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Algorithm for Reliable Detection of Pulse Onsets in Cerebral Blood Flow Velocity Signals

Transcranial Doppler (TCD) ultrasound has been demonstrated to be a valuable tool for assessing cerebral hemodynamics via measurement of cerebral blood flow velocity (CBFV), with a number of established clinical indications. However, CBFV waveform analysis depends on reliable pulse onset detection,...

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Autores principales: Canac, Nicolas, Ranjbaran, Mina, O'Brien, Michael J., Asgari, Shadnaz, Scalzo, Fabien, Thorpe, Samuel G., Jalaleddini, Kian, Thibeault, Corey M., Wilk, Seth J., Hamilton, Robert B.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6798080/
https://www.ncbi.nlm.nih.gov/pubmed/31681147
http://dx.doi.org/10.3389/fneur.2019.01072
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author Canac, Nicolas
Ranjbaran, Mina
O'Brien, Michael J.
Asgari, Shadnaz
Scalzo, Fabien
Thorpe, Samuel G.
Jalaleddini, Kian
Thibeault, Corey M.
Wilk, Seth J.
Hamilton, Robert B.
author_facet Canac, Nicolas
Ranjbaran, Mina
O'Brien, Michael J.
Asgari, Shadnaz
Scalzo, Fabien
Thorpe, Samuel G.
Jalaleddini, Kian
Thibeault, Corey M.
Wilk, Seth J.
Hamilton, Robert B.
author_sort Canac, Nicolas
collection PubMed
description Transcranial Doppler (TCD) ultrasound has been demonstrated to be a valuable tool for assessing cerebral hemodynamics via measurement of cerebral blood flow velocity (CBFV), with a number of established clinical indications. However, CBFV waveform analysis depends on reliable pulse onset detection, an inherently difficult task for CBFV signals acquired via TCD. We study the application of a new algorithm for CBFV pulse segmentation, which locates pulse onsets in a sequential manner using a moving difference filter and adaptive thresholding. The test data set used in this study consists of 92,012 annotated CBFV pulses, whose quality is representative of real world data. On this test set, the algorithm achieves a true positive rate of 99.998% (2 false negatives), positive predictive value of 99.998% (2 false positives), and mean temporal offset error of 6.10 ± 4.75 ms. We do note that in this context, the way in which true positives, false positives, and false negatives are defined caries some nuance, so care should be taken when drawing comparisons to other algorithms. Additionally, we find that 97.8% and 99.5% of onsets are detected within 10 and 30 ms, respectively, of the true onsets. The algorithm's performance in spite of the large degree of variation in signal quality and waveform morphology present in the test data suggests that it may serve as a valuable tool for the accurate and reliable identification of CBFV pulse onsets in neurocritical care settings.
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spelling pubmed-67980802019-11-01 Algorithm for Reliable Detection of Pulse Onsets in Cerebral Blood Flow Velocity Signals Canac, Nicolas Ranjbaran, Mina O'Brien, Michael J. Asgari, Shadnaz Scalzo, Fabien Thorpe, Samuel G. Jalaleddini, Kian Thibeault, Corey M. Wilk, Seth J. Hamilton, Robert B. Front Neurol Neurology Transcranial Doppler (TCD) ultrasound has been demonstrated to be a valuable tool for assessing cerebral hemodynamics via measurement of cerebral blood flow velocity (CBFV), with a number of established clinical indications. However, CBFV waveform analysis depends on reliable pulse onset detection, an inherently difficult task for CBFV signals acquired via TCD. We study the application of a new algorithm for CBFV pulse segmentation, which locates pulse onsets in a sequential manner using a moving difference filter and adaptive thresholding. The test data set used in this study consists of 92,012 annotated CBFV pulses, whose quality is representative of real world data. On this test set, the algorithm achieves a true positive rate of 99.998% (2 false negatives), positive predictive value of 99.998% (2 false positives), and mean temporal offset error of 6.10 ± 4.75 ms. We do note that in this context, the way in which true positives, false positives, and false negatives are defined caries some nuance, so care should be taken when drawing comparisons to other algorithms. Additionally, we find that 97.8% and 99.5% of onsets are detected within 10 and 30 ms, respectively, of the true onsets. The algorithm's performance in spite of the large degree of variation in signal quality and waveform morphology present in the test data suggests that it may serve as a valuable tool for the accurate and reliable identification of CBFV pulse onsets in neurocritical care settings. Frontiers Media S.A. 2019-10-11 /pmc/articles/PMC6798080/ /pubmed/31681147 http://dx.doi.org/10.3389/fneur.2019.01072 Text en Copyright © 2019 Canac, Ranjbaran, O'Brien, Asgari, Scalzo, Thorpe, Jalaleddini, Thibeault, Wilk and Hamilton. http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Neurology
Canac, Nicolas
Ranjbaran, Mina
O'Brien, Michael J.
Asgari, Shadnaz
Scalzo, Fabien
Thorpe, Samuel G.
Jalaleddini, Kian
Thibeault, Corey M.
Wilk, Seth J.
Hamilton, Robert B.
Algorithm for Reliable Detection of Pulse Onsets in Cerebral Blood Flow Velocity Signals
title Algorithm for Reliable Detection of Pulse Onsets in Cerebral Blood Flow Velocity Signals
title_full Algorithm for Reliable Detection of Pulse Onsets in Cerebral Blood Flow Velocity Signals
title_fullStr Algorithm for Reliable Detection of Pulse Onsets in Cerebral Blood Flow Velocity Signals
title_full_unstemmed Algorithm for Reliable Detection of Pulse Onsets in Cerebral Blood Flow Velocity Signals
title_short Algorithm for Reliable Detection of Pulse Onsets in Cerebral Blood Flow Velocity Signals
title_sort algorithm for reliable detection of pulse onsets in cerebral blood flow velocity signals
topic Neurology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6798080/
https://www.ncbi.nlm.nih.gov/pubmed/31681147
http://dx.doi.org/10.3389/fneur.2019.01072
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