learning ... ...

    Abstract Objective: Electroencephalogram (EEG) reactivity is a robust predictor of neurological recovery after cardiac arrest, however interrater-agreement among electroencephalographers is limited. We sought to evaluate the performance of machine learning methods using EEG reactivity data to predict good long-term outcomes in hypoxic-ischemic brain injury.
    Methods: We retrospectively reviewed clinical and EEG data of comatose cardiac arrest subjects. Electroencephalogram reactivity was tested within 72 h from cardiac arrest using sound and pain stimuli. A Quantitative EEG (QEEG) reactivity method evaluated changes in QEEG features (EEG spectra, entropy, and frequency features) during the 10 s before and after each stimulation. Good outcome was defined as Cerebral Performance Category of 1-2 at six months. Performance of a random forest classifier was compared against a penalized general linear model (GLM) and expert electroencephalographer review.
    Results: Fifty subjects were included and sixteen (32%) had good outcome. Both QEEG reactivity methods had comparable performance to expert EEG reactivity assessment for good outcome prediction (mean AUC 0.8 for random forest vs. 0.69 for GLM vs. 0.69 for expert review, respectively; p non-significant).
    Conclusions: Machine-learning models utilizing quantitative EEG reactivity data can predict long-term outcome after cardiac arrest.
    Significance: A quantitative approach to EEG reactivity assessment may support prognostication in cardiac arrest.
    MeSH term(s) Adult ; Aged ; Electroencephalography/methods ; Female ; Humans ; Hypoxia-Ischemia, Brain/diagnosis ; Hypoxia-Ischemia, Brain/physiopathology ; Machine Learning ; Male ; Middle Aged ; Prognosis ; Retrospective Studies
    Language English
    Publishing date 2019-07-25
    Publishing country Netherlands
    Document type Journal Article ; Research Support, N.I.H., Extramural ; Research Support, Non-U.S. Gov't
    ZDB-ID 1463630-x
    ISSN 1872-8952 ; 0921-884X ; 1388-2457
    ISSN (online) 1872-8952
    ISSN 0921-884X ; 1388-2457
    DOI 10.1016/j.clinph.2019.07.014
    Database MEDical Literature Analysis and Retrieval System OnLINE

    Full text online

    More links

    Kategorien

    In stock of ZB MED Cologne/Königswinter

    Ui II Zs.140: Show issues Location:
    Je nach Verfügbarkeit (siehe Angabe bei Bestand)
    bis Jg. 2021: Bestellungen von Artikeln über das Online-Bestellformular
    ab Jg. 2022: Lesesaal (EG)

    Order via subito

    This service is chargeable due to the Delivery terms set by subito. Orders including an article and supplementary material will be classified as separate orders. In these cases, fees will be demanded for each order.

To top