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  1. Article ; Online: Intermediate-Risk and High-Risk Pulmonary Embolism: Recognition and Management: Cardiology Clinics: Cardiac Emergencies.

    Birrenkott, Drew A / Kabrhel, Christopher / Dudzinski, David M

    Cardiology clinics

    2024  Volume 42, Issue 2, Page(s) 215–235

    Abstract: Pulmonary embolism (PE) is the third most common cause of cardiovascular death. Every specialty of medical practitioner will encounter PE in their patients, and should be prepared to employ contemporary strategies for diagnosis and initial risk- ... ...

    Abstract Pulmonary embolism (PE) is the third most common cause of cardiovascular death. Every specialty of medical practitioner will encounter PE in their patients, and should be prepared to employ contemporary strategies for diagnosis and initial risk-stratification. Treatment of PE is based on risk-stratification, with anticoagulation for all patients, and advanced modalities including systemic thrombolysis, catheter-directed therapies, and mechanical circulatory supports utilized in a manner paralleling PE severity and clinical context.
    MeSH term(s) Humans ; Thrombolytic Therapy ; Emergencies ; Pulmonary Embolism/diagnosis ; Heart ; Cardiology ; Treatment Outcome
    Language English
    Publishing date 2024-04-16
    Publishing country Netherlands
    Document type Journal Article ; Review
    ZDB-ID 1196385-2
    ISSN 1558-2264 ; 0733-8651
    ISSN (online) 1558-2264
    ISSN 0733-8651
    DOI 10.1016/j.ccl.2024.02.008
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  2. Article ; Online: Robust estimation of respiratory rate via ECG- and PPG-derived respiratory quality indices.

    Birrenkott, Drew A / Pimentel, Marco A F / Watkinson, Peter J / Clifton, David A

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

    2017  Volume 2016, Page(s) 676–679

    Abstract: Respiratory rate (RR) is one of the most informative indicators of a patient's health status. However, automated, non-invasive measurements of RR are insufficiently robust for use in clinical practice. A number of methods have been described in the ... ...

    Abstract Respiratory rate (RR) is one of the most informative indicators of a patient's health status. However, automated, non-invasive measurements of RR are insufficiently robust for use in clinical practice. A number of methods have been described in the literature to estimate RR from both photo-plethysmography (PPG) and electrocardiography (ECG) based on three physiological modulations of respiration: amplitude modulation (AM), frequency modulation (FM), and baseline wander (BW). However, the quality of the respiratory information acquired is highly patient-dependent and often too noisy to be used. We address this by proposing respiratory quality indices (RQIs) that quantify the quality of the respiratory signal that can be extracted from each modulation from both PPG and ECG waveforms. Signal quality indices (SQIs) detect artefact in the ECG and PPG, which is relatively straight-forward. RQIs have a different role: they quantify if an individual patient's physiology is modulating the sensor waveforms. We have designed four RQIs based on Fourier transform (RQIFFT), autocorrelation (RQIAC), autoregression (RQIAR), and Hjorth complexity (RQIHC). We validated the approach using PPG and ECG data in the CapnoBase and MIMIC II datasets. We conclude that the novel implementation of an RQI-based preprocessing step has the potential to improve substantially the performance of RR estimation algorithms.
    MeSH term(s) Adolescent ; Adult ; Aged ; Algorithms ; Child ; Child, Preschool ; Electrocardiography ; Fourier Analysis ; Humans ; Infant ; Intensive Care Units ; Middle Aged ; Plethysmography ; Respiratory Rate/physiology ; Signal Processing, Computer-Assisted ; Young Adult
    Language English
    Publishing date 2017-03-08
    Publishing country United States
    Document type Journal Article
    ISSN 2694-0604
    ISSN (online) 2694-0604
    DOI 10.1109/EMBC.2016.7590792
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  3. Article ; Online: A Robust Fusion Model for Estimating Respiratory Rate From Photoplethysmography and Electrocardiography.

    Birrenkott, Drew A / Pimentel, Marco A F / Watkinson, Peter J / Clifton, David A

    IEEE transactions on bio-medical engineering

    2017  Volume 65, Issue 9, Page(s) 2033–2041

    Abstract: Objective: Respiratory rate (RR) estimation algorithms based on the photoplethymogram (PPG) and electrocardiogram (ECG) lack clinical robustness. This is because the PPG and ECG respiratory modulations are dependent on patient physiology, regardless of ... ...

