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AuthorKanchon, Kanti Podder
AuthorChowdhury, Muhammad E.H.
AuthorAl-Maadeed, Somaya
AuthorNasrin Nisha, Naima
AuthorMahmud, Sakib
AuthorHamadelneil, Fatema
AuthorAlmkhlef, Taif
AuthorAljofairi, Hind
AuthorMushtak, Adam
AuthorKhandakar, Amith
AuthorZughaier, Susu
Available date2023-05-02T08:05:39Z
Publication Date2023-03-29
Publication NameBiomedical Signal Processing and Control
Identifierhttp://dx.doi.org/10.1016/j.bspc.2023.104882
CitationPodder, K. K., Chowdhury, M. E., Al-Maadeed, S., Nisha, N. N., Mahmud, S., Hamadelneil, F., ... & Zughaier, S. (2023). Deep learning-based middle cerebral artery blood flow abnormality detection using flow velocity waveform derived from transcranial Doppler ultrasound. Biomedical Signal Processing and Control, 85, 104882.
ISSN1746-8094
URIhttps://www.sciencedirect.com/science/article/pii/S1746809423003154
URIhttp://hdl.handle.net/10576/42192
AbstractSince the brain is unlike any other organ in that it cannot store energy and has a high metabolic demand, constant blood flow is essential for healthy brain function. The maximum flow velocity waveform that is produced by transcranial doppler echo ultrasonography has different qualities for a healthy subject and a critically ill patient with conditions such as intraparenchymal or subarachnoid hemorrhage, hydrocephalus, or traumatic brain injury. Depending on the degree of the injury, the symptoms of traumatic brain damage can present themselves right away or not until days or weeks later. To aid in the early and accurate detection of patients with severe brain conditions, a classification system is proposedthat can distinguish between healthy control and patient utilizing the maximum flow velocity waveform derived from Transcranial doppler ultrasound. In this research, we manually labelled the data to remove mediocre and corrupted signals and pre-processed low-quality signals into high-quality ones using a Cycle Generative Adversarial Network (CycleGAN). This study proposes a two-stream deep learning model, DopplerNet2+, based on a Self-organized Operational Neural Network (Self-ONN), which achieves an overall accuracy, precision, recall, sensitivity, f1 score, and specificity of 99.45%, 99.45%, 99.45%, and 99.37% for the classification issue. DopplerNet2+ has a better area under the curve (AUC) of 1.00 and a better Kolmogorov-Smirnov (KS) statistic of 0.996 at the 0.812 thresholds than 11 other Self-ONN models trained with different inputs. The results show that the proposed models can successfully carry out the targeted classification task.
SponsorHigh Impact grant of Qatar University # QUHI-CENG-22_23-548.
Languageen
PublisherElsevier
SubjectMiddle cerebral artery
Transcranial Doppler ultrasound
CycleGAN
Deep learning
TitleDeep learning-based middle cerebral artery blood flow abnormality detection using flow velocity waveform derived from transcranial Doppler ultrasound
TypeArticle
Volume Number85
ESSN1746-8108
dc.accessType Full Text


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