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Deep Learning-Based ECG Classification for Arterial Fibrillation Detection

Computers, Materials & Continua 2024年第6期79卷 4805-4824页

作者： Muhammad Sohail Irshad Tehreem Masood Arfan Jaffar Muhammad Rashid Sheeraz Akram Abeer Aljohani Faculty of Computer Science&Information Technology The Superior UniversityLahore54000Pakistan Intelligent Data Visual Computing Research(IDVCR) Lahore54000Pakistan Department of Computer Science National University of TechnologyIslamabad45000Pakistan Information Systems Department College of Computer and Information SciencesImam Mohammad Ibn Saud Islamic University(IMSIU)Riyadh11432Saudi Arabia Department of Computer Science Applied CollegeTaibah UniversityMedina42353Saudi Arabia

The application of deep learning techniques in the medical field,specifically for Atrial Fibrillation(AFib)detection through Electrocardiogram(ECG)signals,has witnessed significant *** and timely diagnosis increases the patient’s chances of ***,issues like overfitting and inconsistent accuracy across datasets remain *** a quest to address these challenges,a study presents two prominent deep learning architectures,ResNet-50 and DenseNet-121,to evaluate their effectiveness in AFib *** aim was to create a robust detection mechanism that consistently performs *** such as loss,accuracy,precision,sensitivity,and area under the curve(AUC)were utilized for *** findings revealed that ResNet-50 surpassed DenseNet-121 in all evaluated *** demonstrated lower loss rate 0.0315 and 0.0305 superior accuracy of 98.77%and 98.88%,precision of 98.78%and 98.89%and sensitivity of 98.76%and 98.86%for training and validation,hinting at its advanced capability for AFib *** insights offer a substantial contribution to the existing literature on deep learning applications for AFib detection from ECG *** comparative performance data assists future researchers in selecting suitable deep-learning architectures for AFib ***,the outcomes of this study are anticipated to stimulate the development of more advanced and efficient ECG-based AFib detection methodologies,for more accurate and early detection of AFib,thereby fostering improved patient care and outcomes.

关键词： Convolution neural network atrial fibrillation area under curve ECG false positive rate deep learning classification

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Reduced hemoglobin and increased C-reactive protein are associated with upper gastrointestinal bleeding

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World Journal of Gastroenterology 2014年第5期20卷 1311-1317页

作者： Minoru Tomizawa Fuminobu Shinozaki Rumiko Hasegawa Akira Togawa Yoshinori Shirai Noboru Ichiki Yasufumi Motoyoshi Takao Sugiyama Shigenori Yamamoto Makoto Sueishi Department of Gastroenterology National Hospital Organization Shimoshizu Hospital Yotsukaido City Department of Radiology National Hospital Organization Shimoshizu Hospital Yotsukaido City Department of Surgery National Hospital Organization Shimoshizu Hospital Yotsukaido City Department of Neurology National Hos- pital Organization Shimoshizu Hospital Yotsukaido City Department of Rheumatology National Hospital Organization Shimoshizu Hospital Yotsukaido City Department of Pediatrics National Hospital Organization Shimoshizu Hospital Yotsukaido City

AIM: To investigate the early upper gastrointestinal endoscopy (endoscopy) significantly reduces mortality resulting from upper gastrointestinal (GI) bleeding.

关键词： Receiver operating characteristic area under curve Sensitivity Specificity Gastrointestinal bleeding

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