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Your search keyword '"Alqahtani, Abdulrahman"' showing total 543 results
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543 results on '"Alqahtani, Abdulrahman"'

5. A Novel 1D Generative Adversarial Network-based Framework for Atrial Fibrillation Detection using Restored Wrist Photoplethysmography Signals

Author

Sayem, Faizul Rakib, Ahmed, Mosabber Uddin, Alam, Saadia Binte, Mahmud, Sakib, Sheikh, Md. Mamun, Alqahtani, Abdulrahman, Faisal, Md Ahasan Atick, and Chowdhury, Muhammad E. H.

Subjects
Electrical Engineering and Systems Science - Signal Processing
Abstract

Atrial fibrillation (AF) increases the risk of stroke. Electrocardiogram (ECG) is used for AF detection, while photoplethysmography (PPG) is simple to use and appropriate for long-term monitoring. We have developed a novel approach to detect AF from smartwatch-based wrist PPG signals. To the best of our knowledge, this is the first study to employ 1D CycleGAN for the reconstruction of 1D wrist PPG signals, since the quality of wrist PPG signals is very poor due to motion artifacts and acquisition site limitations. Our proposed approach is validated on a dataset of 21,278 10s long wrist PPG segments. The dataset was divided into two halves and two experiments were conducted. One half was used for training and the other half for testing and vice versa. Our classification model (Self-AFNet) with 1D-CycleGAN for restoration achieves an accuracy of 96.41% and 97.09% for both splits, respectively. There is a noteworthy improvement in accuracy (2.94% and 5.08% for test splits, respectively) for restored signals as compared to unrestored PPG. AF detection task was also performed using ECG signals with the matched PPG signals to assess the validity of employing reconstructed PPG-based AF classification. Self-AFNet managed to achieve an accuracy of 98.07% and 98.97%, respectively using two ECG splits which is comparable to the performance of AF detection utilizing reconstructed PPG segments. This study reflects that reconstructed wrist PPG signals acquired from wearable devices can be used for reliable detection of atrial fibrillation, which can significantly help in the reduction of the risk of stroke.

Published
2023

6. Deep learning in computed tomography pulmonary angiography imaging: a dual-pronged approach for pulmonary embolism detection

Author

Bushra, Fabiha, Chowdhury, Muhammad E. H., Sarmun, Rusab, Kabir, Saidul, Said, Menatalla, Zoghoul, Sohaib Bassam, Mushtak, Adam, Al-Hashimi, Israa, Alqahtani, Abdulrahman, and Hasan, Anwarul

Subjects
Computer Science - Computer Vision and Pattern Recognition, Computer Science - Artificial Intelligence
Abstract

The increasing reliance on Computed Tomography Pulmonary Angiography (CTPA) for Pulmonary Embolism (PE) diagnosis presents challenges and a pressing need for improved diagnostic solutions. The primary objective of this study is to leverage deep learning techniques to enhance the Computer Assisted Diagnosis (CAD) of PE. With this aim, we propose a classifier-guided detection approach that effectively leverages the classifier's probabilistic inference to direct the detection predictions, marking a novel contribution in the domain of automated PE diagnosis. Our classification system includes an Attention-Guided Convolutional Neural Network (AG-CNN) that uses local context by employing an attention mechanism. This approach emulates a human expert's attention by looking at both global appearances and local lesion regions before making a decision. The classifier demonstrates robust performance on the FUMPE dataset, achieving an AUROC of 0.927, sensitivity of 0.862, specificity of 0.879, and an F1-score of 0.805 with the Inception-v3 backbone architecture. Moreover, AG-CNN outperforms the baseline DenseNet-121 model, achieving an 8.1% AUROC gain. While previous research has mostly focused on finding PE in the main arteries, our use of cutting-edge object detection models and ensembling techniques greatly improves the accuracy of detecting small embolisms in the peripheral arteries. Finally, our proposed classifier-guided detection approach further refines the detection metrics, contributing new state-of-the-art to the community: mAP$_{50}$, sensitivity, and F1-score of 0.846, 0.901, and 0.779, respectively, outperforming the former benchmark with a significant 3.7% improvement in mAP$_{50}$. Our research aims to elevate PE patient care by integrating AI solutions into clinical workflows, highlighting the potential of human-AI collaboration in medical diagnostics., Comment: Published in Expert Systems With Applications

Published
2023
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13. Modeling the Air Conditioner Performance Tests Using Artificial Neural Network Simulator (ANNS-AC)