    Abstract Objective: Respiratory rate (RR) estimation algorithms based on the photoplethymogram (PPG) and electrocardiogram (ECG) lack clinical robustness. This is because the PPG and ECG respiratory modulations are dependent on patient physiology, regardless of general signal quality. The present work describes an RR estimation algorithm using respiratory quality indices (RQIs) that assess the presence or absence of the PPG- and ECG-derived respiratory modulations.
    Methods: Six respiratory waveforms are derived from the amplitude modulation, frequency modulation, and baseline wander of the PPG and ECG. The respiratory quality of each modulation is assessed by using RQIs based on the fast Fourier transform, autoregression, and autocorrelation. The individual RQIs are fused to obtain a single RQI per modulation per time window. Based on a tunable threshold, the RQIs are used to discard poor modulations and weight the remaining modulations to provide a single RR estimation per time window.
    Results: The proposed method was tested on two independent datasets and found that using a conservative threshold, the mean absolute error was 0.71 $\pm$ 0.89 and 3.12 $\pm$ 4.39 brpm while discarding only 1.3% and 23.2% of all time windows, for each dataset, respectively.
    Conclusion: These errors are either better than or comparable to current methods, and the number of windows discarded is far lower demonstrating improved robustness.
    Significance: This work describes a novel preprocessing algorithm that can be implemented in conjunction with other RR estimation techniques to improve robustness by specifically considering the quality of the respiratory information.
    MeSH term(s) Algorithms ; Electrocardiography/methods ; Humans ; Photoplethysmography/methods ; Respiratory Rate/physiology ; Signal Processing, Computer-Assisted
    Language English
    Publishing date 2017-12-19
    Publishing country United States
    Document type Journal Article ; Research Support, Non-U.S. Gov't
    ZDB-ID 160429-6
    ISSN 1558-2531 ; 0018-9294
    ISSN (online) 1558-2531
    ISSN 0018-9294
    DOI 10.1109/TBME.2017.2778265
    Database MEDical Literature Analysis and Retrieval System OnLINE

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    In stock of ZB MED Cologne/Königswinter

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    bis Jg. 1994: Bestellungen von Artikeln über das Online-Bestellformular
    Jg. 1995 - 2021: Lesesall (1.OG)
    ab Jg. 2022: Lesesaal (EG)

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  4. Article ; Online: Breathing Rate Estimation From the Electrocardiogram and Photoplethysmogram: A Review.

    Charlton, Peter H / Birrenkott, Drew A / Bonnici, Timothy / Pimentel, Marco A F / Johnson, Alistair E W / Alastruey, Jordi / Tarassenko, Lionel / Watkinson, Peter J / Beale, Richard / Clifton, David A

    IEEE reviews in biomedical engineering

    2017  Volume 11, Page(s) 2–20

    Abstract: Breathing rate (BR) is a key physiological parameter used in a range of clinical settings. Despite its diagnostic and prognostic value, it is still widely measured by counting breaths manually. A plethora of algorithms have been proposed to estimate BR ... ...

    Abstract Breathing rate (BR) is a key physiological parameter used in a range of clinical settings. Despite its diagnostic and prognostic value, it is still widely measured by counting breaths manually. A plethora of algorithms have been proposed to estimate BR from the electrocardiogram (ECG) and pulse oximetry (photoplethysmogram, PPG) signals. These BR algorithms provide opportunity for automated, electronic, and unobtrusive measurement of BR in both healthcare and fitness monitoring. This paper presents a review of the literature on BR estimation from the ECG and PPG. First, the structure of BR algorithms and the mathematical techniques used at each stage are described. Second, the experimental methodologies that have been used to assess the performance of BR algorithms are reviewed, and a methodological framework for the assessment of BR algorithms is presented. Third, we outline the most pressing directions for future research, including the steps required to use BR algorithms in wearable sensors, remote video monitoring, and clinical practice.
    MeSH term(s) Algorithms ; Electrocardiography ; Humans ; Photoplethysmography ; Respiratory Rate/physiology ; Signal Processing, Computer-Assisted
    Language English
    Publishing date 2017-10-24
    Publishing country United States
    Document type Journal Article ; Research Support, Non-U.S. Gov't ; Review
    ISSN 1941-1189
    ISSN (online) 1941-1189
    DOI 10.1109/RBME.2017.2763681
    Database MEDical Literature Analysis and Retrieval System OnLINE

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