Patient-Specific Detection of Atrial Fibrillation in Segments of ECG Signals using Deep Neural Networks
Atrial Fibrillation (AF) is the most common cardiac arrhythmia worldwide. It is associated with reduced quality of life and increases the risk of stroke and myocardial infarction. Unfortunately, many cases of AF are asymptomatic and undiagnosed, which increases the risk for the patients. Due to its...
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Editorial Neogranadina
2019-11-01
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| Series: | Ciencia e Ingeniería Neogranadina |
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| Online Access: | https://revistasunimilitareduco.biteca.online/index.php/rcin/article/view/4156 |
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| author | Jeyson A. Castillo Yenny C. Granados Carlos Augusto Fajardo Ariza |
| author_facet | Jeyson A. Castillo Yenny C. Granados Carlos Augusto Fajardo Ariza |
| author_sort | Jeyson A. Castillo |
| collection | DOAJ |
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Atrial Fibrillation (AF) is the most common cardiac arrhythmia worldwide. It is associated with reduced quality of life and increases the risk of stroke and myocardial infarction. Unfortunately, many cases of AF are asymptomatic and undiagnosed, which increases the risk for the patients. Due to its paroxysmal nature, the detection of AF requires the evaluation, by a cardiologist, of long-term ECG signals. In Colombia, it is difficult to have access to an early diagnosis of AF because of the associated costs to the detection and the geographical distribution of cardiologists. This work is part of a macro project that aims to develop a specific-patient portable device for the detection of AF. This device will be based on a Convolutional Neural Network (CNN). We aim to find a suitable CNN model, which later could be implemented in hardware. Diverse techniques were applied to improve the answer regarding accuracy, sensitivity, specificity, and precision. The final model achieves an accuracy of , a specificity of , a sensitivity of and a precision of . During the development of the model, the computational cost and memory resources were taking into account in order to obtain an efficient hardware model in a future implementation of the device.
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| format | Article |
| id | doaj-art-3471f9f4a130494eb259a0cda919a0c3 |
| institution | Kabale University |
| issn | 0124-8170 1909-7735 |
| language | English |
| publishDate | 2019-11-01 |
| publisher | Editorial Neogranadina |
| record_format | Article |
| series | Ciencia e Ingeniería Neogranadina |
| spelling | doaj-art-3471f9f4a130494eb259a0cda919a0c32025-02-05T08:57:44ZengEditorial NeogranadinaCiencia e Ingeniería Neogranadina0124-81701909-77352019-11-01301Patient-Specific Detection of Atrial Fibrillation in Segments of ECG Signals using Deep Neural NetworksJeyson A. Castillo0https://orcid.org/0000-0003-1425-1516Yenny C. Granados1https://orcid.org/0000-0001-5418-0076Carlos Augusto Fajardo Ariza2https://orcid.org/0000-0002-8995-4585Universidad Industrial de SantanderUniversidad Industrial de SantanderUniversidad Industrial de Santander Atrial Fibrillation (AF) is the most common cardiac arrhythmia worldwide. It is associated with reduced quality of life and increases the risk of stroke and myocardial infarction. Unfortunately, many cases of AF are asymptomatic and undiagnosed, which increases the risk for the patients. Due to its paroxysmal nature, the detection of AF requires the evaluation, by a cardiologist, of long-term ECG signals. In Colombia, it is difficult to have access to an early diagnosis of AF because of the associated costs to the detection and the geographical distribution of cardiologists. This work is part of a macro project that aims to develop a specific-patient portable device for the detection of AF. This device will be based on a Convolutional Neural Network (CNN). We aim to find a suitable CNN model, which later could be implemented in hardware. Diverse techniques were applied to improve the answer regarding accuracy, sensitivity, specificity, and precision. The final model achieves an accuracy of , a specificity of , a sensitivity of and a precision of . During the development of the model, the computational cost and memory resources were taking into account in order to obtain an efficient hardware model in a future implementation of the device. https://revistasunimilitareduco.biteca.online/index.php/rcin/article/view/4156Atrial FibrillationAutomatic DetectionConvolutional Neural NetworksDeep Neural NetworksECG |
| spellingShingle | Jeyson A. Castillo Yenny C. Granados Carlos Augusto Fajardo Ariza Patient-Specific Detection of Atrial Fibrillation in Segments of ECG Signals using Deep Neural Networks Ciencia e Ingeniería Neogranadina Atrial Fibrillation Automatic Detection Convolutional Neural Networks Deep Neural Networks ECG |
| title | Patient-Specific Detection of Atrial Fibrillation in Segments of ECG Signals using Deep Neural Networks |
| title_full | Patient-Specific Detection of Atrial Fibrillation in Segments of ECG Signals using Deep Neural Networks |
| title_fullStr | Patient-Specific Detection of Atrial Fibrillation in Segments of ECG Signals using Deep Neural Networks |
| title_full_unstemmed | Patient-Specific Detection of Atrial Fibrillation in Segments of ECG Signals using Deep Neural Networks |
| title_short | Patient-Specific Detection of Atrial Fibrillation in Segments of ECG Signals using Deep Neural Networks |
| title_sort | patient specific detection of atrial fibrillation in segments of ecg signals using deep neural networks |
| topic | Atrial Fibrillation Automatic Detection Convolutional Neural Networks Deep Neural Networks ECG |
| url | https://revistasunimilitareduco.biteca.online/index.php/rcin/article/view/4156 |
| work_keys_str_mv | AT jeysonacastillo patientspecificdetectionofatrialfibrillationinsegmentsofecgsignalsusingdeepneuralnetworks AT yennycgranados patientspecificdetectionofatrialfibrillationinsegmentsofecgsignalsusingdeepneuralnetworks AT carlosaugustofajardoariza patientspecificdetectionofatrialfibrillationinsegmentsofecgsignalsusingdeepneuralnetworks |