2018
DOI: 10.1002/jum.14916
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Cerebral Artery Vasospasm Detection Using Transcranial Doppler Signal Analysis

Abstract: Objectives Silent cerebral artery vasospasm in aneurysmal subarachnoid hemorrhage causes serious complications such as cerebral ischemia and death. A transcranial Doppler (TCD) ultrasound system is a noninvasive device that can effectively detect cerebral artery vasospasm as soon as it sets in, even before and in the absence of clinical deterioration. Continuous or even daily TCD monitoring is challenging because of the operator expertise and certification required in the form of a trained sonographer and inte… Show more

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Cited by 7 publications
(4 citation statements)
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References 39 publications
(74 reference statements)
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“…Among the six model versions, Self-ResAttentioNet18_Q1 had the highest classification accuracy at 96.05%, along with the highest recall (96.05%) and the highest specificity (96.09%). A comparative analysis of our proposed model with the existing literature [ 17 , 18 , 45 , 46 ] in the normal vs. abnormal classification using ICA or MCA waves has been done in this study to evaluate the performance of Self-AttentioNet18 against contemporary existing models. From that analysis, it can be concluded that the accuracy of the Self-AttentioNet18 in classifying healthy subjects and traumatic brain injured subjects is 6.88% greater than the existing state-of-the-art result [ 45 ].…”
Section: Discussionmentioning
confidence: 99%
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“…Among the six model versions, Self-ResAttentioNet18_Q1 had the highest classification accuracy at 96.05%, along with the highest recall (96.05%) and the highest specificity (96.09%). A comparative analysis of our proposed model with the existing literature [ 17 , 18 , 45 , 46 ] in the normal vs. abnormal classification using ICA or MCA waves has been done in this study to evaluate the performance of Self-AttentioNet18 against contemporary existing models. From that analysis, it can be concluded that the accuracy of the Self-AttentioNet18 in classifying healthy subjects and traumatic brain injured subjects is 6.88% greater than the existing state-of-the-art result [ 45 ].…”
Section: Discussionmentioning
confidence: 99%
“…A comparative analysis of our proposed model with the existing literature [ 17 , 18 , 45 , 46 ] in the normal vs. abnormal classification using ICA or MCA waves has been done in this study to evaluate the performance of Self-AttentioNet18 against contemporary existing models. From that analysis, it can be concluded that the accuracy of the Self-AttentioNet18 in classifying healthy subjects and traumatic brain injured subjects is 6.88% greater than the existing state-of-the-art result [ 45 ]. Since both of these studies focus on ultrasound signals from the MCA, the generalizability of our study is also validated.…”
Section: Discussionmentioning
confidence: 99%
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“…Though the traditional TCD Pulsatility Index constitutes an objective metric which describes waveform morphology to some degree, the information it contains is too coarse to effectively detect the presence of occluded or stenosed vessels [28,49]. Moreover, machine learning approaches to extracting information from TCD waveforms have already proven fruitful for multiple clinical applications [50][51][52][53][54][55][56], including the diagnosis of cerebrovascular stenosis [57]. In particular, recent work by our group has shown that a TCD-derived morphological biomarker termed Velocity Curvature Index (VCI) may provide a robust, objectively computable metric for detecting Large Vessel Occlusion (LVO) [54,58].…”
Section: Introductionmentioning
confidence: 99%