Author

AlMutiri, Yousef M., AlRashidi, Mohammed S., AlQahtani, AbdulRahman M., Alqahtani, Turki S., Sadek, A. M., Rannenberg, Kai, Editor-in-Chief, Soares Barbosa, Luís, Editorial Board Member, Carette, Jacques, Editorial Board Member, Tatnall, Arthur, Editorial Board Member, Neuhold, Erich J., Editorial Board Member, Stiller, Burkhard, Editorial Board Member, Stettner, Lukasz, Editorial Board Member, Pries-Heje, Jan, Editorial Board Member, Kreps, David, Editorial Board Member, Rettberg, Achim, Editorial Board Member, Furnell, Steven, Editorial Board Member, Mercier-Laurent, Eunika, Editorial Board Member, Winckler, Marco, Editorial Board Member, Malaka, Rainer, Editorial Board Member, Maglogiannis, Ilias, editor, Iliadis, Lazaros, editor, Macintyre, John, editor, Avlonitis, Markos, editor, and Papaleonidas, Antonios, editor

Published
2024
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17. Percutaneous coronary intervention vs. coronary artery bypass grafting in emergency and non-emergency unprotected left-main revascularization

Author

Daoulah, Amin, Alqahtani, Abdulrahman H., Elmahrouk, Ahmed, Yousif, Nooraldaem, Almahmeed, Wael, Arafat, Amr A., Al Garni, Turki, Qutub, Mohammed A., Dahdouh, Ziad, Alshehri, Mohammed, Hersi, Ahmad S., Malak, Majed M., Djunaedi, Syifa R., Zaidi, Ayesha, Naser, Maryam Jameel, Qenawi, Wael, Elganady, Abdelmaksoud, Hassan, Taher, Ball, Vincent, Elmahrouk, Youssef, Hussien, Adnan Fathey, Alzahrani, Badr, Abuelatta, Reda, Selim, Ehab, Jamjoom, Ahmed, Alshali, Khalid Z., Hashmani, Shahrukh, Refaat, Wael, Kazim, Hameedullah M., Ghani, Mohamed Ajaz, Amin, Haitham, Ibrahim, Ahmed M., Abohasan, Abdulwali, Alama, Mohamed N., Balghith, Mohammed, Abdulhabeeb, Ibrahim A. M., Ahmad, Osama, Ramadan, Mohamed, Ghonim, Ahmed A., Shawky, Abeer M., Noor, Husam A., Alqahtani, Abdulrahman M., Al Samadi, Faisal, Abualnaja, Seraj, Baqais, Rasha Taha, Alhassoun, Abdulkarim, Altnji, Issam, Khan, Mushira, Alasmari, Abdulaziz, Aljohar, Alwaleed, Hiremath, Niranjan, Aithal, Jairam, and Lotfi, Amir

Published
2023
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25. Curcumin-loaded soluplus® based ternary solid dispersions with enhanced solubility, dissolution and antibacterial, antioxidant, anti-inflammatory activities

Author

Ishtiaq, Memoona, Manzoor, Hina, Khan, Ikram Ullah, Asghar, Sajid, Irfan, Muhammad, Albekairi, Norah A., Alshammari, Abdulrahman, Alqahtani, Abdulrahman F., Alotaibi, Saad, Munir, Rabia, Shah, Pervaiz A., Hussain, Liaqat, Saleem, Muhammad Abubakar, Razzaq, Fizza Abdul, and Khalid, Syed Haroon

Published
2024
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40. Utilization of Shock Team and Veno-Arterial Extracorporeal Membrane Oxygenation (VA-ECMO) in the Management of Cardiogenic Shock in Northern Ontario

Author

Alnasser, Sami, Cavanagh, Matthew, Atoui, Rony, Ali, Noman, Nalla, Bhanu, McKechnie, Kyle, Main, Anthony, Rheault-Henry, Mathieu, Al-Shaibi, AbdulAziz, Burke, Lucas, McIsaac, Sarah, Anderson, Robert, Fam, Neil, Shurrab, Mohammed, Kerr, Mary Catherine, Hennessey, Hooman, Armstrong, Craig, Bittira, Bindu, Alqahtani, Abdulrahman, Papadopoulos, Gregory, Kumar, Andreas, MacDonald, Derek, O’Connor, Cormac, McDonald, Michael, and Manchuk, Derek

Published
2024
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46. Cardiovascular diseases diagnosis by impedance cardiography

Author

Mansouri Sofiene, Alharbi Yousef, Alshrouf Anwar, and Alqahtani Abdulrahman

Subjects
cdvs, icg, diagnosis, Medicine (General), R5-920
Abstract

Cardiovascular disease (CVD) represents the leading cause of mortality worldwide. In order to diagnose CVDs, there are a range of detection methods, among them, the impedance cardiography technique (ICG). It is a non-invasive and low-cost method. In this paper, we highlight recent advances and developments of the CDVs diagnosis mainly by the ICG method. We considered papers published during the last five years (from 2017 until 2022). Based on this study, we expressed the need for an ICG database for the different CDVs.

Published
2022
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