HD-MVCNN: High-density ECG signal based diabetic prediction and classification using multi-view convolutional neural network
Diabetes mellitus, also known as diabetes, is a medical condition marked by high blood sugar levels and impacts a large population worldwide. Treating diabetes is not feasible. It can be managed. Hence, it is crucial to promptly identify a diagnosis of diabetes. This study explores the effects of di...
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Elsevier
2024-12-01
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| Series: | Egyptian Informatics Journal |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S1110866524001361 |
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| author | D. Santhakumar K. Dhana Shree M. Buvanesvari A. Saran Kumar Ayodeji Olalekan Salau |
| author_facet | D. Santhakumar K. Dhana Shree M. Buvanesvari A. Saran Kumar Ayodeji Olalekan Salau |
| author_sort | D. Santhakumar |
| collection | DOAJ |
| description | Diabetes mellitus, also known as diabetes, is a medical condition marked by high blood sugar levels and impacts a large population worldwide. Treating diabetes is not feasible. It can be managed. Hence, it is crucial to promptly identify a diagnosis of diabetes. This study explores the effects of diabetes on the heart, focusing on heart rate variability (HRV) signals, which can offer valuable information about the existence and seriousness of diabetes through the evaluation of diabetes-related heart problems. Extracting crucial data from the irregular and nonlinear HRV signal can be quite challenging. Studying cardiac diagnostics involves a thorough analysis of electrocardiogram (ECG) signals. Traditional electrocardiogram recordings utilize twelve channels, each capturing a complex combination of activities originating from different regions of the heart. Examining ECG signals recorded on the body’s surface may not be an effective method for studying and diagnosing diabetic issues. The study introduces a research proposal utilizing a high-density resolution electrocardiogram (ECG) system with a minimum of 64 channels and multi-view convolutional neural network classification (HD-MVCNN) to address the mentioned challenges. This framework may help identify the hypoglycaemia effects on brain regions, leading to decreased complexity and increased theta and delta power during scalp electrocardiogram procedures. The convolutional architectural model primarily contributes to enhancement and optimization through its Stochastic Gradient Descent (SGD) along with convolutional layers and according to results, the HD-MVCNN demonstrated better stability and accuracy in comparison to traditional classification models. Thus, HD-MVCNN shows promise as a powerful method for classifying features in diabetes clinical data. |
| format | Article |
| id | doaj-art-84c9ffe91d1b40f18cb9bc734ee251ab |
| institution | OA Journals |
| issn | 1110-8665 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Egyptian Informatics Journal |
| spelling | doaj-art-84c9ffe91d1b40f18cb9bc734ee251ab2025-08-20T02:35:39ZengElsevierEgyptian Informatics Journal1110-86652024-12-012810057310.1016/j.eij.2024.100573HD-MVCNN: High-density ECG signal based diabetic prediction and classification using multi-view convolutional neural networkD. Santhakumar0K. Dhana Shree1M. Buvanesvari2A. Saran Kumar3Ayodeji Olalekan Salau4Department of Computer Science and Engineering, Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences, Thandalam, Chennai, IndiaDepartment of Computer Science and Engineering, Sri Ramakrishna Engineering College, Coimbatore, IndiaDepartment of Computer Science and Engineering, Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences, Thandalam, Chennai, IndiaDepartment of Artificial Intelligence and Data Science, Coimbatore Institute of Technology, Coimbatore, India; Corresponding authors at: Department of Artificial Intelligence and Data Science, Coimbatore Institute of Technology, Coimbatore, India-641014 (A. Saran Kumar). Department of Electrical/Electronics and Computer Engineering, Afe Babalola University, Ado-Ekiti, Nigeria (Ayodeji Olalekan Salau).Department of Electrical/Electronics and Computer Engineering, Afe Babalola University, Ado-Ekiti, Nigeria; Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences, Chennai, India; Corresponding authors at: Department of Artificial Intelligence and Data Science, Coimbatore Institute of Technology, Coimbatore, India-641014 (A. Saran Kumar). Department of Electrical/Electronics and Computer Engineering, Afe Babalola University, Ado-Ekiti, Nigeria (Ayodeji Olalekan Salau).Diabetes mellitus, also known as diabetes, is a medical condition marked by high blood sugar levels and impacts a large population worldwide. Treating diabetes is not feasible. It can be managed. Hence, it is crucial to promptly identify a diagnosis of diabetes. This study explores the effects of diabetes on the heart, focusing on heart rate variability (HRV) signals, which can offer valuable information about the existence and seriousness of diabetes through the evaluation of diabetes-related heart problems. Extracting crucial data from the irregular and nonlinear HRV signal can be quite challenging. Studying cardiac diagnostics involves a thorough analysis of electrocardiogram (ECG) signals. Traditional electrocardiogram recordings utilize twelve channels, each capturing a complex combination of activities originating from different regions of the heart. Examining ECG signals recorded on the body’s surface may not be an effective method for studying and diagnosing diabetic issues. The study introduces a research proposal utilizing a high-density resolution electrocardiogram (ECG) system with a minimum of 64 channels and multi-view convolutional neural network classification (HD-MVCNN) to address the mentioned challenges. This framework may help identify the hypoglycaemia effects on brain regions, leading to decreased complexity and increased theta and delta power during scalp electrocardiogram procedures. The convolutional architectural model primarily contributes to enhancement and optimization through its Stochastic Gradient Descent (SGD) along with convolutional layers and according to results, the HD-MVCNN demonstrated better stability and accuracy in comparison to traditional classification models. Thus, HD-MVCNN shows promise as a powerful method for classifying features in diabetes clinical data.http://www.sciencedirect.com/science/article/pii/S1110866524001361Diabetic detectionHypoglycemiaECGCNNClassification |
| spellingShingle | D. Santhakumar K. Dhana Shree M. Buvanesvari A. Saran Kumar Ayodeji Olalekan Salau HD-MVCNN: High-density ECG signal based diabetic prediction and classification using multi-view convolutional neural network Egyptian Informatics Journal Diabetic detection Hypoglycemia ECG CNN Classification |
| title | HD-MVCNN: High-density ECG signal based diabetic prediction and classification using multi-view convolutional neural network |
| title_full | HD-MVCNN: High-density ECG signal based diabetic prediction and classification using multi-view convolutional neural network |
| title_fullStr | HD-MVCNN: High-density ECG signal based diabetic prediction and classification using multi-view convolutional neural network |
| title_full_unstemmed | HD-MVCNN: High-density ECG signal based diabetic prediction and classification using multi-view convolutional neural network |
| title_short | HD-MVCNN: High-density ECG signal based diabetic prediction and classification using multi-view convolutional neural network |
| title_sort | hd mvcnn high density ecg signal based diabetic prediction and classification using multi view convolutional neural network |
| topic | Diabetic detection Hypoglycemia ECG CNN Classification |
| url | http://www.sciencedirect.com/science/article/pii/S1110866524001361 |
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